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5049Cambridge Publications, Inc. | All Rights Reserved.CxOTalkhttps://www.w3schools.com/xml/xml_rss.asp1440Sat, 15 Feb 2025 11:52:30 -0500Sat, 15 Feb 2025 11:52:30 -0500AI Snake Oil Exposed: Princeton Researcher Busts AI Hype
https://www.cxotalk.com/episode/ai-snake-oil-exposed-princeton-researcher-busts-ai-hype
<p>Join Princeton Professor Arvind Narayanan, co-author of AI Snake Oil, for a provocative conversation. We will cut through the AI hype cycle to examine what artificial intelligence can and cannot deliver for business.</p><p>While generative AI represents genuine technological progress, many AI applications - particularly in predictive analytics - are fundamentally flawed. Professor Narayanan reveals why certain AI use cases amount to "snake oil" and how institutional pressures drive organizations to adopt AI solutions that cannot deliver on their promises.</p><p>In this episode, we'll explore:</p>Why predictive AI can fail in high-stakes domains like hiring and risk assessmentHow flawed incentives and institutional dynamics create markets for ineffective AIStrategic frameworks for evaluating AI capabilities and limitationsPractical guidance for responsible AI adoption that delivers real business value<p>This is not a basic AI primer. Instead, it's an essential strategic discussion for senior executives who must separate genuine AI innovation from expensive technological dead ends.</p><p>Join us for this candid examination of AI's true capabilities and limitations with one of technology's most incisive critics.</p>Episode HighlightsEvaluate AI Claims Against Real-World PerformanceBy testing AI solutions in your context, distinguish between marketing hype and actual capabilities. Real-world experimentation for a few hours often provides better insight than vendor claims or academic papers.Focus on concrete, measurable outcomes rather than buzzwords like "AI agents" or "superintelligence." Many vendors rebrand traditional automation as AI to satisfy investor demands for AI adoption.Address Institutional Problems Before Implementing AITechnology alone cannot fix fundamental organizational issues like inefficient hiring processes or poor decision-making frameworks. Before turning to AI as a solution, consider reforming underlying business processes.Examine whether AI deployment might create arms races or unintended consequences in your industry. AI tools can sometimes exacerbate existing problems rather than solve them.Create Effective AI Governance FrameworksEstablish clear company-wide guardrails for AI experimentation while encouraging bottom-up innovation. Set explicit policies around privacy, confidentiality, and reward structures for AI-driven improvements.Implement sector-specific controls rather than trying to regulate all AI usage uniformly. Focus on how AI is used in your specific industry context rather than attempting to control the technology itself.Distinguish Between Predictive and Generative AIRecognize that predictive AI for high-stakes decisions about people's lives requires different evaluation standards than generative AI tools. Predictive AI often reduces complex causal questions to simple pattern matching.Deploy generative AI to augment human capabilities rather than entirely replacing workers. Focus on specific, well-defined tasks where AI can enhance existing workflows.Build a Responsible AI Development CultureFoster a culture that balances innovation with critically evaluating AI capabilities and limitations. Encourage transparency about which specific components use AI and what tasks the AI performs.Develop clear processes for evaluating AI vendors' claims and testing solutions before deployment. When implementing AI systems, consider both technical capabilities and broader social impacts.Key TakeawaysEvaluate AI Through Direct Testing, Not Marketing Claims<p>Real-world experimentation for a few hours provides better insight than vendor promises or academic papers when evaluating AI solutions. Companies should test AI tools in their context rather than relying on generic accuracy claims or buzzwords, as AI performance can vary significantly based on specific use cases and data sets.</p>Address Core Business Issues Before AI Implementation<p>Organizations must fix fundamental operational problems before turning to AI as a solution, as technology alone cannot resolve institutional challenges. Many companies rebrand existing automation as "AI agents" to satisfy investor pressure for AI adoption, but this approach often fails to deliver real value while potentially creating new problems.</p>Create Clear AI Experimentation Guidelines<p>Establish company-wide frameworks that balance innovation with practical controls while encouraging employees to test AI solutions. Organizations should implement specific reward structures for AI-driven improvements and set explicit policies around privacy and confidentiality, ensuring workers benefit from sharing successful AI implementations across the company.</p>Episode Participants<p>Arvind Narayanan is a professor of computer science at Princeton University and the director of the Center for Information Technology Policy. He is a co-author of the book AI Snake Oil and a newsletter of the same name, read by 40,000 researchers, policymakers, journalists, and AI enthusiasts. He previously co-authored two widely used computer science textbooks: Bitcoin and Cryptocurrency Technologies and Fairness in Machine Learning. Narayanan led the Princeton Web Transparency and Accountability Project to uncover how companies collect and use our personal information. His work was among the first to show how machine learning reflects cultural stereotypes, and his doctoral research showed the fundamental limits of de-identification. Narayanan was one of TIME's inaugural list of 100 most influential people in AI. He received the Presidential Early Career Award for Scientists and Engineers (PECASE).</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in business transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/ai-snake-oil-exposed-princeton-researcher-busts-ai-hypeMon, 13 Jan 2025 08:00:00 -0500CxOTalk RSSAgentic AI: What is it AND Does it Matter? (Part 2)
https://www.cxotalk.com/episode/agentic-ai-what-is-it-does-it-matter-part-2
<p>In CXOTalk Episode 868, host Michael Krigsman interviews Praveen Akkiraju, Managing Director at Insight Partners, to explore the world of agentic AI. Praveen explains AI agents, their reliance on large language models (LLMs), and how they differ from traditional applications. </p><p>Praveen discusses AI agents' current state and reliability, explains the layered architecture involving user interfaces, reasoning, and dynamic querying, and highlights the importance of evaluation loops and human-in-the-loop systems for consistent output. The episode also discusses practical use cases in various sectors like coding, customer support, and IT operations. It explores the complex economics, security considerations, and future trajectory of AI agents in both startups and large enterprises.</p><p>The conversation includes a discussion of the report "The State of the AI Agents Ecosystem" and the Agentic AI Market Map.</p>
Episode Chapters<p>00:00 Introduction to AI Agents00:37 Understanding AI Agents01:26 Role of Large Language Models (LLMs)02:38 Layers of AI Agents06:19 Current State and Challenges07:09 Spectrum of AI Agents09:42 Design Considerations for AI Agents16:53 Human Element in AI18:03 Training and Evaluation of AI Agents23:50 Addressing Bias in AI26:50 Navigating Constant Change in Business28:08 AI's Impact on Fortune 500 Companies29:21 The Evolution and Integration of Large Language Models30:33 Introducing the AI Agent Market Map34:20 Key Use Cases for AI Agents40:19 Economics of AI Agents43:52 Security Concerns with AI Agents47:43 Future of AI Agents and Their Evaluation50:41 Final Thoughts and Farewell</p>Episode HighlightsLeverage AI Agents as Strategic ToolsConsider AI agents as software applications that utilize LLMs in the areas where they provide the greatest value rather than viewing them as isolated LLMs. Incorporate these tools strategically into existing workflows and systems.The technology is still early, with ongoing work needed on reliability, scalability, and consistent performance. Start experimenting while being realistic about current capabilities.Design Multi-Layer Agent ArchitecturesEffective AI agents require multiple layers, including user interface, reasoning layer, dynamic querying, and evaluation loops. Each layer serves a specific purpose in creating reliable agent performance.Strong evaluation mechanisms and reflection loops are critical for testing output quality and enabling continuous improvement of agent capabilities.Balance Human-AI CollaborationCurrent AI agents work best with humans in the loop, particularly for tasks requiring deterministic outcomes. The human role includes approving plans, providing oversight, and ensuring quality control.Carefully design where and how humans interact with AI agents to maximize the benefits while maintaining reliability and accuracy.Focus on Specific Use CasesThe most promising early applications include coding assistance, customer service, IT operations, and specialized analyst support. Identify focused use cases where value can be measured.Success requires grounding agents in company policies, data, and specific workflow requirements rather than deploying generic solutions.Address Economic and Security ConsiderationsPricing models are evolving from traditional SaaS to outcome-based approaches that depend on the ability to measure value. Develop clear frameworks for evaluating ROI.Security represents both an opportunity (better threat detection) and a challenge (new vulnerabilities). Carefully consider security architecture when deploying AI agents.Key Takeaways<p>Focus on enterprise applications. There are significant opportunities for AI agents in enterprise environments. Prioritize use cases such as coding assistance, customer experience enhancement, and IT operations optimization to drive business value.</p><p>Acknowledge AI Agent Gaps. Recognize the evolving nature of AI agent technology and its challenges, such as non-determinism and security vulnerabilities. Adopt a measured approach focusing on security and clear metrics to assess economic impact.</p><p>Design for Reliability and Control</p><p>AI agents often behave in non-deterministic ways, creating risk for mission-critical tasks. Use reflection loops, human approval steps, and scaffolding techniques to ground the agent in company data and policies.</p><p>Target Clear, Measurable Use Cases</p><p>Organizations see immediate benefits by focusing AI agents on well-defined tasks such as coding support, customer service, and IT operations. Measure their impact through cost and time savings to justify further expansion. Aim for outcomes you can evaluate clearly, then scale as agent capabilities and reliability improve.</p>Episode Participants<p>Praveen Akkiraju is a Managing Director at Insight Partners. He brings a product and operational lens to investing in Automation, Data platforms, DevOps, and Infrastructure software. His investments include companies such as BigPanda, Bardeen, Reco, Rudderstack, and Workato. Praveen spent the early part of his career in the trenches, building products and scaling engineering teams to build world-class platforms in highly competitive market segments. He holds BS and MS degrees in Electrical Engineering and is an alumnus of the Harvard Business School.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep digital transformation, innovation, and leadership expertise. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/agentic-ai-what-is-it-does-it-matter-part-2Mon, 20 Jan 2025 08:00:00 -0500CxOTalk RSSEnterprise AI Adoption: A Board Member’s Perspective
https://www.cxotalk.com/episode/enterprise-ai-adoption-a-board-members-perspective
<p>This week on CXOTalk, multi-industry board director Adriana Karaboutis offers a unique look at how boards of directors shape AI adoption in large organizations. She explains how to strike the right balance between driving innovation and mitigating risk. This episode provides essential guidance for boards navigating the complexities of AI integration.</p><p>Key topics include:</p>Current State of AI Adoption: Understand the latest trends and challenges in enterprise AI implementation.The Board's Role: Discover how boards can effectively oversee AI strategy, balancing innovation with risk mitigation.Bridging the Technical Gap: Learn strategies for non-technical board members to engage meaningfully in AI discussions.Effective Governance: Explore approaches for establishing AI governance, managing risk, and ensuring ethical considerations.Asking the Right Questions: Gain practical tips on how boards can ask insightful questions about AI initiatives, even with limited time.Measuring Success: Learn how to define and track key metrics for evaluating the impact of AI investments.<p>This episode offers valuable perspectives, whether you're a board member, executive, or simply interested in the future of AI in the enterprise.</p>Episode HighlightsDrive AI Adoption Through Clear Business OutcomesTie AI initiatives directly to specific business outcomes and strategic goals rather than implementing technology for its own sake. Management should identify concrete use cases with measurable benefits before investing in AI capabilities.Tech and software companies are leading AI adoption, followed by financial services and healthcare, while regulated industries and governments tend to adopt more slowly.Balance Innovation with Risk ManagementBoards must play offensive and defensive roles - enabling AI innovation while ensuring appropriate risk controls around privacy, security, bias, and regulatory compliance. Success requires finding the right balance rather than defaulting to "no."Communication about AI parameters, data usage, and security controls is critical to help employees understand what they can and cannot do with AI tools.Focus on Enterprise-Wide AI EducationOrganizations typically have a 10-40-40-10 split: 10% AI experts, 40% experimenting, 40% novice users, and 10% resistant to adoption. Companies need comprehensive training programs to increase AI literacy.Board members must develop tech-savvy and an understanding of AI capabilities, though not necessarily deep technical expertise.Enable Responsible AI ImplementationPrioritize ethics and responsible AI practices over maximizing pure profit. Boards should ensure controls through audits, stakeholder interviews, and third-party assessments.Companies need clear governance frameworks around data security, bias prevention, and ethical AI use cases before broad deployment.Drive Cultural Change for AI SuccessAI adoption requires a cultural shift from viewing technology as just for programmers to enabling broader "citizen development." Leaders must actively manage this transformation.Effective communication between management and boards is essential, focusing on business outcomes rather than technical details.Key TakeawaysMaintain Strategic Oversight Without Micromanaging<p>Board members must focus on governance and strategy while avoiding involvement in day-to-day operations. The role requires maintaining "noses in, fingers out" - providing oversight and guidance while letting management handle execution. Success comes from understanding clear boundaries between board and management responsibilities.</p>Develop Technology Literacy<p>Board members need not become technical experts but must develop a sufficient understanding of AI and emerging technologies. This means staying current with technological advancements, potential disruptions, and implications for business strategy. The days of having just one "tech person" on the board are over - broad-based technology knowledge is now essential for most board members.</p>Balance Innovation and Risk<p>Effective board members play both offensive and defensive roles. This means enabling innovation while ensuring appropriate privacy, security, and ethics risk controls. Rather than defaulting to "no," boards should ask, "How can we make this work within our risk appetite?"</p>Focus on Business Outcomes<p>When evaluating AI and technology initiatives, concentrate on business outcomes rather than technical details. Board members should assess whether initiatives align with company strategy and deliver measurable value. Key questions should focus on return on investment, strategic alignment, and risk management.</p>Drive Cultural Transformation<p>Boards must help organizations transition from viewing technology solely for technical specialists to enabling broader adoption across the enterprise. This requires supporting comprehensive training programs while ensuring clear governance frameworks guide the responsible use of technology.</p>Episode Participants<p>Adriana Karaboutis is an independent board director at Aon plc, Perrigo plc, Autoliv, and Savills plc. She has held prior director roles at AspenTech, Advance Auto Parts, and Blue Cross Blue Shield of Massachusetts. Her career highlights include being the Global Chief Information Officer at Dell Technologies and the Group Chief Information & Digital Officer at National Grid plc. Adriana was Biogen's EVP of Technology, Business Solutions & Corporate Affairs. She is also an advisor to iGreenTree.ai.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in business transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/enterprise-ai-adoption-a-board-members-perspectiveMon, 06 Jan 2025 08:00:00 -0500CxOTalk RSSSecuring Critical Infrastructure: Precision AI for OT Environments
https://www.cxotalk.com/episode/securing-critical-infrastructure-precision-ai-for-ot-environments
