"AI can solve everything!" (And that's the problem)

Invest a lot of time and money before there is something tangible? With AI, that is a thing of the past. Claude, Copilot, GitHub: with today's tools, you can tackle almost any problem at lightning speed.

Good news? To a certain extent, yes. After all, if you can solve every challenge, you can go full steam ahead... in theory.

The enthusiasm around AI creates new problems in practice. Because where do you start when the possibilities are endless? And how do you know if what you build actually contributes to your goals?

'AI sprawl': everyone is building... everywhere at once

On an individual level, employees are experimenting with AI. The goal: to increase personal productivity. In many cases, this works quite well. With a Claude or Copilot license, you usually get tasks done faster and work more efficiently. But... dozens of colleagues do that simultaneously. And they all use their own approach. This creates something that worries many organizations: 'AI sprawl'.

Meanwhile, at the team level, AI is touching the processes. There is a lot involved. Think of the customer experience, your data sources and your data platform. Organizations like to record processes in their Microsoft 365 environment, so that they can make the right links and maintain an overview. But in many organizations, developments in the field of AI are going much faster than expected. As a result, good agreements and governance lag behind reality.

"Why doesn't AI add value?"

Because everyone uses all kinds of tools to build a multitude of solutions, you lose track as an organization. You draw from a fast-growing number of data sources and the (old, adapted and new) processes pile up. Soon you have a long range of solutions. But the framework is missing.

That framework consists of your strategic priorities. If you haven't made them clear, you may be solving a lot of problems that your organization doesn't need to tackle in order to grow. And the lack of overview also causes you a lot of governance problems.

Within many organizational walls, the question now arises: "Why doesn't AI add value while we use it frequently?"

And usually the answer is: you currently lack a strategy.

A strategy starts with the right questions

Remember that everything you create with an AI tool is just noise, unless you test it against your own goals. After all, you determine what your source of truth is.

The first step you want to take is to map out your processes and KPIs. From A to Z. Only when you have that overview do you understand where AI adds or can add value.

Start by answering some sharp questions:

Employee level

• Is this a priority or just a possibility?
  'To be able' does not always mean 'to do'.
   
• Will this improve my work or just change it?
  Change without progress takes time and yields little.
   

Board Level

• Do we think from the organizational priorities or do we build on something existing?
  Starting from your KPIs is wise. Automating actions within an existing process without reforming that process often does not lead to improvement.
   
• Is this necessary or are we making it too big?
  If your organization looks substantially the same after an AI initiative, you're wasting time and energy. An implementation makes sense when it actually benefits the organization.
   

Need support with strategic prioritization?

Start with a Data Governance Assessment. This allows you to discover in three minutes how AI- and Copilot-ready your data governance is. You will immediately receive your score and next steps.