LIVE: C-Suite Conversations on AI | CXOTalk

Growth, outside capital, and the race to monetize all pull AI companies toward easy money. Eric Ries on whether a company can resist that pull and stay true to its mission as it scales.

Although companies spend heavily on AI, few capture its value, according to McKinsey QuantumBlack. Their global leader explains why and shares a repeatable process for enterprise and agentic AI.

Box CEO Aaron Levie joins CXOTalk episode 921 to explain why enterprise AI agents stall in production, how they break existing security models, and what CIOs should fix first.

AI agents have become essential enterprise tools. The platforms companies choose now will decide whether they own their AI or depend on big tech. Mozilla CTO Raffi Krikorian argues that open-source AI agents are the key to real independence.Key points:AI agents act inside your systems, and most enterprises have limited visibility into whose interests they serveBuilding on closed, proprietary platforms means the vendor controls your agent's behavior, data access, and roadmapKrikorian explains the open-source alternative and what enterprise control of AI actually requires 

Agentic AI is changing what CIOs are accountable for. Systems that plan, act, and call tools on their own now operate within workflows the CIO no longer fully owns, while boards expect the same standards of security, risk management, and business value. CXOtalk episode 919 turns that pressure into a practical playbook for leading agentic AI in the enterprise.Key points:The CIO mandate shifts from running systems to governing autonomy, with clear decision rights, agent boundaries, and accountability.Trust, data, and control must be managed together, especially when the middle layer of models, agents, and vendors is opaque, and shadow AI is already inside the business.Human oversight must be designed for machine speed, with explicit roles before, during, and after AI operates, backed by an operating model and culture built for continuous change.

AI-driven autonomous software development is transitioning from pilot projects to real production deployments in 2026. CIOs managing large developer teams must decide which tasks to delegate to agentic platforms and the speed of this transition. CXOTalk episode 918 examines how Mexico's largest insurer made those decisions, including pilot design, measured velocity gains, changes to the developer role, and the governance inputs required to keep autonomous output aligned with enterprise compliance standards.Key PointsTest Autonomy Across Mixed Use Cases First. GNP structured their pilot around four concrete scenarios: backend language upgrade, frontend framework migration, new feature builds, and security vulnerability remediation, using live repository and CI/CD connections.Move Guardrails into the Prompt Layer. Treat technical standards, security policies, and test requirements as prompt inputs alongside functional specifications, so the platform produces code that meets corporate guidelines by design.Redefine Developer Roles Around Direction, Not Code. Shift engineers from line-by-line coding into prompt authorship, architecture review, and validation of autonomous output, with co-pilots handling any residual work.

In 2026, insurance technology leaders face an important question: can autonomous development meet the AI determinism, auditability, and quality standards that regulated industries require? This conversation examines how a multinational insurer rebuilds its software development life cycle around AI, covering context engineering, test-driven development, throughput optimization, and the shifting bottlenecks that surface as code generation accelerates.Key PointsRegulated Industries Require Deterministic Code from AI. Regulated insurers need deterministic, auditable code rather than probabilistic output, which shapes vendor choice, context engineering from codebases and standards, and test-driven development.Optimize Throughput, Not Local Efficiency. Accelerating one SDLC stage exposes new limiting factors downstream. Treat requirements, code generation, review, testing, and release as one integrated, measured pipeline rather than isolated wins.Instrument AI Spend Against Actual Business Outcomes. Track velocity, quality, and end-to-end throughput against AI investment, so spend ties to faster product delivery and customer value, not isolated gains.

Every enterprise software decision in 2026 now runs through the same question: what does agentic AI actually do to my stack, my costs, and my vendors? We explore how agents deliver production value, which SaaS categories are genuinely exposed, and ideas for CIOs on the coming shakeout.Key points in this episode:Develop Agent Governance Before You ScaleTreat agents as software with authority to act, but define data access, approval rights, evaluation tests, security limits, and step-by-step monitoring before broad deployment.Match Autonomy to the WorkStart with bounded workflows that have clear inputs, established rules, and measurable outputs, such as coding, customer support, financial reconciliation, or search, and retain human approval for regulated or ambiguous work.Manage AI Spending Based on Capacity and BudgetTrack tokens, API calls, and tool use against business outcomes; then fund agents based on priority and ROI rather than letting every team consume budget on open-ended experimentation.