6 Lessons for Companies Wanting to Compete in the Data & AI Era

I may have entered the world of IT a bit late when it comes to hardware knowledge but I’ve always felt that I joined the IT field at exactly the right time for everything related to data, governance, and AI. These have been my main battle horses. And without diminishing the importance of foundational IT topics like hardware, networking, servers, and data centers, I truly believe that the future of companies lies in their ability to interconnect their environments so that every application in a company’s tech landscape can speak the same language: data.

Moreover, that data language must be reachable by public AI models. Private cloud and private AI implementations can take you only so far—unless your business case requires strict privacy and compliance—because true strategic advantage comes from combining clean, governed internal data with the capabilities of modern AI. When done well, this combination allows companies to uncover the hidden “golden nuggets” that drive business decisions, trends, and organizational knowledge.

Large-scale company initiatives, such as implementing a scalable and futureproof data strategy, must have the blessing and support of top management. Ideally, the initiative should originate from management. User adoption is a huge challenge in the IT world, and not every Head of IT can carry the criticism, pressure, and resistance alone for extended periods. That is one of the main reasons why so few implementations, such as enterprise-wide data platforms, are executed seriously. These efforts are often seen as “projects of the future,” while urgent operational matters tend to dominate the present. IT is too often measured by its ability to implement quick technological changes that support halfdeveloped processes—processes that will later change, rendering IT’s earlier work obsolete.

It should not be seen as a commodity. Many companies already outsource the operational side of it. Instead, organizations should invest heavily in documenting their processes, digitizing them, and implementing them in reliable enterprise systems that can talk to each other with as little friction as possible. These systems should then be connected to the company’s most important data sources through an almost frictionless data platform so that time to value is reduced. Those early data achievements will spark stakeholder support for the broader strategy.

Once a data strategy gains stakeholder buyin, the very first request usually arrives: “I need a dashboard for this process.”

This is where things get tricky. The processes stakeholders want dashboards for are often not documented, not stable, or not implemented in a way that produces the necessary data for the KPIs they expect.

One mistake I made early in my role as Head of IT was trying to document each department’s processes myself and identifying the data needed for their dashboards. I ended up investing too much of my time there and since I’m not a supply chain or finance expert, I couldn’t drive deep changes.

So instead, I decided to make IT the example for everything new. I built all the BI dashboards from the IT department first, and showing them to stakeholders sparked a real desire (combined with their own need for better processes) to work on and improve their areas.

If at this point in my career someone asked me: “What should a company do to gain competitive advantage in today’s Data and AI era?” I would say: