How CIOs Are Strengthening Data And AI Foundations

What CIOs are working on, Part 2 of 4

In Part 1 of this series, I focused on core modernization. In this post, I turn to the data and AI initiatives that emerged from those same 30-plus conversations with CIOs and CTOs.

Many organizations can point to their data lakes and warehouses but struggle to answer basic questions: Who owns customer data? Where is the authoritative revenue number? Which teams can access what, and why? Core systems generate valuable data, but it is often used in one-off projects, with limited reuse, unclear ownership, and weak literacy outside specialist teams. At the same time, AI interest is high: Leaders want to apply predictive, generative, and even agentic AI but worry about trust, compliance, skills, and architectural impact.

From guidance kickoff conversations with CIOs and CTOs, three initiative patterns stood out on data and AI:

Build shared data platforms that operate as a backbone. Several organizations are investing in cloud data platforms that become the structural spine for analytics and AI. A European insurer has completed the migration of its on-premises data warehouse to Azure Synapse and now focuses on the “data backbone” that moves data from operational systems into this platform in a repeatable way. A public insurer in Australia runs its data platform on AWS and is raising expectations for secure data sharing across partners and internal teams so that early disease detection models have richer signals to work with. An asset manager in Southeast Asia is at capacity on a seven-year-old enterprise data warehouse and is weighing a cloud-based replacement to create an AI-ready platform while meeting data sovereignty obligations.
Adopt data as a product and invest in literacy and governance. Becoming more data-driven shows up repeatedly as a change in mindset and responsibilities. A health insurer is moving from project-based data work to a data-as-a-product model. Domains such as marketing, healthcare partnerships, HR, and finance are starting to own specific data products. A European consumer organization with a modern Snowflake and customer data platform stack is revisiting data governance and team structure to reduce internal friction, clarify decision rights, and secure commercial data as it introduces genAI-powered search and new digital services. In these conversations, CIOs describe data as a product and cultural asset, with governance and literacy treated as enablement for AI rather than compliance overhead.
Create AI enablement programs with clear guardrails and internal focus. Most leaders I speak with already have pilots or proofs of value in generative and/or agentic AI. The work now is to turn scattered initiatives into an enterprise program. A health insurer has run proofs of value in call transcription and real-time chat assistance and now wants guidance on pragmatic use cases, agentic AI implications for enterprise architecture, and a roadmap that keeps employees at the center. A major bank is embedding AI into its software development lifecycle, using tools for specification-driven development, code generation, and automation of “keep the lights on” activities while managing cultural concerns about skill erosion and overreliance on automation. Across these examples, CIOs are putting in place guardrails, platforms, and learning programs that make AI safe, valuable, and repeatable before they scale it across customer-facing journeys.

In the next part of this series, I’ll turn to the operating model side of the story and look at how CIOs are evolving team structures, architecture roles, and governance to support this broader transformation agenda.