Live C-Suite Conversations on AI

Banks run dozens of AI pilots, but almost no AI agents in production, and the gap is in controls, not models. Former UBS Group CIO Oliver Bussmann outlines the key conditions that must be met before agents can go live.

AI is moving from pilot projects to the center of how large companies operate, and CIOs own the transition. IBM Consulting's Andy Baldwin shares practical advice for the next 12 months.

Most companies, including AI startups, lose their way as they grow, trading their founding mission for short-term profit, says Eric Ries, author of The Lean Startup. He explains why this corruption happens and shares the governance structures that keep a company loyal to its purpose.

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.