MachineCon 2026

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CIOs are actively wrestling with whether to build purpose-built AI models in-house, buy off-the-shelf solutions, or borrow capabilities through managed services a decision that has major implications for cost, control, and long-term vendor dependency.
What’s the right sourcing model for AI in your enterprise and how do you avoid locking yourself in?

Every enterprise has AI on the roadmap. Few have it in production. Moving from idea to something that actually works, across real data, real workflows, real systems, is where most initiatives stall.
In this session, Chris Slovak draws on experience across some of the world’s largest AI deployments to cut through the noise. What does a responsible, scalable AI transformation actually look like? How do you sequence use cases, manage risk, and build a foundation that compounds over time rather than creating technical debt? And how do you keep governance and speed from working against each other?
A practical session for leaders who are done waiting on AI to deliver.

AI budgets are growing, but “we saved some hours” no longer cuts it in the boardroom. CIOs are being pushed to show hard numbers lower costs, new revenue, faster outcomes not just efficiency wins.
What does a credible AI business case actually look like? And how are CIOs having that conversation with their CFO and board

AI is no longer an innovation topic; it is a governance issue and autonomous teams without orchestration architecture create organized chaos.
Panel angle: Who owns AI governance in your organization – the CIO, the board, or legal? And what does a governance structure that actually works look like day-to-day?