<p>Critical infrastructure faces unprecedented cybersecurity challenges as operational technology (OT) systems become increasingly connected to IT networks. How can organizations protect these vital systems while enabling digital transformation?</p><p>In this episode, Michael Krigsman speaks with Anand Oswal, SVP and General Manager of Network Security at Palo Alto Networks, about securing critical infrastructure in an IT/OT convergence era. Oswal reveals that over 75% of threats to OT networks originate from IT systems, while 70% of industrial organizations experienced cyber-attacks last year alone.</p><p>Learn how precision AI and machine learning transform OT security, enabling organizations to detect and prevent known and unknown threats in real-time. Discover practical approaches to securing legacy systems, managing remote access, and maintaining compliance in harsh industrial environments - all while ensuring continuous operations of mission-critical infrastructure.</p><p>This conversation offers essential insights for technology and business leaders responsible for protecting industrial operations, manufacturing facilities, utilities, and other critical infrastructure in today's complex threat landscape.</p>Episode HighlightsSecure OT/IT Convergence in Critical InfrastructureIndustrial organizations must address the growing interconnection between operational and IT systems while managing increased cyber risks. Over 75% of OT network threats originate from IT systems, requiring a unified security approach.Implement a consistent architecture across IT and OT environments while maintaining controls specific to operational technology needs to improve visibility while protecting mission-critical systems.Deploy AI-Powered Security SolutionsTraditional signature-based security approaches are insufficient against sophisticated modern threats targeting industrial systems. Leverage precision AI combining machine learning, deep learning, and large language models for real-time threat detection.Use AI to automatically identify devices, establish baselines, and create dynamic segmentation rules. Manual configuration often leads to breaches, while AI can adapt to changing environments and new threats.Enable Secure Remote AccessImplement least-privilege access controls and secure enterprise browsers for contractors and employees accessing critical OT assets remotely. Over 50% of organizations now allow remote access to high-value industrial systems.Maintain comprehensive audit logs of all remote activities and implement zero-trust principles where no implied trust exists. Monitor and verify every connection to and from OT assets.Use Virtual Patching to Address Legacy System ChallengesUtilize virtual patching to protect outdated or unpatchable systems by blocking exploitation attempts at the network level. This provides central protection for legacy devices that cannot be directly updated.Deploy ruggedized security solutions designed for harsh industrial environments with temperature, vibration, and weather challenges. Ensure security measures don't disrupt critical operations.Automate Compliance and ReportingImplement automated systems to track assets, vulnerabilities, and security events to meet new regulatory requirements, including 72-hour breach reporting and 24-hour ransomware incident disclosure.Use AI-driven solutions to automatically generate required audit documentation and compliance reports while proactively identifying remediation needs across the OT environment.Key Takeaways<p>AI-Driven Security is Essential for Modern OT Protection. Traditional signature-based security approaches are no longer sufficient for protecting operational technology environments. Precision AI, combining machine learning, deep learning, and large language models, enables organizations to detect and prevent known and unknown threats in real-time. Palo Alto Networks stops 12 billion attacks daily, including 2.5 million previously unseen threats, demonstrating the crucial role of AI in modern security.</p><p>OT/IT Convergence Creates New Security Challenges. The increasing digitization of industrial environments has led to a critical convergence of operational and information technology systems. Over 75% of OT network threats originate from IT systems, so implementing unified security architecture across both environments is essential. This convergence demands sophisticated security solutions to protect legacy systems while enabling digital transformation.</p><p>Automated Compliance and Virtual Patching for Legacy Systems. Organizations must address the challenge of securing legacy OT systems that cannot be regularly updated or patched. Virtual patching provides a network-based solution to protect vulnerable endpoints without directly modifying them, while AI-driven automation helps maintain compliance with new regulations requiring incident reporting within 24-72 hours. This approach enables organizations to secure critical infrastructure without disrupting operations.</p>Episode Participants<p>Anand Oswal serves as Senior Vice President and General Manager at cyber security leader Palo Alto Networks where he leads the company’s Firewall as a Platform efforts. He holds more than 60 U.S. patents and earned a bachelor’s degree in telecommunications from the College of Engineering, Pune, India and a master’s degree in computer networking from the University of Southern California, Los Angeles.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in digital transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Securityhttps://www.cxotalk.com/episode/securing-critical-infrastructure-precision-ai-for-ot-environmentsTue, 17 Dec 2024 08:00:00 -0500CxOTalk RSSAI Ethics and Trustworthy AI: Navigating Truth and Deception
https://www.cxotalk.com/episode/ai-and-trust-navigating-truth-deception-and-human-impact
<p>In episode 864 of CXOTalk, hosted by Michael Krigsman, the topic of discussion is trustworthy versus deceptive AI, featuring three prominent researchers. Dr. Anastassia Lauterbach, the Founder and CEO of AI Edutainment; Dr. David Bray, Distinguished Chair of the Accelerator at the Stimson Center; and Dr. Anthony Scriffignano, Distinguished Fellow at the Stimson Center, share practical methods for developing governance frameworks, addressing cybersecurity challenges, and ensuring that humans are involved in automated decision-making.</p><p>Key points from this episode for business and technology leaders include:</p>Establish accountability for data handling, bias mitigation, and model retraining.Balance innovation with compliance to ensure responsible AI deployment.Collaborate among legal, technical, and business teams to shape AI strategy and investments.Episode HighlightsDevelop a Governance ApproachForm a cross-functional group that includes legal, technical, and business leaders to steer AI policies. They should set clear guardrails and accountability measures, so everyone understands roles and responsibilities.Establish transparent methods for tracking AI outcomes in data collection, bias mitigation, and model retraining. This ensures any risks or failures are quickly identified and resolved.Elevate AI LiteracyProvide employees with short, targeted training sessions that explain AI fundamentals in everyday language. This helps everyone evaluate vendor claims and avoid adopting tools that don’t address real business needs.Offer continuous learning paths and clear documentation so team members know how AI models work. These steps reduce overreliance on “black box” solutions and empower more informed decision-making.Prepare for Evolving Cyber ThreatsConduct regular security audits and invest in monitoring tools that detect AI-driven attacks. This proactive approach protects corporate infrastructure and sensitive data.Form alliances with industry peers and cybersecurity experts to share insights on emerging threats and mitigation strategies. Such collaboration helps you stay ahead of criminals who are constantly adapting AI for malicious purposes.Balance Innovation and ComplianceEvaluate new AI opportunities by factoring in revenue potential and regulatory obligations. Early alignment with legal and ethical standards prevents costly remediation later.Communicate openly with stakeholders about how AI systems handle data, make decisions, and stay compliant. This builds trust, reduces confusion, and positions the company as a responsible innovator.Keep Humans in the LoopRequire that critical decisions made by AI-driven tools undergo human review, particularly when safety or ethical considerations are involved. This process minimizes the risk of unapproved actions or biased outcomes.Define specific intervention points where employees can question, refine, or override automated processes. By maintaining human oversight, leaders ensure transparency and safeguard against unintended consequences.Key TakeawaysFocus on Real Business Needs<p>Narrow your AI strategy to well-defined problems that impact revenue, customer experience, or operations. Align leaders with the outcomes and metrics that matter most to your organization. This approach helps you avoid chasing hype and accelerates meaningful returns.</p>Anticipate AI-Driven Fraud and Misinformation<p>Remain vigilant to new tactics criminals use to exploit large language models and deepfakes. Establish rapid-response plans and cross-functional teams to detect, counter, and minimize reputational damage. By anticipating bad actors’ methods, you protect customers and stay one step ahead of emerging threats.</p>Foster Cross-Disciplinary Collaboration<p>Engage teams from marketing, IT, finance, and legal in regular discussions about AI opportunities and risks. Combine diverse viewpoints to ensure your policies and solutions are ethical and feasible. A unified approach strengthens decision-making and drives more balanced outcomes across the organization.</p>Episode Participants<p>Dr. Anthony Scriffignano is an internationally recognized data scientist. He has an extensive background in advanced anomaly detection, computational linguistics, and inferential methods, is the primary inventor of over 100 patents worldwide, and is currently a Distinguished Fellow with The Stimson Center.</p><p>Dr. David A. Bray is a Distinguished Fellow and Chair of the Accelerator with the Alfred Lee Loomis Innovation Council at the non-partisan Henry L. Stimson Center. Previously he was Chief Information Officer at the Federal Communications Commission and a Senior National Intelligence Service Executive with the U.S. Intelligence Community.</p><p>Dr. Anastassia Lauterbach is a Non-Executive Director of Aircision and Freight One. She is also a member of the Advisory Council of Nasdaq and the Diligent Institute. Formerly, she was Non-Executive Director of easyJet PLC, Dun & Bradstreet, and served as Chairwoman of the Board of Directors of Censhare AG.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator who has written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/ai-and-trust-navigating-truth-deception-and-human-impactMon, 16 Dec 2024 08:00:00 -0500CxOTalk RSSAI Governance and Ethics: Personal Perspectives from a CIA Privacy Professional
https://www.cxotalk.com/episode/ai-governance-and-ethics-personal-perspectives-from-a-cia-privacy-professional
<p>On CXOTalk episode 863, join a fascinating conversation with Jacqueline Acker, Esq., a privacy professional from the Central Intelligence Agency, as we explore the critical intersection of AI governance, ethics, and national security. This episode offers unique insights into the personal views of how the CIA approaches AI ethics and governance while balancing operational security with transparency.</p><p>Drawing from her experience at the CIA, Acker discusses the practical challenges of implementing ethical AI frameworks in high-stakes environments. She shares valuable perspectives on developing robust governance processes, establishing accountability structures, and creating effective compliance programs for AI systems.</p><p>Key topics include:</p>Building ethical AI programs that balance innovation with responsibilityPractical approaches to AI governance and risk managementThe role of leadership in fostering ethical AI cultureSimilarities and differences between government and private sector AI ethicsBest practices for AI ethics auditing and compliance<p>This episode provides business and technology leaders with actionable insights from the intelligence community's approach to AI ethics, offering valuable lessons for organizations developing their own ethical AI frameworks.</p><p>[Here is a link to the National Security Commission on Artificial Intelligence (NSCAI) report mentioned during the discussion.]</p>Episode HighlightsBuild Trust Through Responsible AI GovernanceCreate clear governance frameworks with documented roles, responsibilities, review cycles, and decision-making protocols to demonstrate responsible AI use. Establish regular assessment schedules based on each AI system's risk level and rate of change.Develop transparent documentation showing how AI systems are developed, what rules they follow, and how they protect user privacy and rights. This builds confidence with stakeholders and enables trust-based partnerships.Establish Cross-Functional AI Ethics ProgramsSecure executive sponsorship and assemble interdisciplinary teams, including legal, technical, policy, and domain experts, to develop effective AI governance initiatives. Having data scientists work alongside policymakers ensures frameworks are both principled and practical.Before deploying AI systems, create thorough impact assessments covering privacy, security, bias, and other risks. Regular reassessment is crucial as capabilities evolve and new risks emerge.Learn From Government ExperienceStudy mature federal frameworks around data privacy, impact assessments, and transparency reporting that have evolved over 50+ years. Government experience offers valuable lessons for private-sector AI governance.Adapt proven public sector documentation standards and oversight mechanisms while accounting for your organization's specific context and requirements. Look especially at Privacy Act compliance models.Implement Dynamic Review ProcessesMove beyond traditional "set and forget" technology governance to establish flexible frameworks that adapt to rapidly changing AI capabilities. High-risk systems need more frequent assessment than conventional technologies.Monitor emerging regulations, technical advances, and societal expectations to ensure governance keeps pace with change. Build adaptable processes that can evolve as new use cases emerge.Balance Innovation with ControlsDefine clear boundaries and standards that enable rapid innovation while maintaining appropriate safeguards and oversight. Like adding brakes to cars, good governance enables faster progress.Focus governance intensity based on risk level and potential impact. Critical AI systems require more robust controls and frequent review than lower-risk applications.Key TakeawaysTrust is the Foundation for AI Success<p>Building and maintaining stakeholder trust is essential when deploying AI systems. As the CIA's experience shows, organizations must demonstrate responsible AI use through clear governance frameworks, consistent oversight, and transparent communication about how AI systems are developed and used. Without trust, organizations risk losing their authority, customer base, or social license to innovate with AI technologies.</p>Dynamic Governance is Critical<p>Unlike traditional technology systems, AI governance cannot follow a static "set and forget" approach. Organizations need flexible frameworks that adapt to rapidly evolving AI capabilities, emerging regulations, and changing societal expectations. This requires regular reassessment of AI systems based on their risk level and rate of change, with more frequent reviews for high-risk or rapidly evolving systems. The pace of change in AI technology demands continuous monitoring and updating of governance approaches.</p>Build Cross-Functional Teams for AI Ethics<p>Effective AI governance requires collaboration across technical, legal, policy, and domain experts. Having data scientists work alongside policymakers helps ensure principled and practical governance frameworks. Organizations should establish interdisciplinary teams with executive sponsorship to develop comprehensive approaches addressing ethical considerations and operational requirements. This collaborative approach helps create more effective and implementable solutions that work in the real world.</p>Episode Participants<p>Jacqueline Acker, Esq., is a Deputy Privacy and Civil Liberties officer at the Central Intelligence Agency. She earned a Bachelor of Arts from the University of Texas at Austin in 2010, followed by a Juris Doctorate from the University of Tulsa College of Law in 2013. Her academic journey set the stage for her future achievements, particularly in the areas of privacy, data security, and ethical technology. Since January 2022, Jackie has been an Adjunct Assistant Professor at American University Washington College of Law, teaching Information Privacy and Data Security. Her role involves educating students on privacy laws and integrating emerging issues into the curriculum, preparing the next generation of professionals in this critical field.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in digital transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/ai-governance-and-ethics-personal-perspectives-from-a-cia-privacy-professionalMon, 09 Dec 2024 08:00:00 -0500CxOTalk RSSHow to Get and Keep Customers with AI and Personalization
https://www.cxotalk.com/episode/the-ai-personalization-playbook-whats-new-and-what-works
<p>David Edelman, a senior advisor at the Boston Consulting Group (BCG) and the author of "Personalized: Customer Strategy in the Age of AI," joins Michael Krigsman on CXOTalk episode 857 to discuss the transformative potential of AI-driven personalization.</p><p>The conversation explores how organizations can use AI to foster more meaningful customer relationships, emphasizing the importance of moving beyond mass marketing to deliver individualized experiences that resonate with each customer’s unique needs and preferences.</p><p>The episode examines Edelman’s five critical promises of effective personalization: empower me, know me, reach me, show me, and delight me. He discusses the challenges companies encounter when implementing AI-driven personalization, including data silos and the risk of intrusive marketing.</p><p>The discussion emphasizes practical advice for creating customer-centric organizations, encouraging cross-functional collaboration, and developing the skills and capabilities needed to thrive in the age of AI. He emphasizes that Chief Marketing Officers (CMOs) must adopt strategic leadership roles, advocating for personalization and promoting the organizational changes necessary to compete effectively in today’s market.</p><p>This episode is a must-watch for CXOs, business leaders, and anyone seeking to understand the transformative power of AI in customer strategy and create deeper, more valuable customer relationships.</p>Episode HighlightsEmbrace AI-powered personalization to enhance customer relationships.Treat personalization as the core of your customer relationship strategy, not just a marketing tactic. Leverage AI to deeply understand individual customer needs and preferences and use this knowledge to create valuable and relevant experiences.Go beyond simple demographic or behavioral segmentation. Strive for "segment of one" marketing, where you tailor interactions to each customer's unique context, creating a sense of individual recognition and appreciation.Mitigate the risks of intrusive or irrelevant personalization.Avoid bombarding customers with excessive or irrelevant marketing messages. Prioritize quality interactions over quantity, focusing on delivering timely and valuable information that respects their preferences and avoids a "creepy" factor.Implement clear data governance policies and guidelines. Establish ethical data collection, usage, and sharing boundaries, ensuring transparency and customer consent. Designate a responsible leader, perhaps a chief data or privacy officer, to oversee data management and compliance.Design personalized experiences that empower customers.Focus on creating solutions that address genuine customer needs. Ask yourself, "How can we use AI to help customers achieve their goals more easily and effectively?" This approach builds trust and fosters long-term loyalty.Shift from simply providing product information to delivering personalized solutions. Consider the entire customer journey and identify opportunities where AI can empower customers at each stage, streamlining processes, offering tailored recommendations, and facilitating informed decision-making.Adapt your marketing organization for AI-driven personalization.Embrace agile, cross-functional teams ("pods") to facilitate rapid experimentation and iteration. Break down data silos and foster collaboration between departments like strategy, analytics, creative, operations, technology, and compliance.Invest in training and development to enhance employees' skills. Equip your marketing team with the knowledge and capabilities to leverage AI tools effectively, interpret data insights, and develop personalized experiences. Encourage the development of generalist skills as AI automates more specialized tasks.Establish a data-driven culture focused on customer lifetime value.Align your organization around the goal of maximizing customer lifetime value. Shift from a product-centric approach to a customer-centric approach, prioritizing long-term relationships over short-term gains.Measure the impact of personalization initiatives based on your specific business goals. Define clear metrics related to customer engagement, retention, sales growth, cost reduction, or other relevant objectives. Use these metrics to track progress, demonstrate ROI, and continuously optimize your personalization strategies.
