As AI becomes embedded in core business processes, executive conversations are shifting toward sustainability. For technology leaders, the question is no longer whether AI can deliver value, but whether the organization is structured to support it over time.
I sat with dale williamsonChief Technology Officer for EMEA, to find out what separates companies that are really serious about AI from those that are just experimenting.
What emerged was an operating-model view of enterprise AI, highlighting that structure and ownership matter more than technology choices.
Signs that an organization is serious about AI
Katherine Brown: When you go to an organization that is seriously investing in AI, what’s the first thing you look at in their operating model and platform choices?
Dale Williamson: Most organizations today believe that AI is real and that it matters. What matters is how clearly that commitment is reflected in the operating model.
The first thing I look at is ownership. Who owns the data and who owns the AI, and how close that ownership is to the CEO. If data and AI are owned directly by or near the CEO, this indicates a high level of strategic importance. Most often, ownership occurs at multiple levels, and in many cases data and AI are owned by completely different groups.
When AI is structurally disconnected from data, the result is stagnant use cases and fragmented experiences. But the world in which organizations operate is dynamic. Traffic changes. Markets change. Supply chains fluctuate. If data and AI are separated, it becomes very difficult to respond to that reality in real time.
The second thing I look at is whether the organization has an inventory of its data assets. While financial and physical assets are well documented, many organizations are still maturing in how they catalog and understand data assets. In many cases, organizations are not fully aware of what data they have, where it resides, or how valuable it may be.
A third indicator is how broadly the organization defines the data. Many people still think of data primarily as structured tables or logs. But images, emails, collaboration tools (documents, spreadsheets), and code all contain rich operational insights. Organizations that expand their definition of data unlock far greater value over time.
Why does the proximity between data and AI matter?
Katherine: Everything you described is based on how closely data and AI are linked. Why does that proximity matter so much in practice?
Dell: When data and AI work on the same foundation, organizations can support more dynamic use cases. When they are separated, the AI becomes dependent on slower, more stable inputs.
Traditional governance and catalog tools are very effective at managing structured data, but they struggle with unstructured, rapidly changing sources. This is one reason why it is difficult to expand the scope of data governance, and why comprehensive data inventories are still rare.
If you’re trying to solve problems like liquidity modeling, credit risk, or supply chain resiliency, you need AI working directly with timely, constantly updated data. Otherwise, decisions are always delayed, and insights come after the moment when they are most useful.
Katherine: How are leading companies creating connections between central teams and the business?
Dell: The leader responsible for data and AI needs a seat at the executive table, and they need a deep understanding of how these systems actually work. AI behaves differently than traditional software, and organizations benefit when leadership reflects that reality.
When it comes to tooling, leading companies resist the temptation to rely exclusively on AI features embedded in dozens of SaaS tools. Although those tools can improve individual productivity, they rarely help teams work cohesively across tasks. Over time, that approach reinforces existing inconsistencies in definitions, metrics, and processes.
At the same time, these organizations are rethinking the build-versus-buy equation. Their goal is not to build everything in-house, but they also avoid excessive lock-in. Portability, transparency, and control over data and AI assets are becoming increasingly important.
The winning organizations also manage their AI initiatives as a portfolio. Not every project is successful. Something needs to be stopped. Others guarantee additional investment. Treating AI as a portfolio of bets rather than a linear roadmap allows organizations to adapt as technology and business conditions evolve.
What the enterprise AI operating model looks like in three years
Katherine: Looking ahead, how do you expect enterprise AI operating models to change over the next three years?
Dell: Most organizations will still be in some stage of transformation, but one of the biggest changes will be reducing the traditional separation between IT and business. Business teams will become more technically adept, and technical teams will become more closely aligned with business outcomes. This change is already underway and will continue.
As a result, the shape and form of IT organizations is likely to change. Historically, IT has focused on risk management, governance, and operational complexity. AI is becoming increasingly effective in those areas, especially in cybersecurity, IT support, and compliance.
When organizations also reduce legacy complexity and move away from siled vendor ecosystems, operating models begin to change more fundamentally. Teams are defined less by the systems they use and more by the results they deliver.
Over time, this may lead to the creation of entirely new units focused on lean organization or new forms of value creation. Exactly how this is implemented will vary by company.
How skills and roles evolve in the AI-powered enterprise
Katherine: This type of operating model shift has a big impact on talent. For our closing question, how do you see skills and roles evolving?
Dell: Many IT organizations will continue to shrink, primarily because much enterprise technology is still built on decades-old systems that are expensive to maintain. At the same time, the software development lifecycle is changing. Tasks that once required the most effort, such as manual coding, are increasingly being assisted by AI. Now more time is spent on assessment, behavior testing, reining in and ongoing monitoring.
This transformation brings business and technical teams closer together. Business teams become more involved in defining and validating behavior. Technical teams focus more on results, reliability and governance. New roles are emerging around observability, orchestration, and system inspection. These roles often blend technical, operational and organizational skills, and they do not always come from a traditional engineering background.
Management itself is also evolving. As AI takes on more administrative tasks, management turns back to analysis, decisions, and improving work flow. Critical thinking becomes necessary. Those who are comfortable experimenting, learning and adapting will do well. And as organizations drive this transformation, analytical and scientific mindsets will become increasingly valuable.
closing thoughts
Enterprise AI readiness is ultimately an operating model decision. Leaders who are making sustained progress have clear executive ownership of data and AI, treat data as a known and governed asset, and ensure that AI works with real-time, shared data directly rather than through fragmented handoffs. They manage AI initiatives as portfolios, not pipelines, with the discipline to decide where to invest, pause or pause. And they organize teams around assessment, oversight, and results rather than tools or projects. The organizations that succeed will not be those that most accurately predict the future of AI, but rather those that are ready to adapt to change.
To learn more about building an effective operating model, download the Databricks AI Maturity Model.
