Every enterprise has an AI strategy. Few have AI that works reliably at scale. The gap isn't the model but it's the data underneath it. In this session, we'll explore why the majority of enterprise AI initiatives stall between pilot and production, what the most successful AI-at-scale organizations do differently, and why the next frontier of competitive advantage isn't building smarter models, it's building the data foundation those models can trust.

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. Panel angle: What does a credible AI business case actually look like? And how are CIOs having that conversation with their CFO and board

While enterprise AI promises unprecedented efficiency, up to 80% of projects fail due to LLM hallucinations, "black box" obscurity, and a lack of proprietary context—risks that FinTech, Pharma, and Manufacturing leaders simply cannot afford. Knowledge graphs bridge this gap by establishing a trustworthy, explainable semantic layer that transforms siloed enterprise data into connected, agent-readable context. By integrating Graph Retrieval-Augmented Generation (GraphRAG), Model Context Protocol (MCP), and Agent Memory Graph tools, this knowledge graph-based approach improves the accuracy of LLM responses while providing an ironclad, fully auditable map for autonomous AI agents to navigate. A well-constructed semantic layer ultimately helps organizations de-risk their AI investments, delivering highly secure, governable, and accurate generative AI solutions with unmatched speed to value.

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?

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

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.

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?

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