How to use AI-powered personalization (from CXOTalk episode 857)
Key Takeaways<p>Personalization Drives Competitive Advantage. AI-powered personalization is transforming how businesses compete. By understanding and addressing individual customer needs at scale, organizations can build stronger relationships, increase customer lifetime value, and differentiate themselves in the market. This shift requires a strategic approach, moving beyond tactical marketing campaigns to create personalized experiences that genuinely empower customers.</p><p>Data Silos Hinder Personalization Efforts. Organizational silos and a lack of cross-functional collaboration can prevent companies from realizing the full potential of AI-driven personalization. Balkanized data, where different departments hoard customer information, leads to fragmented and ineffective marketing efforts. Companies should break down these silos, fostering data sharing and a unified view of the customer to create seamless, personalized experiences.</p><p>CMOs Must Step Up as Strategic Leaders. In the age of AI, chief marketing officers have a unique opportunity to become strategic leaders within their organizations. CMOs can drive customer-centric transformation and unlock significant value by championing personalization as a core business strategy. CMOs must embrace AI, advocate for cross-functional collaboration, and reshape their marketing organizations for agility and speed.</p>Episode Participants<p>David Edelman spent over 30 years as a chief marketing officer at Aetna and CVS. He built consultancy businesses in digital and marketing transformation with McKinsey & Company, Digitas, and the Boston Consulting Group. He now teaches marketing at Harvard Business School and is an advisor to top executives in startups, private equity, and larger enterprises. Having driven large-scale change from both the client and client-service side, he is well-suited to help CXOs shape their strategic direction, build their teams' capabilities, and become more digitally agile. David coauthored the book Personalized: Customer Strategy in the Age of AI.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in digital transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Customer Experiencehttps://www.cxotalk.com/episode/the-ai-personalization-playbook-whats-new-and-what-worksWed, 23 Oct 2024 08:00:00 -0400CxOTalk RSSWhat is CX Automation?
https://www.cxotalk.com/episode/what-is-cx-automation
<p>AI is transforming customer experience (CX) automation and on this episode of CXOTalk, Dan Bodner, Founder and CEO of Verint, shares practical strategies for integrating AI into contact centers. Bodner explains how AI can enhance customer satisfaction, reduce operational costs, and turn contact centers into revenue generators. He discusses real-world examples, such as automating call summaries with AI to boost efficiency and provide more personalized service and using AI-powered coaching bots to improve real-time agent performance.</p><p>Verint's approach emphasizes augmenting existing human agents with AI rather than replacing them by seamlessly embedding AI tools into current workflows. This allows for a smooth transition and demonstrable results. Key takeaways include leveraging AI to improve CX and reduce costs, integrating AI into existing workflows for enhanced agent efficiency, using AI bots to augment the workforce, and starting small with AI initiatives before scaling based on measured outcomes.</p>Episode HighlightsEmbrace CX Automation to Elevate Customer Experience and Reduce CostsImplement AI solutions to automate customer interactions, enhancing customer satisfaction while reducing operational expenses.Balance cost savings with improved customer experiences to transform your contact center into a revenue-generating asset.Integrate AI into Existing Workflows to Enhance Agent EfficiencyEmbed AI tools within current processes to assist employees without disrupting their routine, ensuring seamless adoption.Use AI to automate repetitive tasks like call summaries, allowing agents to focus on delivering empathetic and personalized service.Leverage AI Bots to Augment, Not Replace, Your WorkforceDesign AI systems that collaborate with staff, augmenting their capabilities rather than replacing them for a more effective workforce.Employ bots for specific functions—such as real-time coaching or compliance prompts—to support agents during customer interactions.Invest in Real-Time Data Training for Effective AI PerformanceContinuously train AI models on fresh, real-time data to maintain effectiveness and relevance in dynamic customer service environments.Develop unified data hubs to centralize customer interaction data, enabling AI to learn and adapt to changing behaviors and needs.Start Small with AI Initiatives and Scale Based on Measurable OutcomesLaunch AI projects with limited scope to quickly demonstrate value and gain stakeholder buy-in through tangible results.Measure key metrics like increased agent capacity and improved customer satisfaction to guide the gradual expansion of AI solutions.
Key Takeaways<p>Transform Customer Experience with AI to Reduce Costs and Improve Customer Satisfaction. Business leaders can implement AI in contact centers to enhance customer experiences while lowering operational expenses. By automating customer interactions, AI allows companies to provide faster, more efficient service, turning contact centers into revenue-generating assets without increasing budgets.</p><p>Integrate AI into Existing Workflows to Boost Agent Efficiency. Embedding AI tools within current processes enables agents to work more effectively without disrupting their routines. This approach automates repetitive tasks, freeing staff to focus on delivering empathetic and personalized service that strengthens customer relationships.</p><p>Begin with Small AI Initiatives and Scale Based on Results. Starting with limited-scope AI projects allows leaders to demonstrate value and gain stakeholder support quickly. By measuring outcomes like increased agent capacity and improved customer satisfaction, companies can gradually make informed decisions to expand AI solutions.</p>Episode Participants<p>Dan Bodner started Verint in 1994 with a focus on unstructured data analytics. Under his leadership, the company experienced rapid growth. It became an Actionable Intelligence market leader with one division focused on the customer engagement market and another on the security intelligence market. Dan led the company through a successful IPO, and Verint became a public company in 2002 (NASDAQ: VRNT). Post-IPO, Dan continued to lead the company’s growth journey organically and through strategic acquisitions and reached scale with over $1.3 billion in revenue. In Feb. 2021, Verint executed a successful public company spin-off, and its security intelligence division became a separate public company named Cognyte. Today, Dan is the Chairman and CEO of Verint, a pure-play Customer Engagement company.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep digital transformation, innovation, and leadership expertise. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Customer Experiencehttps://www.cxotalk.com/episode/what-is-cx-automationTue, 08 Oct 2024 08:00:00 -0400CxOTalk RSSAI, Deep Learning, and the Future of Work
https://www.cxotalk.com/episode/ai-and-the-future-of-work-an-ai-neuroscience-pioneer-speaks
<p>Artificial intelligence is rapidly transforming business, technology, and society. On this episode of CXO Talk, Dr. Terrence Sejnowski, a renowned computational neuroscientist, deep learning pioneer, and author of "ChatGPT and the Future of AI," discusses the implications of this technological revolution. He explores how AI is evolving, drawing parallels with the human brain, and explains why a robust data strategy is crucial for successful AI implementation. Dr. Sejnowski holds the Francis Crick Chair at the Salk Institute for Biological Studies and is a Distinguished Professor at UC San Diego.</p><p>Dr. Sejnowski explains the importance of lifelong learning for employees and emphasizes AI's role in augmenting, not replacing, human capabilities. He also addresses critical topics such as explainability in AI decision-making, ethical considerations, and the potential impact of AI on the future of work. This discussion offers practical guidance for business and technology leaders navigating the complexities of AI integration and its implications for their organizations.</p>Episode HighlightsEmbrace lifelong learning for your workforce.Provide opportunities for employees to upskill and reskill in AI-related technologies through online courses, workshops, and mentorship programs. Focus on the practical application of AI tools within their current roles.Cultivate a continuous learning and adaptability culture within your organization to navigate the evolving technological landscape and maintain a competitive edge.Integrate AI strategically to augment human capabilities.View AI as a tool to enhance productivity and decision-making rather than a complete replacement for human workers. Prioritize AI applications that complement existing workflows and address specific business challenges.Focus on areas where AI excels, such as data analysis, pattern recognition, and automation of repetitive tasks, while leveraging human expertise for complex problem-solving, critical thinking, and emotional intelligence.Develop a robust data strategy.Prioritize data quality, accuracy, and completeness as the foundation for successful AI implementation. Invest in data governance frameworks, cleaning processes, and robust data management systems.Tailor data collection and curation practices to align with specific business objectives and ethical considerations. Recognize the potential for bias in datasets and implement strategies for mitigation.Prioritize explainability and ethical considerations in AI deployment.Seek AI solutions offering transparency and insights into their decision-making processes, especially in critical areas like healthcare and finance. Encourage the development of "fact-checking" layers to enhance reliability.Establish clear ethical guidelines for AI development and deployment within your organization. Consider the potential societal impact of AI systems and strive to minimize harm and promote fairness.Prepare for the evolving future of work.Recognize that AI will reshape job roles and organizational structures over time. Foster a culture of agility and encourage employees to embrace change and adapt to new working methods.Explore how AI can enable new collaboration and knowledge sharing across teams and departments. Consider re-evaluating traditional hierarchies and embracing more fluid, cross-functional teams.
Key Takeaways<p>AI Augments Human Potential: Deep learning pioneer Dr. Terrence Sejnowski emphasizes that AI is a powerful tool to enhance human capabilities, not replace them. Leaders should focus on integrating AI strategically to improve productivity and decision-making by addressing specific business challenges and complementing existing workflows. This approach maximizes the benefits of human expertise and AI's computational power.</p><p>Data Strategy is Key for AI Success: Sejnowski highlights the critical role of high-quality data in successful AI implementation. Business leaders must prioritize data accuracy, completeness, and governance to mitigate bias and ensure reliable AI outcomes. A robust data strategy fuels effective AI and unlocks its transformative potential.</p><p>Embrace Lifelong Learning in the Age of AI: The rapid evolution of AI necessitates continuous learning and adaptation. Sejnowski advises business leaders to invest in upskilling and reskilling their workforce to utilize new AI tools effectively. Cultivating a culture of continuous learning prepares organizations for the evolving future of work and ensures they can leverage AI's full potential.</p>Episode Participants<p>Terrence J. Sejnowski is Francis Crick Chair at The Salk Institute for Biological Studies and Distinguished Professor at the University of California at San Diego. He has published over 500 scientific papers and 12 books, including ChatGPT and The Future of AI: The Deep Language Learning Revolution. He was instrumental in shaping the BRAIN Initiative that was announced by the White House in 2013, and he received the prestigious Gruber Prize in Neuroscience in 2022 and the Brain Prize in 2024. Sejnowski was also a pioneer in developing learning algorithms for neural networks in the 1980s, inventing the Boltzmann machine with Geoffrey Hinton; this was the first learning algorithm for multilayer neural networks and laid the foundation for deep learning. He is the President of the Neural Information Processing Systems (NeurIPS) Foundation, which organizes the largest AI conference, and he is a leader in the recent convergence between neuroscience and AI.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in digital transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/ai-and-the-future-of-work-an-ai-neuroscience-pioneer-speaksTue, 19 Nov 2024 08:00:00 -0500CxOTalk RSSGlobal AI Governance and Digital Equity: A UN Perspective
https://www.cxotalk.com/episode/ai-perspectives-from-the-united-nations-insights-and-practical-lessons
<p>In episode 862 of CXOTalk, United Nations Assistant Secretary-General and Chief Information Technology Officer Bernardo Mariano Jr. shares an inside view of the United Nations’ approach to AI governance, ethics, and equitable access. He discusses the newly adopted Global Digital Compact and its core commitments, including reducing the digital divide, ensuring data governance, and fostering security and interoperability.</p><p>Learn how the UN balances innovation with responsibility, seeks international cooperation to prevent AI-driven harm, and works to align the interests of governments, the private sector, and civil society to shape a more inclusive and beneficial global digital landscape.</p>Episode HighlightsPromote Inclusive AI Governance FrameworksEncourage international collaboration to develop clear, balanced regulations that reflect diverse interests and safeguard human rights.Engage with global institutions, including the UN, to establish common standards that ensure access, trust, and fairness in AI adoption.Close the Digital and AI DivideInvest in affordable broadband and computing power to bring underserved communities online and ensure equitable AI benefits.Encourage flexible pricing and distribution strategies, enabling access to AI tools at scale without undermining business sustainability.Integrate Ethical and Human-Centered AI PracticesAdopt widely recognized ethical guidelines, such as those outlined by UNESCO, to reduce algorithmic bias and promote trustworthy AI outputs.Involve civil society, academia, and private-sector stakeholders to shape responsible data policies that protect vulnerable populations.Foster Broader Industry Engagement and AdvocacySupport open-source communities and private-sector innovators to establish global standards and frameworks.Leverage advocacy to influence national policies, encouraging responsible AI use in regions that may not initially embrace global frameworks.<p>Align Commercial Incentives with Social Value</p>Offer tiered pricing or differentiated market approaches that balance profitability with equitable access.Reassess product strategies so that growth, innovation, and profit coexist with humanitarian goals, ultimately expanding long-term market opportunities.Key Takeaways<p>Adopt Global AI Principles to Build Trust. Leaders who adopt international governance frameworks strengthen AI credibility and mitigate cross-border risks. This approach aligns stakeholder incentives and reduces uncertainty, paving the way for sustainable growth and reliable innovation.</p><p>Balance Profitability with Equitable Access. Leaders who set flexible pricing and distribution strategies expand their customer base while preserving profits. By ensuring fair access to AI tools, they foster long-term stability, reduce market inequalities, and support sustainable development.</p><p>Leverage Open-Source Communities for Innovation. Leaders who engage with open-source projects gain insights into emerging tools, frameworks, and best practices. This engagement creates better standards, ensures adaptability, and encourages responsible AI use across diverse markets.</p>Episode Participants<p>Bernardo Mariano Jr. is the Chief Information Technology Officer (CITO), Assistant Secretary-General, Office of Information and Communications Technology, on 1 June 2021. His tenure began 1 August 2021. Mr. Mariano brings to the position 28 years of experience within the United Nations system and international organizations, most recently serving as the Chief Information Officer and Director for Digital Health and Innovation at the World Health Organization (WHO), where he led the organization’s digital transformation journey, leveraging digital technologies and innovations to accelerate the achievement of WHO strategic goals.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in digital transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/ai-perspectives-from-the-united-nations-insights-and-practical-lessonsMon, 02 Dec 2024 08:00:00 -0500CxOTalk RSSEnterprise AI Strategy: Algorithms and Ecosystems
https://www.cxotalk.com/episode/enterprise-ai-strategy-algorithms-and-ecosystems
<p>In episode 852 of CXOTalk, host Michael Krigsman explores the world of Enterprise AI strategy with two distinguished guests: Anindya Ghose, professor at NYU's Stern School of Business, and Ravi Bapna, chair professor at the University of Minnesota's Carlson School of Management. These experts discuss their new book, Thrive: Maximizing Well-Being in the Age of AI, which offers a comprehensive framework for implementing AI in business.</p><p>The conversation covers a wide range of topics, including the "House of AI" framework, the importance of data engineering, and challenges of AI adoption in organizations. Ghose and Bapna share practical insights on overcoming barriers to AI implementation, addressing ethical concerns, and building AI-ready workforces. They also explore the potential of AI to drive innovation and competitive advantage, even for smaller companies with limited datasets.</p>Episode HighlightsBuild a Strong AI FoundationPrioritize data engineering as the foundation of your AI strategy, allocating at least 70% of resources to cleaning and preparing data.Implement the "House of AI" framework, focusing on descriptive, predictive, causal, and prescriptive analytics pillars to drive value from your data.Overcome Barriers to AI AdoptionAddress the "three I's" hindering AI implementation: inertia, ignorance, and lack of imagination.Foster a culture of innovation by educating leadership on AI use cases and potential benefits across various business functions.Leverage AI for Competitive AdvantageExplore AI applications beyond predictive modeling, such as causal inference, to understand the "why" behind business outcomes and scale recommendations effectively.Use transfer learning and fine-tuning techniques to overcome small dataset limitations and compete with larger companies.Address AI Ethics and Bias ProactivelyImplement de-biasing processes in your AI workflows, including data cleaning, algorithm adjustment, and output validation.Develop metrics to measure fairness in AI models and be prepared to recalibrate when biases are detected.Cultivate an AI-Ready WorkforceUpskill existing talent and recruit professionals with a holistic understanding of AI, including causal inference and experimental design capabilities.Educate executives on AI potential and use cases to bridge the gap between technical AI capabilities and business leadership.Key Takeaways<p>Prioritize Data Engineering for AI Success. Data engineering forms the foundation of successful AI implementation. Spend at least 70% of resources on cleaning and preparing data before diving into modeling. This investment in data quality pays dividends across all AI applications, from descriptive analytics to advanced predictive and causal modeling.</p><p>Overcome Barriers to AI Adoption. Address the "three I's" hindering AI implementation: inertia, ignorance, and lack of imagination. Educate leadership on AI use cases and potential benefits across business functions. Start small with existing data and gradually build AI capabilities, using techniques like transfer learning to overcome the limitations of smaller datasets.</p><p>Balance Predictive and Causal Analytics. While predictive modeling is valuable, causal inference is crucial for understanding the "why" behind business outcomes and scaling recommendations effectively. Invest in developing causal modeling skills within your workforce. This balanced approach enables more robust decision-making and helps address potential biases in AI systems.</p>Episode Participants<p>Anindya Ghose is an award-winning professor of business at NYU Stern and author of the best-selling book TAP: Unlocking the Mobile Economy. Ghose has been named among the top 1% of researchers in his field and recognized as one of 30 management thinkers most likely to shape the future. He has published more than 115 papers in premier scientific journals and peer-reviewed conferences and has given more than 300 talks internationally. He’s consulted for Apple, Facebook, Google, Microsoft, Samsung, Snapchat, Tinder and Verizon, among other companies. He has provided expert testimony in many high-profile trials and depositions, including the Tinder vs. Match valuation lawsuit, the Facebook IPO matter, the counterfeit goods case against Amazon, and more. He has been interviewed, and his research has been profiled numerous times in the BBC, Bloomberg TV, CNBC, Wall Street Journal, The Economist, Financial Times, Fox News, TIME, The Guardian, and elsewhere.</p><p>Ravi Bapna is the chair of business analytics and information systems at the University of Minnesota’s Carlson School of Management. His research investigates online dating, social media, social engagement, the causal effect of AI and ML innovations such as recommender systems, analytics, economics of information systems, trust and peer influence online, human capital in digital services and online auctions. His work has been published in numerous journals, including Management Science, Informs Journal on Computing, Statistical Science, Information Systems Research, Journal of Retailing, MIS Quarterly, and Decision Sciences. His views have also been featured in the Financial Times, Wall Street Journal, Knowledge@Wharton, and The Economic Times, among others. He founded the Analytics for Good Institute at the University of Minnesota and is the Inaugural INFORMS ISS Practical Impacts Award winner for his analytics and digital transformation work.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep digital transformation, innovation, and leadership expertise. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/enterprise-ai-strategy-algorithms-and-ecosystemsMon, 09 Sep 2024 08:00:00 -0400CxOTalk RSSAI and Data Science: Stop the Madness!
https://www.cxotalk.com/episode/deep-dive-real-ai-with-real-data-scientists
<p>CXOTalk episode 856 explores the critical intersection of AI and data science with Dr. Satyam Priyadarshy, CEO of ReIgnite Future and former Chief Data Scientist at Halliburton, and Dr. Anthony Scriffignano, Distinguished Fellow at the Stimpson Center and former Chief Data Scientist at Dun & Bradstreet. They discuss the practical realities of implementing AI, emphasizing what truly works, what doesn't, and the reasons behind AI project failures. They examine common misconceptions, such as the idea that AI is a magic bullet, highlighting the importance of data quality, rigorous analysis, and effective communication between data scientists and business leaders.</p><p>Dr. Priyadarshy and Dr. Scriffignano also address the ethical implications of using synthetic data and offer practical advice on building a data-driven culture while managing computational costs. They explore the challenges of navigating organizational politics and biases that can hinder the success of data science initiatives. This conversation offers valuable guidance for business and technology leaders seeking to unlock the potential of AI and avoid common pitfalls </p>Episode HighlightsRecognize the Pitfalls of Confirmation Bias in Data ScienceActively challenge existing beliefs and encourage diverse perspectives within data science teams to prevent biased data analysis.Establish transparent processes that prioritize objective evaluation, minimizing the influence of preconceived notions on decision-making.Understand the Crucial Role of Data Quality in AIInvest in robust data governance frameworks to ensure data accuracy, completeness, and relevance throughout the AI lifecycle.Implement data quality checks at every stage, from collection and preprocessing to model training and deployment, to maintain AI effectiveness.Navigate the Complex Relationship Between Data Scientists and Business LeadersFoster open communication and mutual understanding of roles and expertise to enhance collaboration between data scientists and business leaders.Encourage clear and transparent presentation of findings while empowering leaders to ask questions without imposing predetermined outcomes.Address the Ethical Implications of Synthetic Data in AIBe aware of potential biases that synthetic data can introduce, and implement measures to prevent amplification of existing or artificial biases.Ensure transparency in the use of synthetic data by establishing ethical guidelines for its generation and application.Develop a Data-Driven Culture While Managing CostsPrioritize data initiatives that align with business goals and demonstrate clear value to maximize resource efficiency.Explore cost-effective data storage and processing strategies, such as cloud-based solutions and open-source tools, to manage costs effectively.
AI Implementation Strategies
Key TakeawaysEmphasize Data Quality and Context to Maximize AI Effectiveness<p>Data quality and context are crucial for successful AI implementation. Business leaders should invest in robust data governance frameworks to ensure data is accurate, complete, and relevant. Organizations can make informed decisions and avoid misinterpretations that undermine AI initiatives by understanding the context in which data is collected and used.</p>Foster Collaboration Between Data Scientists and Business Leaders<p>Effective communication between data scientists and business leaders bridges understanding gaps and aligns objectives. Leaders should encourage open dialogue, allowing data scientists to present findings transparently while being receptive to insights that may challenge existing assumptions. This collaboration enables organizations to use AI effectively and drive meaningful changes.</p>Address Bias in Data and AI Models to Ensure Fair Outcomes<p>Bias in data and AI models can lead to unfair or inaccurate results. Leaders must implement strategies to find and mitigate biases in datasets and algorithms. Regularly reviewing assumptions and validating AI models maintains integrity and trust in data-driven decisions, leading to more equitable and effective outcomes.</p>Episode Participants<p>Anthony Scriffignano, Ph.D. is an internationally recognized data scientist with experience spanning over 40 years in multiple industries and enterprise domains. Scriffignano has extensive background in advanced anomaly detection, computational linguistics and advanced inferential methods, leveraging that background as primary inventor on multiple patents worldwide. He also has extensive experience with various boards and advisory groups. He is a Distinguished Fellow with The Stimson Center, a nonprofit, nonpartisan Washington, D.C. think tank and a member of the OECD Network of Experts on AI working group on implementing Trustworthy AI.</p><p>Dr. Satyam Priyadarshy is the CEO of Reignite Future. He was previously Chief Data Scientist and a Technology Fellow at Halliburton. He was also the Managing Director of Halliburton’s India Center. He often is recognized as the first Chief Data Scientist of the oil and gas industry. Recently Forbes India named him as one of the top 10 outstanding business leaders. His work or profile has appeared in many places including Chemical and Engineering News, The Scientist, Silicon India, Oil ReviewMiddle East, Petroleum Review, Rigzone, Forbes among others.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator, known for his deep expertise in the fields of digital transformation, innovation, and leadership. He has presented at industry events around the world and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/deep-dive-real-ai-with-real-data-scientistsMon, 14 Oct 2024 03:30:00 -0400CxOTalk RSSStrategy with Seth Godin: Hard Talk and Painful Truths
https://www.cxotalk.com/episode/strategy-with-seth-godin-hard-talk-and-painful-truths
<p>In CXOTalk episode 858, marketing legend and bestselling author Seth Godin challenges conventional thinking about strategy and leadership. Drawing from his new book "This Is Strategy," Godin explores the critical distinction between strategy and tactics, offering fresh perspectives for senior executives navigating today's complex business landscape.</p><p>The conversation explores how leaders can craft enduring strategies, emphasizing the importance of long-term thinking over quick fixes. Godin shares his perspective on embracing constraints as catalysts for innovation and discusses the growing role of AI in strategic decision-making.</p><p>Key topics include:</p>Why most strategic planning fails and how to avoid common pitfallsThe concept of the "smallest viable audience" and its impact on business successHow empathy and systems thinking shape effective leadershipPractical frameworks for developing and communicating clear, memorable strategiesThe future of strategy in an AI-driven world<p>Whether you're a C-suite executive, business leader, or entrepreneur, this discussion addresses your challenges. It provides practical guidance to help you navigate the complexities of modern leadership and create a lasting impact in your organization.</p><p>Join Michael Krigsman and Seth Godin for this frank, unvarnished conversation about the hard truths of strategy and what it takes to succeed in today's rapidly evolving business environment.</p>Episode HighlightsDistinguish Strategy from TacticsDevelop a Clear Strategic Vision: Executives should focus on crafting an overarching strategy that guides decision-making and aligns with the organization's core mission. This strategic vision should be a compass for all organizational activities, ensuring coherence and direction.Avoid Tactical Myopia: Many organizations fall into the trap of focusing solely on tactical execution without a clear strategic framework. Leaders must resist getting lost in day-to-day operational details and maintain a broader, long-term perspective.Embrace Change and AdaptationAnticipate Market Shifts: Encourage your leadership team to stay attuned to emerging technology and market dynamics trends. Develop scenario planning exercises to prepare for potential disruptions and opportunities.Cultivate Organizational Agility: Foster a culture that values flexibility and quick adaptation. This might involve creating cross-functional teams, implementing agile methodologies, or establishing innovation labs to test new ideas rapidly.Empower Employees with StrategyCascade Strategic Understanding: Ensure every employee understands and can articulate the company's strategy, from the C-suite to entry-level positions. This shared understanding enables more cohesive decision-making at all levels.Promote Autonomy and Trust: Provide employees with the autonomy and resources necessary to execute tactics that support the overall strategy. This approach fosters a culture of trust and innovation, leading to more engaged and productive teams.Leverage Technology WiselyAlign Technology with Strategy: Ensure all technology investments and implementations directly support your strategic goals. Avoid the temptation to chase every new technological trend without a clear purpose.Balance Innovation and Pragmatism: Consider whether being a fast follower or an early adopter of new technologies aligns best with your strategic objectives. Sometimes, waiting for technologies to mature can be more advantageous than being first to market.Focus on Empathy and Human NeedsConduct Deep Customer Research: Go beyond surface-level market research to understand your target audience's underlying needs, desires, and pain points. This empathetic approach can uncover opportunities for innovation and differentiation.Prioritize Employee Well-being: Apply the same empathetic lens to your workforce. Strategies that consider the human element of your organization are more likely to succeed as they foster engagement, loyalty, and productivity.Key Takeaways<p>Strategic Vision Over Tactical Execution: Develop a clear, long-term vision that guides all organizational activities. This overarching strategy is a compass, ensuring that short-term tactical actions align with the company's overall mission and goals. Prioritize strategic thinking to avoid getting lost in the weeds of daily operations.</p><p>Embrace Change and Empower Your Team: A rapidly changing business landscape requires flexible and adaptive strategies. Leaders must anticipate market shifts and cultivate organizational agility. Empower employees by clearly communicating the company's strategic vision and fostering a culture of trust and autonomy.</p><p>Empathy Fuels Effective Strategies: Understanding the needs of both customers and employees is crucial for developing resonant and effective strategies. Deep customer research helps uncover unmet needs and opportunities for innovation. Prioritizing employee well-being fosters engagement, loyalty, and productivity, ultimately contributing to organizational success.</p>
Episode Participants<p>Seth Godin is the author of 21 international bestsellers that have changed how people think about work and art. They have been translated into 38 languages. His books include Unleashing the Ideavirus, Permission Marketing, Purple Cow, Tribes, The Dip, Linchpin, The Practice, and This is Marketing. His new book, This is Strategy, is an essential guide to thinking strategically in a complex, ever-changing world. He writes one of the most popular daily blogs in the world and has given 5 TED talks.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in digital transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Leadershiphttps://www.cxotalk.com/episode/strategy-with-seth-godin-hard-talk-and-painful-truthsMon, 04 Nov 2024 08:00:00 -0500CxOTalk RSSTough Talk: Ethical and Responsible AI in Finance
https://www.cxotalk.com/episode/tough-talk-ethical-and-responsible-ai-in-finance
<p>Although artificial intelligence is transforming financial services, the ethical implications demand careful consideration. In CXOTalk episode 859, Dr. Scott Zoldi, Chief Analytics Officer at FICO, discusses responsible AI development and deployment in the financial sector.</p><p>With decades of experience leading data science and AI research, Dr. Zoldi brings a practical perspective on building trust, transparency, and accountability into AI systems. He explores the challenges of black box AI, the necessity of explainable AI (XAI), and the role of robust ethical frameworks in mitigating bias and ensuring fairness.</p>Episode HighlightsImplement Responsible AI Practices Across the OrganizationEstablish a cross-functional ethics committee to define and enforce ethical AI guidelines for your industry and business. This committee should include data scientists, legal experts, product experts, and other relevant stakeholders.Prioritize ethical considerations alongside performance metrics when developing AI models. Consider defining success not just by model accuracy, but also by its fairness and lack of bias towards protected groups.Embrace Explainable AI (XAI) to Foster Transparency and TrustFavor interpretable machine learning models over black box algorithms, especially for high-stakes decisions. This allows for clear explanations of how the model arrives at its outcomes, facilitating scrutiny and building stakeholder trust.Develop internal tools and processes to extract and analyze the learned relationships within AI models, ensuring they align with ethical guidelines and do not perpetuate biases.Establish a Robust Model Development Standard and Life CycleCreate a comprehensive model development standard that outlines specific steps for data collection, preprocessing, model training, validation, and deployment. This standard should incorporate ethical considerations at every stage.Use blockchain technology or immutable record-keeping systems to document the model development process, ensuring transparency and accountability. This helps prevent post-hoc manipulation of ethical standards.Prioritize Data Quality and RepresentativenessTreat data as a liability, not just an asset. Scrutinize data for biases, inaccuracies, and gaps in representation before using it to train AI models. Ensure the data represents the population the model will be used to assess.Validate the outcome data used to train AI models to ensure its accuracy and relevance to the problem being solved. Inaccurate or incomplete outcome data can lead to flawed and biased models.Foster a Culture of Ethical AI Development and DeploymentEmbed ethical AI principles into the company culture, starting with leadership buy-in and extending throughout the organization. This includes training data scientists and other stakeholders on ethical considerations.Engage with industry groups and other organizations to share best practices and develop joint ethical AI development and deployment standards. This fosters collaboration and accelerates progress in the field.
Key Takeaways<p>Prioritize Explainable AI for Transparency and Trust. Black box AI models erode trust and hinder regulatory compliance. Leaders should prioritize explainable AI (XAI) to justify AI-driven decisions. This transparency builds confidence with customers and regulators while simplifying the auditing process.</p><p>Establish Company-Wide Ethical AI Frameworks. Building ethical AI is a shared responsibility, not solely a data science function. Create a cross-functional ethics committee to establish and enforce AI governance standards, including legal, product, and ethics experts. This ensures consistent ethical practices across the AI lifecycle.</p><p>Leverage Blockchain for Auditable and Accountable AI. Blockchain technology creates an immutable record of the AI development process. This reinforces ethical practices by preventing post-hoc manipulation of standards and providing a transparent audit trail. This builds trust and demonstrates a commitment to responsible AI.</p>Episode Participants<p>Dr. Scott Zoldi is chief analytics officer at FICO, responsible for artificial intelligence (AI) and analytic innovation across FICO's product and technology solutions. While at FICO, he has authored more than 120 analytic patents, with 88 granted and 39 pending. Scott is an industry leader at the forefront of Responsible AI, and an outspoken proponent of AI governance and regulation. His groundbreaking work in AI model development governance, including a patented use of blockchain technology for this application, has helped propel Scott to AI visionary status, with recent awards received including a Future Thinking Award at Corinium Global’s Business of Data Gala. Scott serves on the Boards of Directors of Software San Diego and San Diego Cyber Center of Excellence. He received his Ph.D. degree in theoretical and computational physics from Duke University.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in digital transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/tough-talk-ethical-and-responsible-ai-in-financeMon, 11 Nov 2024 08:00:00 -0500CxOTalk RSSInside the Advanced AI Lab: Agentic AI, Robots, and Levitating Trees
https://www.cxotalk.com/episode/inside-the-advanced-ai-lab-agentic-ai-robots-and-levitating-trees
<p>In CXOTalk Episode 854, host Michael Krigsman speaks with Babak Hodjat, Cognizant's CTO of AI and head of the company’s Advanced AI Lab, on the emerging field of agentic AI, evolutionary AI, and the impact of AI on the future of work.</p><p>Hodjat argues that multi-agent systems, comprised of individual agents that perform specific tasks, offer several advantages over traditional centralized AI systems, including increased robustness, future-proofing, and the ability to leverage the power of natural language. He highlights the importance of responsible AI and the need for careful consideration of ethical implications, particularly in the context of potential bias in training data.</p><p>The interview also explores the role of evolutionary computation in enhancing AI creativity and its applications in diverse fields, from website optimization to robot locomotion. A fascinating point of the discussion centers on how robots “learn” to navigate in an environment where trees levitate and float in space.</p><p> Hodjat ultimately envisions a future where businesses adopt agent-based enterprises, comprised of interconnected agents working together to achieve complex goals, but emphasizes the need for human-centric design and responsible implementation.</p>Episode HighlightsEmbrace Multi-Agent Systems to Enhance AI Capabilities.Explore how breaking down complex AI tasks into smaller, interconnected agents can improve scalability, manageability, and robustness compared to monolithic AI systems.Consider adopting a multi-agent architecture when developing AI solutions for intricate business processes or workflows that involve multiple interconnected steps or functions.Mitigate AI Hallucinations Through Agent-Based Frameworks.Implement techniques like uncertainty measurement and agent-specific context definition to reduce the risk of AI models generating inaccurate or fabricated information.Establish transparent verification and validation processes, potentially incorporating human oversight, to ensure the reliability and trustworthiness of AI-generated outputs, especially in critical decision-making scenarios.Integrate AI into Decision-Making Processes for Enhanced Outcomes.Recognize that AI can augment human decision-making by analyzing vast datasets, optimizing for multiple objectives, and providing insights that may not be readily apparent to human decision-makers.Develop strategies to leverage AI for decision support, particularly when complex data analysis or multi-objective optimization is required while retaining human oversight for ethical considerations and final decision authority.Prepare for Workforce Transformation by Upskilling and Reskilling Employees.Analyze the potential impact of AI on job roles within your organization and identify tasks that are likely to be automated or augmented by AI.Invest in training programs to equip employees with the skills needed to effectively collaborate with AI systems and focus on tasks that require uniquely human capabilities like critical thinking, creativity, and emotional intelligence.Leverage Evolutionary Computation to Drive Innovation and Adaptability.Explore how evolutionary algorithms, potentially combined with large language models, can generate creative solutions to complex problems and adapt to changing environments.Consider applying evolutionary computation techniques to areas like website optimization, robotics, and product design to explore a broader range of potential solutions and discover innovative approaches that may not be readily apparent through traditional methods.Key TakeawaysAdopt Multi-Agent Systems to Build More Powerful and Reliable AI.<p>Deconstruct complex AI tasks into a network of smaller, specialized agents. This approach improves scalability, manageability, and robustness while creating opportunities for integrating human expertise and oversight into the AI system.</p>Leverage AI to Enhance Human Decision-Making and Achieve Superior Outcomes.<p>Integrate AI into decision-making processes to benefit from its ability to analyze vast datasets, optimize for multiple objectives, and uncover insights that might be missed by human decision-makers alone. Maintain human control over ethical considerations and final decision authority.</p>Prepare Your Workforce for the Age of AI by Investing in Upskilling and Reskilling.<p>Analyze the potential impact of AI on your organization's roles and identify tasks likely to be automated or augmented. Implement training programs that equip your employees with the skills needed to effectively collaborate with AI and focus on tasks that require uniquely human capabilities.</p>Important Ideas<p>Agentic AI represents a significant evolution beyond the capabilities of current generative AI models. Its ability to interact with and influence its environment opens new possibilities for automation and decision-making.</p><p>Multi-agent systems provide a framework for modeling and potentially optimizing organizational structures, mirroring the division of labor and interdependencies within human organizations. This concept extends beyond software development, potentially impacting diverse industries.</p><p>Addressing the "hallucination" problem inherent in LLMs is crucial for building trust and reliability in agentic AI systems. Hodjat proposes integrating uncertainty measurement tools and designated "checker" agents to mitigate risks.</p><p>Human-AI collaboration will be vital to navigating the future of work. While AI will automate tasks across various job roles, Hodjat believes human oversight, creativity, and adaptability will remain essential.</p><p>Evolutionary computation and LLMs are powerful approaches to developing adaptable AI systems. This approach proves particularly effective in dynamic environments where constant learning and adaptation are critical.</p>
Episode Participants<p>Babak Hodjat is the CTO of AI at Cognizant. He is responsible for the core technology behind the world’s largest distributed artificial intelligence system. Babak was also the founder of the world's first AI-driven hedge fund, Sentient Investment Management. He is a serial entrepreneur, having started several Silicon Valley companies as the main inventor and technologist. A published scholar in the fields of artificial life, agent-oriented software engineering and distributed artificial intelligence, Babak has 31 granted or pending patents to his name. He is an expert in numerous fields of AI, including natural language processing, machine learning, genetic algorithms and distributed AI and has founded multiple companies in these areas. Babak holds a Ph.D. in machine intelligence from Kyushu University in Fukuoka, Japan.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep digital transformation, innovation, and leadership expertise. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/inside-the-advanced-ai-lab-agentic-ai-robots-and-levitating-treesSun, 29 Sep 2024 08:00:00 -0400CxOTalk RSSWhat is Agentic AI? Intelligent Agents and Autonomous Workflows
https://www.cxotalk.com/episode/what-is-agentic-ai-intelligent-agents-and-autonomous-workflows
<p>Organizations are increasingly turning to artificial intelligence (AI) to automate tasks and workflows, improve efficiency, and drive innovation. In CXOTalk episode 853, host Michael Krigsman and Praveen Akkiraju, Managing Director at VC firm Insight Partners, discuss the promising field of agentic AI. They discuss the capabilities, applications, and implications of AI agents for businesses, focusing on the pivotal roles of data infrastructure, trust, and transparency in successful implementations.</p><p>Akkiraju demystifies the complexities of agentic AI, presenting these technologies in a way that is accessible to business and technology leaders. He addresses concerns about job displacement, highlighting how these technologies can augment human capabilities and unlock greater productivity. The conversation also outlines vital factors to consider when evaluating agentic AI solutions—such as reasoning abilities, data requirements, and ethical considerations—empowering leaders to navigate the enterprise's rapidly evolving landscape of AI agents confidently.</p>Episode HighlightsEmbrace Agentic AI to Drive Productivity GainsFocus on automating repetitive, non-value-added back-office tasks with agentic AI to unlock significant productivity gains across various business functions.View agentic AI as an evolution of traditional automation, offering enhanced dynamism and adaptability compared to older technologies like RPA.Harness the Power of Reasoning and Planning in Agentic AIPrioritize AI agents with robust planning and reasoning capabilities to handle complex tasks and automate decision-making processes more effectively.Evaluate and experiment with techniques like chain-of-thought reasoning and reinforcement learning to enhance the accuracy and reliability of agentic AI systems.Prioritize Data Infrastructure and Integration for Agentic AI SuccessInvest in establishing a solid data infrastructure with accessible APIs to empower AI agents with the information they need to execute tasks effectively.Utilize techniques like fine-tuning, RAG, and embeddings to optimize models for specific domain-specific data and use cases within your organization.Build Trust and Transparency into Agentic AI SystemsImplement safeguards, such as reflection loops and comprehensive testing, to ensure the accuracy and reliability of agentic AI outputs and build trust in the technology.Incorporate transparency features like provenance tracking and explainability into agentic workflows, enabling users to understand how AI agents arrive at their conclusions.Adopt a Strategic Approach to Agentic AI AdoptionEvaluate existing enterprise application architectures and identify opportunities to integrate AI agents for greater efficiency and automation.Focus on building deterministic systems around non-deterministic LLMs by carefully managing prompts, implementing guardrails, and leveraging suitable techniques to ensure reliable and predictable outcomes.
Key TakeawaysUnlock Productivity with Agentic AI<p>Adopting agentic AI offers businesses a significant advantage, enabling them to automate repetitive back-office processes. This drives substantial productivity gains across various functions and allows organizations to redirect human resources towards higher-value work, thereby increasing efficiency and potentially reducing operational costs.</p>Prioritize Reasoning and Planning in Your AI Strategy<p>Reasoning and planning capabilities play a pivotal role in the effectiveness of agentic AI. Evaluating AI agents based on their ability to handle complex tasks autonomously through techniques like chain-of-thought reasoning is crucial. This approach enhances the automation of decision-making processes and improves the accuracy of AI-driven outcomes.</p>Build Trust and Transparency into Agentic AI Implementations<p>Building trust in agentic AI is critical for its widespread adoption and achieving positive results. Investing in solutions that prioritize transparency and demonstrate how decisions are made and how data is used is essential. Additionally, organizations should implement governance frameworks and ethical guidelines to ensure responsible AI usage and avoid potential biases or negative consequences.</p>Episode Participants<p>Praveen Akkiraju is a Managing Director at Insight Partners. He brings a product and operational lens to investing in Automation, Data platforms, DevOps, and Infrastructure software. His investments include companies such as BigPanda, Bardeen, Reco, Rudderstack, and Workato. Praveen spent the early part of his career in the trenches, building products and scaling engineering teams to build world-class platforms in highly competitive market segments. He holds BS and MS degrees in Electrical Engineering and is an alumnus of the Harvard Business School.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep digital transformation, innovation, and leadership expertise. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/what-is-agentic-ai-intelligent-agents-and-autonomous-workflowsSun, 22 Sep 2024 08:00:00 -0400CxOTalk RSSState of the Enterprise and the CIO, with Unisys CEO
https://www.cxotalk.com/episode/state-of-the-enterprise-and-the-cio-with-unisys-ceo
<p>In CXOTalk episode 848, Peter Altabef, Chair and CEO of Unisys, explores enterprise technology transformation and the impact of AI on business. Altabef shares his perspective on how Unisys uses AI across its operations, from client solutions to internal processes, and discusses the company's approach to AI investment and project management.</p><p>The conversation covers various topics, including the importance of investment discipline in AI initiatives, strategies for engaging the C-suite in technology adoption, and the role of generative AI in driving innovation. Altabef also offers insights into organizational culture, leadership approaches, and CIOs' evolving role in the AI age. Throughout the interview, he emphasizes the need for companies to balance current technological advancements with future innovations, such as quantum computing.</p>Episode HighlightsEmbrace AI's transformative potential for innovation and growthRecognize that AI, particularly generative AI, can open new possibilities and reimagine how companies serve their customers.Encourage a mindset shift from incremental improvements to game-changing innovations that drive revenue and market expansionImplement a disciplined approach to AI investment and adoptionEstablish an investment framework that prioritizes AI projects based on ROI and strategic alignmentFoster a culture of continuous learning and experimentation while keeping a focus on measurable outcomes and value creationAlign investments with business goalsEnsure regular reporting to enhance accountability and track progress. This helps keep focus on strategic objectives and ensures resources are allocated efficiently.Prioritize projects that align with strategic business objectives to maximize returns. This approach fosters disciplined investment and avoids chasing trends without clear business value.Cultivate a servant leadership culture to navigate AI-driven changeLead by example, demonstrating a commitment to helping others succeed and adapt to new roles in an AI-enabled organizationFoster open communication, transparency, and personal connections to build trust and resilience in the face of uncertaintyPrepare for the future of computing beyond classical systemsStay informed about emerging technologies like quantum computing that could exponentially increase AI's potentialDevelop a long-term strategy that positions your organization to capitalize on these advancements as they matureKey TakeawaysDrive Investment Discipline<p>Investment discipline is crucial to ensure resources are allocated effectively and not wasted on projects that lack a solid business case. By regularly reporting on AI projects, Unisys maintains focus and accountability, encouraging teams to prioritize initiatives that align with strategic objectives. This disciplined approach prevents the allure of trendy technologies from overshadowing meaningful business outcomes.</p>Expand AI Projects for Greater Impact<p>Unisys has significantly increased its AI project portfolio from 51 to 135 projects, demonstrating solid internal traction. This expansion reflects a strategic effort to integrate AI across various business functions, enhancing productivity and innovation. By categorizing projects into proofs of concept, pilots, and production, Unisys ensures a balanced and scalable approach to AI implementation, fostering both short-term wins and long-term growth.</p>Engage the C-Suite in Technology Adoption<p>Engaging multiple C-suite members in technology discussions broadens the strategic impact of AI initiatives. As AI solutions increasingly influence revenue and market positioning, interest from CEOs and other executives grows. This engagement elevates AI from a cost-saving tool to a driver of innovation and competitive advantage, highlighting the importance of cross-functional collaboration in technology adoption.</p>Episode Participants<p>Peter Altabef has served as chair of the board of Unisys Corporation since April 2018 and as CEO of Unisys since January 2015. He also served as company president from 2015 to 2020 and December 2021 to May 2022. Before joining Unisys, Peter served as president and CEO of MICROS Systems, Inc., president and CEO of Perot Systems Corporation, and president of Dell Services, Dell's information technology services and business process solutions unit.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in digital transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Digital Transformationhttps://www.cxotalk.com/episode/state-of-the-enterprise-and-the-cio-with-unisys-ceoTue, 06 Aug 2024 08:00:00 -0400CxOTalk RSSVC Update: Investing in Early Stage Enterprise AI
https://www.cxotalk.com/episode/vc-update-investing-in-early-stage-enterprise-ai
<p>Ed Sim, founder and general partner of boldstart ventures, shares his perspective on investing in enterprise AI startups. With over 25 years of experience in enterprise technology investments, Sim offers a unique view into how AI reshapes the startup landscape and what investors look for in AI-driven companies.</p><p>In this interview, Sim discusses the importance of integrating AI into existing products and processes rather than creating standalone AI solutions. He emphasizes the need for startups to focus on solving real customer problems and using AI to enhance their offerings significantly. Sim also explores the challenges of balancing rapid development with scalable processes in AI initiatives and highlights the value of building long-term relationships in the AI ecosystem.</p>Episode HighlightsLeverage AI to enhance existing products and workflowsEvaluate how AI can improve your current offerings or internal processes, rather than building standalone AI productsFocus on solving real customer problems first, then consider how AI can make your solution 10x betterPrioritize data privacy and security in AI implementationsAddress enterprise concerns about data protection, especially for regulated industriesConsider offering on-premises or private cloud deployment options to give customers control over their dataBalance speed and process as you scale AI initiativesIn early stages, prioritize rapid product iteration and learning over rigid processesAs you grow, gradually introduce more structure while maintaining agilityLook beyond general-purpose AI to industry-specific applicationsExplore opportunities to apply AI to specialized vertical use cases, leveraging domain-specific dataConsider how AI can enhance compliance, risk management, or other industry-specific workflowsBuild long-term relationships with AI partners and investorsWhen seeking funding or partnerships, focus on developing mutual understanding and alignment, not just transactionsLook for partners who are passionate about your problem space and can support you through multiple venturesKey TakeawaysAI is a Force Multiplier, Not a Standalone Solution<p>Integrate AI into existing products and processes to enhance value rather than develop as standalone solutions. Business leaders should first identify core problems their customers face and then explore how AI can make their solutions significantly more effective or efficient. This approach ensures AI investments deliver tangible benefits and meet real market needs.</p>Balance Speed and Structure in AI Development<p>In the initial stages of AI initiatives, prioritize rapid product iteration and learning over rigid processes. As projects mature, gradually introduce more structure while maintaining agility. This balance allows organizations to quickly validate AI concepts and adapt to market feedback while establishing the necessary framework for scalable, enterprise-grade solutions.</p>Build Long-Term Relationships in the AI Ecosystem<p>When seeking AI partnerships or investments, develop mutual understanding and alignment rather than pursuing quick transactions. Look for partners who are passionate about your problem space and can support you through multiple ventures. These enduring relationships provide not only financial backing but also valuable expertise and support during the inevitable challenges of AI development and deployment.</p>Episode Participants<p>Ed Sim is the founder of boldstart ventures, a true believer and partner from Inception for bold founders reinventing the enterprise stack. Ed is currently on the boards of Snyk, BigID, Blockdaemon, Protect AI, Env0, and a number of other cybersecurity and infrastructure startups. Other notable inception investments include Kustomer where he was on the board until exiting to Meta, Superhuman, Security Scorecard, and Front. Ed has been recognized as a Top 10 investor on the Forbes Midas Seed List for the last 3 years and also as the No.1 seed investor in the Business Insider Seed 100 for 2023 and 2024. </p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in digital transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/vc-update-investing-in-early-stage-enterprise-aiMon, 29 Jul 2024 08:00:00 -0400CxOTalk RSSStanford d.school: How to Design a GREAT Story?
https://www.cxotalk.com/episode/stanford-d-school-how-to-design-a-great-story
<p>In this episode of CXOTalk, host Michael Krigsman welcomes Carissa Carter, the Academic Director of the d.school at Stanford University, to explore the intricate relationship between storytelling, design thinking, and data in business communication. As co-author of "Assembling Tomorrow," Carter shares her perspective on how storytelling can help leaders make sense of an ever-changing world characterized by rapid technological advances and complex challenges. Throughout the interview, Carter emphasizes the importance of clarity, purpose, and values in crafting narratives that resonate with audiences and drive change.</p><p>Carter also addresses the role of bias in data storytelling, encouraging leaders to acknowledge their biases and those of their audience to create more authentic and impactful stories. As artificial intelligence reshapes the storytelling landscape, Carter highlights the need for business and technology leaders to engage with AI thoughtfully while staying vigilant about its potential for misinformation. This conversation provides valuable insights for executives looking to use storytelling as a powerful tool for communication and leadership in the digital age.</p>
Episode HighlightsHarness Storytelling to Enhance Business CommunicationClarify the purpose of your story by identifying whether it addresses the past, present, or future. This will help you connect more effectively with your audience by aligning with their values.Use storytelling as a unifying force in organizations, conveying complex ideas in a way that makes them relatable and memorable for all stakeholders.Integrate Values into Corporate NarrativesEmbed your organizational values into the stories you tell to align with your audience's beliefs and enhance authenticity, as demonstrated by brands like Patagonia.Recognize that bias from the storyteller and the audience is inevitable. Embrace transparency as a powerful tool to mitigate misunderstandings and build trust.Utilize Visual Data StorytellingBalance data, bias, and craft to create impactful visual narratives. This helps present information clearly and persuasively.Practice deconstructing infographics to understand emotional reactions and improve your data presentations.Leverage AI for Innovative StorytellingExperiment with AI to generate ideas and enhance creativity, but remain aware of its limitations, such as potential biases and misinformation risks.Stay informed about AI developments to harness its potential while safeguarding against its pitfalls, especially in data-driven narratives.Simplify Complex Concepts for Diverse AudiencesBreak down sophisticated technical ideas into simple, relatable explanations to engage non-technical audiences effectively.Match the level of detail in your presentations to your audience’s needs and the feedback you seek, ensuring clarity and understanding.Key TakeawaysEmphasize Storytelling with Clarity and Purpose<p>Business leaders must clarify the purpose of their storytelling to effectively engage their audience, whether reflecting on the past, describing the present, or envisioning the future. Understanding and articulating the motivation, audience, and value system behind a narrative can unify organizations and help convey complex ideas more effectively.</p>Recognize and Manage Bias in Data Storytelling<p>Every narrative, including data-driven stories, contains bias from the storyteller and the audience. Business leaders should acknowledge their biases and those of their audience, using transparency to mitigate potential misunderstandings. By understanding this dynamic, leaders can craft stories that resonate more truthfully and effectively with diverse audiences.</p>Adapt to the AI-Driven Storytelling Landscape<p>AI can serve as a creative tool for storytelling, offering new ways to generate ideas and envision possibilities. However, leaders must remain cautious of AI's potential to produce misinformation or biased narratives. Embracing AI while actively questioning and refining its outputs can help ensure technology is a beneficial partner in crafting authentic and meaningful stories.</p>Episode Participants<p>Carissa Carter is the Director of Teaching and Learning at the Stanford d.school. In this role she guides the development of the d.school’s pedagogy, leads its instructors, and shapes its class offering. She teaches courses on the intersection of data and design, design for climate change, and maps and the visual sorting of information. Carissa was one of the co-leaders of Stanford 2025, a multi-year d.school project that envisioned the future of higher education. Before the d.school, Carissa ran her own design practice, Parallel Design Labs, and focused on helping companies of all sizes execute their internal innovation projects and programs. She also spent time living in Hong Kong working for Herman Miller leading their user research efforts for the Asian market.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep digital transformation, innovation, and leadership expertise. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Leadershiphttps://www.cxotalk.com/episode/stanford-d-school-how-to-design-a-great-storyMon, 15 Jul 2024 08:00:00 -0400CxOTalk RSSAI and Cybersecurity Update from Palo Alto Networks
https://www.cxotalk.com/episode/ai-and-cybersecurity-update-from-palo-alto-networks
<p>In this episode of CXOTalk, Anand Oswal, Senior Vice President and General Manager of Network Security at Palo Alto Networks, discusses the rapid adoption of AI in business and the critical importance of securing AI applications. As organizations increasingly use AI to enhance productivity and transform customer experiences, they face new challenges in mitigating risks associated with data exposure, supply chain vulnerabilities, and runtime threats.</p><p>Anand emphasizes the need for a comprehensive approach to securing AI-powered applications, from ensuring visibility and control over employee usage to protecting against configuration risks and runtime attacks. He highlights the importance of balancing productivity and security while enabling organizations to harness AI's full potential. Anand also shares insights on the evolving AI security landscape and how Palo Alto Networks collaborates with industry leaders to develop robust security frameworks for AI applications.</p>Episode HighlightsSecure AI Applications by DesignEnsure that AI applications are integrated into the enterprise environment with complete visibility and control over data protection and threat protection policies. This involves setting the right level of data protection policies to protect sensitive data and enabling threat protection for responses from AI applications.Implement AI security posture management to secure AI-powered applications from configuration risks, supply chain risks, and runtime threats such as prompt injection attacks, model DOS attacks, and data leakage.Manage Shadow AI RisksIdentify and monitor AI applications used by employees, whether approved by IT or not, to ensure that sensitive data is not exposed. This includes having visibility into all application attributes to make informed decisions about usage.Develop policies and recommendations to allow, deny, or limit the usage of AI applications based on their attributes and potential risks, ensuring that productivity is not compromised.Protect AI-Powered Applications from ThreatsImplement holistic security measures to secure AI-powered applications from classical and AI-specific threats. This includes protecting against supply chain and configuration risks, as well as runtime threats like prompt injection and model DOS attacks.Collaborate with other leaders and vendors to develop joint reference architectures for securing AI applications, such as the partnership between Palo Alto Networks and NVIDIA.Balance Security and ProductivityEnsure that employees can access AI applications without compromising security. This involves providing complete visibility into AI usage across the enterprise and implementing data protection and threat protection policies.Automate policy creations and recommendations to enable agile and fast deployment of AI applications, ensuring that security measures do not hinder productivity.Adopt a Platform-Centric Security ApproachSimplify and unify network security by adopting a platform-centric approach rather than relying on multiple point products. This helps reduce operational costs and complexity while improving security outcomes.Use AI copilots to help customers use platforms and products effectively, simplifying operations and enhancing security posture.Key TakeawaysSecuring Shadow AI: Balance Productivity and Risk<p>The rapid adoption of AI tools by employees, often without IT approval, creates a "shadow AI" phenomenon. Over 57% of employees use AI applications to boost productivity, potentially exposing sensitive company data. Organizations need visibility into AI usage to make informed decisions about allowing, denying, or limiting access. Implementing robust data protection policies and threat detection measures is crucial to balance productivity gains with security concerns.</p>Holistic Security for AI-Powered Applications<p>As companies develop AI-powered applications, they must adopt a comprehensive security approach. This involves protecting against supply chain risks, configuration vulnerabilities, and runtime threats specific to AI, such as prompt injection and model denial-of-service attacks. Organizations should implement AI security posture management and runtime security measures to safeguard their entire AI ecosystem, including models, infrastructure, tools, datasets, and plugins.</p>Embracing a Platform-Centric Approach to Network Security<p>The complexity of managing multiple point solutions for network security is becoming unsustainable. CIOs and CISOs should adopt a platform-centric approach to simplify and unify their network security infrastructure. This strategy can lead to better security outcomes, lower operational costs, and increased agility in implementing new policies consistently across the entire infrastructure. A unified platform allows for easier management of traditional and AI-specific security concerns.</p>Episode Participants<p>Anand Oswal is the Senior Vice President and General Manager at cybersecurity leader Palo Alto Networks, where he leads the company’s firewall-as-a-platform efforts. He holds more than 60 U.S. patents and earned a bachelor’s degree in telecommunications from the College of Engineering, Pune, India and a master’s degree in computer networking from the University of Southern California, Los Angeles.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator, known for his deep expertise in the fields of digital transformation, innovation, and leadership. He has presented at industry events around the world and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Securityhttps://www.cxotalk.com/episode/ai-and-cybersecurity-update-from-palo-alto-networksMon, 05 Aug 2024 08:00:00 -0400CxOTalk RSSIs the Enterprise Browser Secure? A CISO Speaks.
https://www.cxotalk.com/episode/is-the-enterprise-browser-secure-a-ciso-speaks
<p>In CXOTalk episode 850, host Michael Krigsman speaks about cybersecurity challenges with Jeff Schilling, Chief Information Security Officer of Teleperformance, one of the world's largest business process outsourcing firms. With 500,000 employees across 100 countries, Teleperformance faces unique security risks due to its global reach and diverse client base. </p><p>Schilling discusses how his team manages these challenges, using innovative technologies like the Island Enterprise Browser to enhance security while maintaining employee productivity.</p><p>The conversation explores Teleperformance's approach to balancing robust security measures with operational efficiency, the importance of collaboration between security and IT teams, and strategies for effective risk management in a complex, multinational environment. Schilling also shares insights on measuring security value and implementing new technologies across a global organization.</p>Episode HighlightsImplement granular security controls through enterprise browsersUtilize enterprise browsers to manage employee interactions with corporate and client environments, allowing for fine-tuned control over data access and usage.Leverage browser-based security features to extend the lifespan of hardware investments and reduce endpoint capital expenditures.Balance security measures with user experienceStrive to make security measures invisible to employees, engineering security out of the user experience where possible.Implement flexible policies and quick approval processes to accommodate legitimate business needs while maintaining security.Collaborate closely with clients on shared risk managementConduct regular risk assessments for each client to identify and categorize risks as company-owned, jointly-owned, or client-owned.Provide documented reports to clients detailing the results of security risk assessments and recommendations for mitigation.Align security strategy with IT infrastructureFoster a strong partnership between the CISO and CIO to ensure security measures support and enhance overall IT strategy.Integrate security solutions with existing IT service management tools to streamline processes and improve response times.Adapt security training and awareness programsCreate engaging, scenario-based training that focuses on practical situations employees may encounter.Regularly test employee knowledge through simulated phishing campaigns and use results to inform penetration testing and red team exercises.Key TakeawaysTransform the browser from a security risk into an enterprise asset<p>The enterprise browser can be a powerful tool for managing security risks and improving operational efficiency. By implementing granular controls through the browser, companies can protect sensitive data, reduce hardware costs, and simplify the user experience. This approach allows organizations to balance robust security measures with employee productivity, creating a win-win situation for IT and business operations.</p>Implement shared risk management with clients<p>In a business process outsourcing environment, security is a shared responsibility between the company and its clients. Regular risk assessments and clear communication about risk ownership are crucial for maintaining strong client relationships and protecting both parties' interests. Companies can build trust and differentiate themselves in the market by proactively identifying and addressing security concerns.</p>Design invisible security for maximum effectiveness<p>The most effective security measures are often those employees don't notice. By engineering security controls into the background of daily operations, companies can reduce the burden on employees while keeping a strong security posture. This approach minimizes disruptions to workflow, increases compliance, and allows employees to focus on their core responsibilities rather than complex security procedures</p>Episode Participants<p>Jeff Schilling is Teleperformance’s Chief Information Security Officer responsible for the overall direction, coordination, and evaluation of the cybersecurity function and global information security incident response. He serves as the strategic advisor to the Board of Directors and C-Suite on all matters relating to cybersecurity posture, readiness, investment, and risk. He is a retired U.S. Army Colonel with 24 years of military experience in IT service management, product management, Chief Information Officer roles, information security, and global cyber operations. Since retiring from military service, Jeff’s functions have included managing an international incident response practice and multiple Chief Information Security Officer positions for global multitenant service provider companies.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep expertise in digital transformation, innovation, and leadership. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Securityhttps://www.cxotalk.com/episode/is-the-enterprise-browser-secure-a-ciso-speaksThu, 15 Aug 2024 08:00:00 -0400CxOTalk RSSGenerative AI and Business Transformation at New York Life
https://www.cxotalk.com/episode/generative-ai-and-business-transformation-at-new-york-life
<p>In this episode of CXOTalk, Michael Krigsman speaks with Don Vu, Chief Data and Analytics Officer at New York Life, about the role of generative AI in driving business transformation. As a 179-year-old company and the largest mutual life insurance company in the US, New York Life is leveraging AI and data strategies to enhance client and agent experiences while navigating the challenges of legacy systems and change management.</p><p>Throughout the conversation, Vu shares insights on aligning AI initiatives with business goals, fostering collaboration between teams, and empowering employees with AI tools. He discusses the company's multi-threaded approach to AI adoption, which includes leveraging existing tools, targeting specific use cases, and exploring innovative solutions. Vu also highlights the importance of integrating AI and data strategies, addressing technical debt, and preparing for an AI-driven future in the life insurance industry.</p>Episode HighlightsIntegrate AI and data strategies:Ensure that AI and data strategies are not just aligned, but deeply integrated with the business strategy. This alignment is the key to driving meaningful transformation and it's your role that makes it possible.Foster a culture of collaboration between business, technology, and data teams. This synergy is the powerhouse that supports the company's goals and your role is crucial in making it happen.Honor Legacy While InnovatingBuild upon the company's historical strengths and mission-based approach while incorporating new technologies.Utilize the company's legacy as a foundation for future growth, leveraging it as a springboard for innovation.Address Technical Debt CollaborativelyNavigate technical debt by promoting a strong partnership between business and technology teams.Develop a strategy to shift from outdated systems to more flexible, cloud-based solutions.Manage Change for AI AdoptionRecognize and address the "last mile problem" in AI projects, including operationalizing solutions and managing organizational change.Engage business partners early to set clear expectations and tackle change management challenges collaboratively.Empower the Workforce with AIInvest in training and tools that allow employees to use AI capabilities in their daily work.Develop a comprehensive AI strategy that involves using existing tools, targeting specific use cases, and exploring innovative solutions.Key Takeaways<p>Combine AI, Data, and Business Strategies. To achieve impactful transformation, it's crucial to tightly integrate your AI and data strategies with your overall business goals. This alignment creates a collaborative environment where AI can effectively contribute to the company's strategic objectives. Emphasize teamwork between business, technology, and data teams to maximize the impact of AI initiatives and address technical debt challenges.</p><p>Embrace an AI-Empowered Workforce. Invest in training and tools that enable employees to leverage AI capabilities in their daily work. Develop a multi-threaded AI strategy that includes defending existing processes, extending targeted use cases, and upending traditional models with innovative solutions. This comprehensive approach ensures broad AI adoption, empowers employees to be more productive, and maximizes organizational impact.</p><p>Prepare for an AI-Driven Future. Recognize that AI is a transformative technology with far-reaching implications. While the full potential of AI may take time to manifest, early adopters are already seeing tangible benefits in specific use cases. To stay competitive, organizations must proactively invest in AI, manage change effectively, and cultivate a culture of continuous learning. Embrace the journey and adapt to the evolving landscape to position your company for long-term success in an AI-powered world.</p>Episode Participants<p>Don Vu is Fortune 100 Chief Data & Analytics Officer with 25+ years of experience leading teams and developing data/AI/ML solutions to drive business outcomes. Currently serving as Chief Data & Analytics Officer at New York Life, overseeing AI & data strategy for the company's $58.5B business.</p><p>Previously, heheld leadership roles at:</p>Northwestern Mutual: Chief Data OfficerWeWork: Led Central Data & Analytics teamMajor League Baseball: VP Data & Analytics, overseeing Analytics OrgMLB Advanced Media/BAMTech: VP Data & Analytics<p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator, known for his deep expertise in the fields of digital transformation, innovation, and leadership. He has presented at industry events around the world and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/generative-ai-and-business-transformation-at-new-york-lifeTue, 09 Jul 2024 08:00:00 -0400CxOTalk RSSHow AI-Driven Disinformation Works (and How to Stop It)
https://www.cxotalk.com/episode/how-ai-driven-disinformation-works-and-how-to-stop-it
