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Get Your AI Strategy Out of the Drawer

There’s a familiar pattern in many organizations, and AI strategies are no exception: ambitions and principles are defined, the strategy is approved and presented—and then day-to-day work continues much as before.

That’s exactly the trap Darize consultant Inge Bograd works to avoid. Here, she shares practical lessons from working with the Danish Environmental Portal (DMP), where the AI strategy has been turned into clear priorities, everyday practices, and tangible value. She also offers four recommendations for moving an AI strategy off the shelf and into day-to-day decision-making.

October 6, 2026 · Jacob Mygind

  • AI and Data

Lessons from the Danish Environmental Portal: Turning AI Strategy Into a Practical Management Tool

“One of the biggest risks when developing a strategy is that it never really becomes part of the organization’s mindset.”

Inge Bograd has seen this happen time and again. That’s why she is proud of how the Danish Environmental Portal (DMP) has brought its AI strategy to life.

“At DMP, we’ve successfully made the strategy operational and integrated it into everyday work. It’s no longer just a document—it’s an active management tool. We’ve connected it to virtually everything DMP does,” she says.

Why Do You Need an AI Strategy?

But why have an AI strategy at all?

“In some ways, it’s a little strange that we need a dedicated AI strategy. After all, AI is ‘just’ another technology. But it’s new, and it’s evolving incredibly fast,” Inge says.

According to Inge, organizations that want to approach AI professionally need a clear strategic direction:

“Do we know what we want to use AI for? Do we understand our risk appetite? And how should AI work with the systems, processes, and technologies the organization already has?”

Without those answers, AI initiatives risk being driven by what the technology can do rather than by the problems the organization actually needs to solve. The result can be a collection of disconnected initiatives with no shared direction.

How DMP Makes Its AI Strategy Operational

At DMP, the AI strategy has been designed as a management system with clear governance.

“The foundation of DMP’s AI strategy is a set of principles that serve as both a moral and operational compass for responsible AI. They act as a filter for deciding what we should pursue,” Inge explains.

The strategy is also closely connected to DMP’s overall business strategy. That keeps AI initiatives from operating in isolation and ensures they directly support business goals.

In practice, DMP translates the strategy into action through a structured decision-making process. Every AI initiative is evaluated based on factors including the business value it can deliver. That applies to major projects as well as smaller initiatives.

Each initiative also goes through a standardized risk assessment. At a minimum, it is assessed twice: before work begins and again before anything goes into production.

“DMP has a dedicated AI domain group responsible for coordinating, evaluating, and following up on AI activities across the organization—without taking ownership away from the individual development projects. Instead, the group serves as a cross-functional governance and advisory forum. It provides visibility across initiatives, ensures alignment, and brings in leadership when decisions need to be made,” Inge says.

“The domain group exists to make sure AI doesn’t become a universal tool that we try to use for everything simply because the technology makes it possible.”

A Good Strategy Is Never Finished

One of DMP’s most practical approaches is the direct connection between strategy and backlog.

Rather than allowing the strategy to exist separately from day-to-day work, DMP has integrated its long-term objectives directly into the backlog. This means tasks and activities are prioritized not only by what is urgent today, but also by what will actually move the organization in the desired direction.

“It allows us to look at the bigger picture. If one strategic objective accounts for a disproportionate share of our activities while another is barely being addressed, that tells us we need to actively identify initiatives that restore the balance. In other words, the strategy isn’t just a document we refer to at the beginning of the year. It’s visible in the work we do every day,” Inge says.

DMP’s strategy is not static, either. It is formally reviewed at least once a year, but in practice, adjustments are made continuously.

Some elements are moved from the strategy itself into action plans because AI is evolving so quickly that detailed plans can become outdated. The underlying direction can remain relevant even when the path to achieving it needs to change as technology and circumstances evolve.

When Is an Organization “Ready” for an AI Strategy?

Not every organization is ready for a complete AI strategy on day one—and it doesn’t need to be.

According to Inge, what matters is not when the strategy is written, but whether it works. Are people actually using it? Does it shape how they think? Does it help them make better decisions?

“DMP started working with AI before it had a finished strategy. Initially, the focus was on being willing to take risks, experiment, and get started. The strategy came later—once the organization had enough experience to make it both realistic and useful.”

Inge rejects the idea that organizations should develop a complete strategy first and only then begin working with AI as a purely top-down exercise.

“My view is that an organization’s maturity should determine how top-down the approach needs to be. AI is evolving so quickly that strategy development will always need an exploratory element. The strategy has to translate into priorities and operations while the organization continues to learn,” Inge concludes.

Ultimately, that is what the strategy is designed to achieve: not AI for AI’s sake, but AI that moves the organization in the right direction—across its work, day by day and decision by decision.

Inge’s 4 Recommendations for Making AI Strategy Work in Practice

Based on her experience with DMP and other organizations, Inge highlights four ways to turn an AI strategy into an operational tool:

  1. Build the strategy into a clear governance and decision-making process

    An AI strategy only becomes operational when people use it to make decisions. Evaluate every AI initiative—large or small—based on tangible business value, risk, and strategic relevance.

    Use a consistent risk assessment framework and assess initiatives at least twice: before they begin and again before they go into production. A cross-functional forum or dedicated group can coordinate activities, maintain visibility across initiatives, and involve leadership when decisions are needed—without taking ownership away from individual projects.

    2. Connect the strategy directly to day-to-day priorities

    Integrate strategic objectives where work is actually prioritized, such as the backlog or portfolio management process.

    This ensures that priorities are based not only on what is urgent today, but also on what will move the organization toward its strategic goals. It also makes imbalances visible. If one strategic objective dominates the organization’s activities while others receive little attention, that is a signal to actively identify initiatives that restore the balance.

    3. Treat the strategy as a living framework

    An AI strategy should never be static. Review it formally at least once a year, but continue to adjust it as technology, opportunities, and circumstances evolve.

    Keep the strategy focused on direction and principles, and move more detailed elements into action plans that can evolve as conditions change.

    4. Let organizational maturity set the pace—and start by exploring

    You don’t need a finished AI strategy on day one. It may make more sense to start by experimenting, testing ideas, and building experience before defining a strategy that is both realistic and useful.

    What matters is not when the strategy is written. What matters is whether people use it to think more clearly, set better priorities, and make better decisions.