<p>In Episode 844 CXOTalk, we explore the growing landscape of disinformation and its impact on businesses and society. Our guests are Patrick Warren and Darren Linvill, co-directors of the Media Forensics Hub at Clemson University and leading researchers on how disinformation spreads, especially on social media. They discuss the latest tactics that bad actors use, from state-sponsored campaigns to individual influencers, and how artificial intelligence is changing the game.</p><p>Warren and Linvill provide insights into the structure of disinformation campaigns, the challenges of detection, and potential technological solutions on the horizon. They also consider the business implications, as companies can become targets or suffer collateral damage.</p><p>By better detecting and understanding these threats' origins, business, and structure, technology leaders can prepare and protect their organizations from AI-driven disinformation and instead use AI’s benefits to keep their organizations safer.</p>Episode HighlightsIncrease defenses against narrative launderingUnderstand how disinformation campaigns layer narratives through multiple sources to increase the credibility of falsehoodsDevelop systems to track and connect disparate content that may be part of coordinated campaignsLeverage AI for both offense and defenseRecognize that AI can be used to create and spread disinformation at a large scale rapidlyInvest in AI-powered detection and mitigation tools to be able to keep up with evolving threatsRethink digital media literacy educationHelp business users learn how to evaluate information sourcesDevelop more nuanced approaches that do not inadvertently fuel conspiracy thinkingPrepare for disinformation threats to businessesRecognize that companies can be targets or collateral damage in disinformation campaigns\Develop strategies to monitor and promptly address reputational threats arising from coordinated attacksBalance authentication and privacy in online spacesExplore technology solutions like blockchain to enable partial verification without compromising user privacyConsider tiered authentication options that allow users to prove specific attributes selectivelyKey Takeaways1. AI Accelerates Disinformation Creation and Spread<p>AI tools have significantly increased the speed and scale at which disinformation can be created and spread. Malicious individuals can quickly generate large volumes of false content, translate it into multiple languages, and distribute it across numerous fake websites and social media accounts. This poses a challenge for human moderators and fact-checkers to effectively handle the influx of AI-generated misinformation.</p>2. Narrative Laundering Obscures Disinformation Origins<p>Disinformation campaigns often use "narrative laundering" techniques to obscure the true source of false information. This involves planting a story in obscure outlets and then amplifying it through diverse sources that are increasingly mainstream. When a narrative reaches major platforms, its dubious origins are hidden, increasing its perceived credibility and impact.</p>3. Businesses Face Growing Disinformation Threats<p>Companies are increasingly becoming targets of collateral damage in disinformation campaigns. Competitors or motivated actors may spread false narratives to damage a brand's reputation or manipulate market perceptions. Even unrelated political disinformation can affect businesses, as seen when NBA-China relations were strained by social media attacks following an executive's comments on Hong Kong.</p>Episode Participants<p>Patrick Warren is an associate professor of economics who has been at Clemson since 2008. Before coming to Clemson, he studied at MIT, earning a Ph.D. in economics (2008), and an undergraduate degree from the University of South Carolina Honors College (BArSc, 2001). His research investigates the operation of organizations in the economy — for-profit and non-profit firms, bureaucracies, political parties and even armies. He has written numerous peer-reviewed articles in top economics and law journals and currently serves as an associate editor of the Public Finance Review. He has served on the board of the Society for Institutional and Organizational Economics, been a visiting associate professor at Northwestern University and a visiting scholar at the RAND Corporation.</p><p>Darren Linvill is an associate professor of communication whose research explores social media disinformation and its influence on civil discourse (in and out of the classroom). He became a faculty member at Clemson after earning degrees from Wake Forest and Clemson and started studying social media in 2010. After becoming an associate professor in 2017, he delved deeper into the truth or falsity of online messaging and its effects. As a sought-after media expert, he’s contributed to many articles and broadcasts by outlets such as the New York Times, the Wall Street Journal, the Washington Post, Bloomberg, Rolling Stone, Inside Higher Ed, The State, CNN, NPR, ABC, NBC, WFAE and others.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator, known for his deep expertise in the fields of digital transformation, innovation, and leadership. He has presented at industry events around the world and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/how-ai-driven-disinformation-works-and-how-to-stop-itMon, 24 Jun 2024 08:00:00 -0400CxOTalk RSSThe AI Imperative: New Rules of Leadership
https://www.cxotalk.com/episode/the-ai-imperative-new-rules-of-leadership
<p>In Episode 843 of CXOTalk, join us for a discussion on artificial intelligence and leadership with two prominent guests. Anthony Scriffignano, a Distinguished Fellow at The Stimson Center and former Chief Scientist at Dun & Bradstreet, brings over four decades of expertise in data science and advanced anomaly detection. Inderpal Bhandari, the founder of Virtual Gold and former Global Chief Data Officer at IBM, shares his experience in leveraging data and AI to drive business value.</p><p>Together, they explore how AI is reshaping leadership roles and the essential skills executives need to thrive, emphasizing the need for a deep understanding of technology and data. Scriffignano and Bhandari highlight the challenges and opportunities presented by generative AI, offering strategies to integrate AI into business processes and decision-making. This episode explains how to understand and overcome the complexities of AI adoption and harness its potential to drive organizational success.</p>Episode HighlightsAdapt to the Changing Nature of WorkRecognize that AI is transforming job roles and workflows; prepare the workforce through training and upskilling.Align new technologies with current business processes to enhance efficiency without disrupting workforce stability.Manage AI Ethics and BiasImplement checks and balances to be sure that AI systems are used ethically and that outputs are free from biases.Regularly review and update AI models to reflect ethical standards and societal values, ensuring transparency in AI operations.Address AI and Workforce ConcernsCommunicate openly with employees about AI implementations and future implications for their roles.Develop clear pathways for employees to transition into new roles or enhance their skills in an AI-driven workplace.Cultivate Data-Driven LeadershipLeaders must develop a thorough understanding of AI and data analytics to drive company strategy effectively.Encourage a culture where decision-making is carried out by data insights to enhance accuracy and strategic outcomes.Understand Investment and ROI in AICarefully evaluate the potential returns on AI investments, considering both direct benefits and indirect impacts on competitive positioning.Balance the focus on immediate ROI with long-term strategic benefits and the potential costs of inaction. Maintain ultimate decision-making authority with experienced executives.Key TakeawaysNavigate Ethical Challenges and Unintended Consequences<p>Implementing AI requires navigating complex ethical challenges and mitigating unintended consequences. Establish governance frameworks to ensure responsible, unbiased AI use and regularly review models for adherence to ethical standards. Leaders must strike a careful balance between pursuing innovation and managing risk.</p>Cultivate Essential Leadership Skills for the AI Era<p>Succeeding in an AI-driven landscape demands new leadership skills. Leaders must become comfortable with ambiguity, develop deep technological understanding, and effectively communicate AI's implications to stakeholders. Encouraging experimentation, inviting dissent, and leveraging diverse perspectives will be crucial for making sound decisions in a rapidly evolving environment.</p>Prepare for AI's Transformative Impact on Work<p>The nature of work itself is fundamentally changing due to AI. Leaders should recognize this shift and proactively adapt by upskilling employees, aligning AI with current processes, and fostering a data-driven, innovative culture. Transparent communication and clear pathways for employee growth in an AI-driven workplace are essential.</p>Episode Participants<p>Anthony Scriffignano, Ph.D. is an internationally recognized data scientist with experience spanning over 40 years in multiple industries and enterprise domains. Scriffignano has extensive background in advanced anomaly detection, computational linguistics and advanced inferential methods, leveraging that background as primary inventor on multiple patents worldwide. He also has extensive experience with various boards and advisory groups.</p><p>Scriffignano was recognized as the U.S. Chief Data Officer of the Year 2018 by the CDO Club, the world's largest community of C-suite digital and data leaders. He is a Distinguished Fellow with The Stimson Center, a nonprofit, nonpartisan Washington, D.C. think tank that aims to enhance international peace and security through analysis and outreach.. He is a member of the OECD Network of Experts on AI working group on implementing Trustworthy AI, focused on benefiting people and planet.</p><p>Inderpal Bhandari is the Founder of Virtual Gold, a Data and AI company. He has served as a director of The AES Corporation, a global energy company, since January 2024. He serves on the Financial Audit Committee and the Innovation and Technology Committee of the Board. Additionally, since September 2022, he has been a member of the Board of Directors at Walgreens Boots Alliance, a global retail pharmacy company, where he serves on the Finance & Technology, the Audit, and the Nominating & Governance committees.</p><p>Dr. Bhandari is the former Global Chief Data Officer of IBM, where he led the company's global data strategy to ensure IBM maintained its leadership as the top AI and hybrid cloud provider for enterprises. His career spans over two decades in healthcare, including roles at Cambia Health Solutions as Senior Vice President and Chief Data Officer, and at Express Scripts / Medco Health Solutions as Vice President of Knowledge Solutions and Chief Data Officer.</p><p>Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator, known for his deep expertise in the fields of digital transformation, innovation, and leadership. He has presented at industry events around the world and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.</p><p>As the founder and host of the widely acclaimed CXOTalk, Michael has interviewed almost 1,000 of the world’s top business leaders, technologists, and academics, providing valuable insights into how organizations can harness technology to drive success and create value. With over three decades of experience, Michael has built a reputation for his ability to distill complex concepts into clear, actionable strategies. His work spans a broad range of industries, including technology, healthcare, finance, and manufacturing.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/the-ai-imperative-new-rules-of-leadershipTue, 11 Jun 2024 08:00:00 -0400CxOTalk RSSVC Insights: Vital Lessons from AI Investing
https://www.cxotalk.com/episode/vc-insights-vital-lessons-from-ai-investing
<p>In episode 841 of CXOTalk, Michael Krigsman speaks with Yousuf Khan, a partner at Ridge Ventures and former multi-time Chief Information Officer, and they discuss the transformative impact of AI on venture capital. They explore how AI is reshaping investment strategies, emphasizing the importance of focusing on real-world applications and managing deployment risks. They address both challenges and opportunities this technological revolution presents for both enterprises and startups.</p><p>Yousuf shares his perspective on evaluating AI startups, highlighting the necessity of sustainable business models and the process of selecting AI models while respecting data privacy. This conversation offers valuable insights for business and technology leaders making their way through rapidly evolving business landscape, providing practical guidance on leveraging AI for long-term success.</p>Episode HighlightsEvaluate AI startups beyond the hypeLook for companies solving real business problems rather than just "wrapper" solutions on top of existing AI models and services.Assess the team's ability to execute long-term and build a sustainable business, not their ability to create an interesting product.Consider the "blast radius" of AI deploymentsRecognize that AI implementations can have far-reaching consequences if they go wrong, affecting brand reputation and customer trust.Take incremental steps and implement proper controls when deploying AI solutions across the enterprise.Focus on specific AI use cases for enterprise adoptionLook for specific, high-impact applications of AI that solve clear business problems.Prioritize operational efficiency improvements and use cases with clear ROI to gain organizational buy-in.Address AI security and data privacy concernsImplement measures to secure AI models and protect sensitive data used in training and deployment.Stay informed about evolving regulations around AI and data sovereignty to ensure continued compliance.Develop change-management skills for AI initiativesClearly articulate the vision and purpose behind AI projects to generate excitement and buy-in.Cultivate the ability to communicate AI's transformative potential, and motivate teams throughout the implementation process.Critical TakeawaysPrioritize Real-World AI Applications<p>AI investments should focus on solving tangible business problems rather than creating superficial "wrapper" solutions. Leaders should carefully evaluate whether AI applications address core issues, such as disease prevention in medical research or demand forecasting in finance, to ensure they provide real and substantial value and are scalable.</p>Manage AI Deployment Risks<p>AI implementations can have significant consequences if not managed properly. Business leaders should take incremental steps and implement robust controls to mitigate risks. This includes ensuring AI models are secure, up-to-date, and managed by skilled professionals to avoid potential negative impacts on brand reputation and customer trust.</p>Invest in Sustainable AI Businesses<p>Not all AI startups are viable long-term investments. Leaders should look for companies with strong teams capable of executing a sustainable business strategy. This involves assessing whether the startup can scale its AI models, maintain cutting-edge technology, and deliver consistent value to customers, rather than just having an interesting and innovative product.</p>Episode Participants<p>Yousuf Khan is a partner at Ridge Ventures, he focuses on early-stage investments in B2B software companies that are disrupting or creating new categories. He also serves as a board member for several portfolio companies, such as Cerby, Lightyear, and Theom. Additionally, he advises leading companies like Zoom, Productiv, and Material Security on their product, go-to-market, and customer success strategies. His mission is to empower and support visionary founders and teams that are building the next generation of enterprise solutions. Previously, Yousuf was the first CIO of Automation Anywhere, the CIO and Vice President of Customer Success at cloud-based AI platform Moveworks, as well as CIO of Pure Storage and Qualys.</p><p>Michael Krigsman is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world’s top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/vc-insights-vital-lessons-from-ai-investingFri, 24 May 2024 08:00:00 -0400CxOTalk RSSSocial Impact: The Philosophy of Zoho CEO Sridhar Vembu
https://www.cxotalk.com/episode/social-impact-the-philosophy-of-zoho-ceo-sridhar-vembu
<p>In Episode 842 of CXOTalk, we explore innovative strategies for business growth and sustainability with Sridhar Vembu, CEO and Founder of Zoho, a global leader in cloud-based business software. Vembu, known for his unique approach to building a global software company, shares his unique perspectives on leveraging rural talent and fostering innovation outside of traditional urban centers.</p><p>Throughout the conversation, Vembu emphasizes the importance of long-term thinking over short-term gains, explaining how this approach has shaped Zoho's success. He talks about the critical role of building a strong company culture based on core values and how this foundation supports sustainable growth. Vembu also shares insights on vertical integration, practical skill development, and adapting to local markets while maintaining global standards. This interview offers actionable advice for business leaders looking to drive innovation, build resilient organizations, and instill positive social impact in today's ever-changing business landscape.</p>Episode HighlightsEmbrace rural development for talent and innovationEstablish offices in rural areas to tap into untapped talent pools, and implement digital infrastructure and connectivity solutions in rural areas to bridge the urban-rural divide.Develop technology-based solutions that address specific challenges faced by rural populations.Prioritize long-term thinking over short-term gainsDevelop strategies that focus on sustainable growth rather than quick profits.Foster a company culture that values patience, continuous improvement, and long-term vision.Invest in vertical integration for greater controlDevelop in-house capabilities across a number of aspects of the business to reduce dependencies on external vendors.Balance the benefits of vertical integration with the need for flexibility and external partnerships.Emphasize practical skills and hands-on learningImplement apprenticeship programs and on-the-job training to develop employees' skills.Encourage a culture of continuous learning and experimentation within the organization.Adapt to local markets while maintaining global standardsTailor products and services to meet specific regional needs and preferences.Maintain consistent quality and core functionality across all markets to ensure brand integrity.Key TakeawaysSustaining Innovation through Financial Independence<p>Zoho has remained private for 28 years, giving the company freedom from external pressures. This allows Zoho to make long-term investments in R&D and employees without the constant pressure for quarterly growth. Leaders can consider alternative funding models that provide more autonomy and the ability to focus on creating long-term value.</p>Transnational Localism for Balanced Growth<p>By establishing operations in rural areas, Zoho aims to create high-value jobs and capabilities outside of traditional tech hubs of major cities. This "transnational localism" approach helps to balance economic development across many regions. Business leaders can explore ways to tap into talent in underserved areas while contributing to local economies.</p>Building Culture through Practice, Not Preaching<p>Rather than formally codifying values, Zoho's culture and philosophy are transmitted organically through leadership example and empowering employees. Leaders aiming to shape company culture should focus on consistently modeling desired behaviors, rather than relying solely on formal policies and metrics.</p>Episode Participants<p>Sridhar Vembu is the co-founder and CEO of Zoho Corp. He is known for his unconventional choices. Sridhar started a product company in India when the service sector was all the rage in the IT sector. In 2005, he began the Zoho University program with six high school students, who were trained for two years in computer science and eventually absorbed in the company. Currently, 15% of Zoho's workforce is made of ZU graduates. Instead of opening new offices in metros, he prefers smaller towns or suburbs. In 2016, the Tenkasi office located in rural India launched Zoho Desk, a product that was developed there.</p><p>Michael Krigsman is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world’s top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.</p>
Leadershiphttps://www.cxotalk.com/episode/social-impact-the-philosophy-of-zoho-ceo-sridhar-vembuTue, 04 Jun 2024 08:00:00 -0400CxOTalk RSSSecuring the Last Mile with an Enterprise Browser: NASCAR Team Hendrick Motorsports
https://www.cxotalk.com/episode/securing-the-last-mile-with-an-enterprise-browser-nascar-team-hendrick-motorsports
<p>In this CXOTalk conversation, Michael Krigsman speaks with Matthew Cochran, Director of Information Technology at Hendrick Motorsports, one of NASCAR's top teams. Cochran shares his experience leveraging data, analytics, machine learning, and innovative tools like the Island Enterprise Browser to gain a competitive edge in professional racing.</p><p>Cochran emphasizes the critical role of real-time data processing and cybersecurity in Hendrick Motorsports' success. He discusses the challenges and opportunities of managing diverse users and systems, and how the IT team collaborates across departments to align technology investments with strategic objectives.</p><p>Cochran's experience demonstrates how to align IT with business goals to succeed with data and technology in highly competitive environments. Watch now and read the transcript to learn more!</p>Episode HighlightsEmbrace a Data-Driven Culture for Competitive AdvantageInvest in tools and technologies that enable real-time data collection, analysis, and visualization to identify performance trends and actionable insights quickly.Utilize machine learning algorithms to process large datasets efficiently, automating tasks like image recognition and competitor analysis to free up human resources for strategic decision-making.Foster Collaboration Between IT and Business UnitsEstablish clear communication channels and invest in resources that translate technical jargon into actionable insights for non-technical personnel, ensuring data-driven insights are effectively communicated and utilized.Align IT initiatives with key business objectives and performance indicators to ensure technology investments directly contribute to achieving organizational goals, such as winning races in the case of Hendrick Motorsports.Prioritize Cloud-First Strategy for Agility and ScalabilityMigrate applications and data to the cloud to enable remote access, collaboration, and scalability to accommodate evolving business needs.Consolidate tools and reduce IT complexity by centralizing data and applications in the cloud, leading to cost savings and improved efficiency.Leverage Zero Trust and Granular Access Controls for Enhanced SecurityImplement a zero trust approach to data and application access by leveraging solutions like the Island Enterprise Browser, granting access on a per-user, per-application basis for enhanced security.Utilize granular controls and auditing capabilities to maintain visibility and control over user access, mitigating potential data breaches or unauthorized access.Prioritize User Productivity and AdoptionEvaluate solutions that seamlessly integrate into existing user workflows, minimizing the need for extensive training and encouraging organic adoption.Incorporate built-in productivity tools, such as password managers, AI assistance, and copy-paste managers, to enhance the user experience and boost efficiency.Continuously Innovate and Embrace Emerging TechnologiesStay informed about the latest advancements in areas like AI, machine learning, and data analytics, and evaluate their potential to enhance your competitive edge.Foster a culture of continuous improvement and experimentation within the IT team and the broader organization to remain agile and adaptable in the face of evolving industry demands.Key TakeawaysEmbrace Data-Driven Decision Making for Competitive Advantage<p>Leveraging real-time data streams and advanced analytics empowers organizations to make informed, rapid decisions that can significantly impact performance and outcomes. Investing in robust data processing and visualization tools allows key stakeholders to quickly consume and act upon insights gleaned from vast datasets. This data-driven approach is crucial for maintaining a competitive edge in today's fast-paced business environment.</p>Implement Granular Security Measures to Protect Sensitive Data and IP<p>Utilizing solutions like the Island Enterprise Browser allows organizations to maintain stringent security standards by providing granular control over endpoint access and implementing comprehensive zero-trust architectures. Adopting advanced security measures, such as watermarking and data leak prevention, is important for safeguarding sensitive information, especially in highly competitive environments where threats may be in close proximity.</p>Foster a Proactive IT Culture to Boost Productivity and Innovation<p>Cultivate an IT culture that embraces innovation and focuses on user-friendly tools, to drive productivity and continuous improvement. By selecting technologies with familiar interfaces and integrated productivity features, organizations can ensure smooth adoption and minimize learning curves. Encouraging IT to say "yes" to new ideas and empowering employees to solve problems fosters a culture of innovation and responsiveness.</p>Episode Participants<p>Matthew Cochran is Director of Information Technology at Hendrick Motorsports, where he brings over 20 years of IT experience to the organization. Cochran joined Hendrick Motorsports in 2001, initially serving as I.T. Manager, he was promoted to Director of IT in January 2022.</p><p>Prior to Hendrick Motorsports, Cochran worked as a Network Manager at Lance, Inc. from 1995 to 2001. He launched his IT career at Collins & Aikman SSD, serving as Store Room & Public Relations Specialist from 1993 to 1995. Cochran earned a Project Management Certificate from Cornell University in 2021 and holds a CCNA certification from Cisco, obtained in March 2023. He has also completed LinkedIn certifications related to virtual collaboration in May 2021.</p><p>Michael Krigsman is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world’s top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.</p>
Data / Analyticshttps://www.cxotalk.com/episode/securing-the-last-mile-with-an-enterprise-browser-nascar-team-hendrick-motorsportsMon, 22 Apr 2024 07:30:00 -0400CxOTalk RSSWhy Enterprise AI Fails and How to Fix It
https://www.cxotalk.com/episode/why-enterprise-ai-fails-and-how-to-fix-it
<p>Artificial intelligence holds immense promise for businesses, but real-world deployments often fall short of expectations. In Episode 840 of CXOTalk, we explore the common reasons why AI projects fail and discuss practical strategies for achieving success. Our guest is Sol Rashidi, author of "Your AI Survival Guide" and a seasoned technology executive who draws on her experience leading data and analytics initiatives at companies like Sony Music and Estée Lauder.</p><p>Join us as we discuss the critical factors that determine AI success, from establishing clear objectives, and ensuring data quality to navigating the complex and occasionally daunting landscape of AI. This conversation provides valuable guidance and advice for business and technology leaders who are looking to navigate the complexities of AI implementation.</p>Episode HighlightsEstablish a Clear AI StrategyDefine whether your organization aims to be AI-centric or to embed AI across specific workflows. This decision will guide the overall direction and resource allocation for AI initiatives.Formulate a comprehensive plan that includes selecting use cases, forming a dedicated team, and designing, and deploying AI projects. Avoid common pitfalls by ensuring alignment with business goals and technical capabilities.Consider Projects’ Criticality and ComplexityPrioritize AI projects based on their criticality and complexity rather than just business value, to avoid internal conflicts and ensure objective decision-making. This approach helps in selecting projects that are truly feasible and impactful.Consider factors such as competitive threats, regulatory requirements, and market consolidation when evaluating AI projects.Engage Leadership and Manage ChangeSecure top-down support for AI initiatives to overcome resistance and ensure alignment with strategic goals. Leadership involvement is crucial for driving AI adoption and managing organizational change.Communicate the benefits and realistic expectations of AI projects to all stakeholders. This helps in managing fears and misconceptions about AI, and helps to foster a culture of collaboration and innovation.Leverage Existing Data InvestmentsMaximize the use of existing enterprise data before seeking new data sources. Many organizations actually underuse their current data assets, which can be a rich source for AI applications.Focus on connecting and integrating existing data to uncover new insights and drive value. This approach is cost-effective and accelerates the deployment of AI solutions.Avoid the “Shiny Object” SyndromeBe cautious of investing in AI projects just because they are trendy. Ensure that the chosen AI solutions are appropriate and necessary for your business needs.Start small with AI projects to test their viability and gain buy-in before scaling up. This helps to ensure that resources are not wasted on unproven technologies.Critical TakeawaysRethink Use-Case Selection for AI Projects<p>Choosing AI use cases based solely on business value is a flawed approach, according to Sol Rashidi. Instead, she advises evaluating use cases based on criticality and complexity, ensuring that projects align with the organization's current capabilities and infrastructure. This method helps avoid the common pitfall of projects getting stuck in "pilot purgatory," and increases the likelihood of successful deployment and scaling.</p>The Importance of Continuous Monitoring in AI Deployments<p>AI projects differ from traditional IT projects because they require ongoing attention and adaptation. Rashidi describes AI as a "live wire" that needs continuous monitoring and human involvement to ensure data accuracy. Company leaders must plan for this ongoing involvement to maintain the effectiveness and reliability of AI applications, post-deployment.</p>Overcoming Organizational Resistance to AI<p>Rashidi highlights the significant resistance AI projects can face within organizations due to fear of redundancy and job loss. She suggests that successful AI implementations require strong top-down leadership and clear communication about the benefits of AI. Engaging stakeholders early and addressing their concerns can help mitigate resistance and create a culture that embraces technological change.</p>Episode Participants<p>Sol Rashidi currently holds 7 patents, with 21 filed in the Data & Analytics space and is a keynote speaker at several technology conferences speaking on various topics such as Machine Learning, Data & Analytics, and Emerging Operating Models for organizations taking on transformations in the D&A space. Prior to joining Estee Lauder as their Chief Analytics Officer, Sol was the Chief Data & Analytics Officer for Merck, EVP and CDO for Sony Music, and Chief Data & Cognitive officer for Royal Caribbean.</p><p>Michael Krigsman is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world’s top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/why-enterprise-ai-fails-and-how-to-fix-itMon, 13 May 2024 13:00:00 -0400CxOTalk RSSThe Data Value Chain: Key to Successful AI
https://www.cxotalk.com/episode/the-data-value-chain-key-to-successful-ai
<p>In this CXOTalk conversation, Mike Capone, the CEO of Qlik, describes the relationship between data and successful AI initiatives. He explains that to harness the power of artificial intelligence, organizations must understand how AI and data value chains intertwine. However, curating high-quality data and building trust in AI through effective governance remains a challenge for many.</p><p>Capone emphasizes the need for a strategic approach, aligning data and AI efforts directly with core business objectives. Cultivating a data-driven culture and navigating organizational anxieties through thoughtful change management are also crucial. Finally, he offers guidance for leaders in both business and technology, stressing the importance of adapting to the rapid evolution of AI.</p>Episode HighlightsEmbrace the AI Value ChainUnderstand the end-to-end process of capturing, curating, and preparing data for AI and analyticsRecognize that data is the crucial foundation and that AI value is derived from the underlying dataPrioritize the Data Value ChainFocus first on harnessing data and understanding where it resides in real-timeEnsure the data value chain is robust before embarking on AI initiativesDevelop a Data-Driven StrategyStart by identifying key business decisions that can be improved with data and AIWork backwards to determine necessary data and ensure it is high-quality and curatedFoster Trust and Data LiteracyDeploy AI responsibly, starting with trusted, validated data to earn trust in the outcomesInvest in data literacy training to help employees become comfortable working with dataImplement Responsible AI PracticesTake a leadership role in establishing responsible AI deployment practicesConsider the ethical implications of how customer data and technologies like facial recognition are usedApproach AI Adoption StrategicallyTreat AI as a business challenge rather than just a technology problemPrioritize change management and cultural impacts before rushing to deploy AI technologiesKey TakeawaysThe AI Value Chain Depends on High-Quality Data<p>AI success requires a robust data foundation. Organizations must capture, curate, and catalog high-quality data in real-time to feed AI and analytics systems. Focus on data veracity and lineage to ensure AI delivers accurate, trustworthy results.</p>Align Data and AI Strategies with Business Priorities<p>Start by identifying key business decisions that can benefit from data and AI. Work backwards to determine the data needed to support those decisions. Develop cohesive data and AI strategies that align with overall business goals and be sure to gain full executive support.</p>Balance Technology and Culture for Successful AI Adoption<p>Implementing AI requires more than just deploying technology. Overcoming cultural resistance is critical. Invest time in change management, building data literacy, and earning trust in AI systems. Establish governance frameworks that address both technical and ethical considerations.</p>Episode Participants<p>Mike Capone leads Qlik's mission to create a data-literate world where people, businesses, organizations and governments tackle their most complex challenges with data. Mike has direct experience using data power through analytics to transform companies and entire industries.</p><p>In addition to his extensive experience at high-growth SaaS companies, Mike was most recently COO of Medidata Solutions, a publicly traded company providing SaaS analytics solutions to the pharmaceutical, healthcare, and human sciences markets. At Medidata, he played a significant role in product development, data science, professional services, and go-to-market operations, guiding the company's strategy to deliver a comprehensive cloud platform that leverages data and analytics to transform clinical trials.</p><p>Prior to Medidata, Mike held senior leadership roles at ADP, including Corporate Vice President of Product Development and CIO, as well as Senior Vice President and General Manager of ADP's global outsourcing business. He was also head of product development and technology operations at ADP, one of the world's largest B2B cloud service providers, offering critical services to more than 600,000 companies and 39 million employees worldwide.</p><p>Michael Krigsman is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world’s top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.</p>
Artificial Intelligencehttps://www.cxotalk.com/episode/the-data-value-chain-key-to-successful-aiTue, 16 Apr 2024 13:00:00 -0400CxOTalk RSSCISO Insights: Why Use an Enterprise Browser?
https://www.cxotalk.com/episode/ciso-insights-why-use-an-enterprise-browser
<p>Healthcare organizations face unique challenges in protecting sensitive patient data while delivering efficient and accessible care. In CXOTalk episode 839, we explore these challenges with Bill Dougherty, Chief Information Security Officer of Omada Health, a virtual-first healthcare company specializing in chronic disease prevention and management.</p><p>Dougherty discusses Omada Health's approach to security, emphasizing the importance of building trust with patients and integrating security into the company's brand identity. He also shares insights into how the organization leverages technology to enhance security, optimize operations, and reduce costs by 50 percent by using the Island enterprise browser to simplify IT management and provide a consistent user experience.</p>Episode HighlightsEmbrace Security as a Brand DifferentiatorPrioritize trust: Building trust with customers and partners is crucial in healthcare due to the sensitive nature of data. Integrate security into your brand identity to demonstrate commitment to data protection.Make security a core value: Foster a culture of security awareness throughout the organization, from leadership to individual contributors. Encourage open communication about security risks and best practices.Navigate the Complexities of Healthcare RegulationsInvest in compliance expertise: Healthcare regulations are intricate and constantly evolving. Build a team with deep knowledge of HIPAA, GDPR, and other relevant regulations to ensure compliance and avoid costly penalties.Streamline vendor management: Implement robust third-party risk management processes to assess the security posture of vendors handling sensitive data. Negotiate strong data protection agreements and conduct regular audits.Leverage Threat Modeling for Proactive Risk ManagementAdopt a tailored threat model: Develop or adapt a threat model specific to your organization's unique needs and regulatory environment. Consider factors like data sensitivity, system criticality, and potential attack vectors.Integrate threat modeling into workflows: Incorporate threat modeling into the development lifecycle, vendor selection process, and ongoing risk assessments. This proactive approach helps identify and mitigate potential threats before they materialize.Optimize Security and User Experience with Enterprise BrowsersConsolidate and control: Transition from managing multiple consumer browsers to a single, purpose-built enterprise browser. This simplifies IT administration, reduces security risks associated with browser extensions, and provides a consistent user experience.Centralize policy enforcement: Leverage the enterprise browser as a central point of control for security policies and access management. Integrate with your identity provider (IDP) to enforce multi-factor authentication and ensure secure access to applications.Drive Efficiency and Cost Savings Through ConsolidationIdentify redundant tools: Regularly assess your software portfolio and identify opportunities to consolidate overlapping tools and eliminate underutilized applications. Leverage usage data from your enterprise browser and other sources to inform decisions.Evaluate cost-saving alternatives: Explore solutions that offer multiple functionalities within a single platform. This reduces licensing costs, streamlines vendor management, and simplifies operations, leading to greater efficiency and cost savings.Key TakeawaysSecurity as a Brand Differentiator in Healthcare<p>Bill Dougherty highlights the unique challenge of building trust in digital healthcare, where patient data privacy is paramount. He emphasizes that Omada Health views security not just as a technical necessity but as a core element of its brand identity. This approach is particularly important in healthcare because trust directly impacts patient engagement and health outcomes. Building a strong security posture and communicating it effectively can therefore be a key differentiator in the market.</p>Enterprise Browsers: Simplifying Security and User Experience<p>Dougherty advocates for the adoption of enterprise browsers as a means to simplify IT management and enhance security. By consolidating multiple consumer browsers into a single, controlled environment, Omada Health reduces security risks associated with managing different platforms and extensions. Additionally, the enterprise browser provides a consistent user experience across the organization, addressing challenges related to browser inconsistencies and user preferences.</p>Data-Driven Cost Optimization through Consolidation<p>Omada Health leverages its enterprise browser to gain insights into software usage, enabling strategic decisions about tool consolidation and license management. By identifying underutilized applications and overlapping functionalities, the company has achieved significant cost savings and improved operational efficiency. This data-driven approach allows for continuous evaluation and optimization of the IT landscape, ensuring that technology investments align with actual usage and business needs.</p>Episode participants<p>Bill Dougherty is CISO of Omada Health He has over 20 years of experience protecting and overseeing information security, information technology (IT) operations, and managed services for a host of technology companies. A self-proclaimed “tech geek,” he helped lead Omada through its hyper-growth phase by shaping all aspects of internal IT, end-user support, vendor management, operational security and compliance.</p><p>Michael Krigsman is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world’s top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.</p>
Securityhttps://www.cxotalk.com/episode/ciso-insights-why-use-an-enterprise-browserMon, 13 May 2024 08:00:00 -0400CxOTalk RSS