Smarter Decisions (3/3)

Gregor Stoeckler

Decision Debt:

Why Digital Transformation Keeps Stalling 

“Let’s do the Brownfield conversion now. We’ll deal with the data later.”

I heard this sentence so many times. It sounds pragmatic. Rational. Focused. But it’s not. Behind this single sentence lies a series of silent, postponed decisions:

  • Not clarifying the business value you need
  • Not defining or aligning with a broader data strategy
  • Not preparing for the age of AI
  • Not investing in cleaner, future-ready processes

It’s not just technical debt. It’s decision debt — the growing cost of decisions we delay, avoid, or defer to the future. Unlike technical debt, it doesn’t show up in code or budget lines. But it has real consequences. And in today’s Data & AI-driven world, it’s a BIG reason why transformation efforts lose momentum.

 

What Is Decision Debt?

Decision debt builds up when important choices are pushed off — not because they’re unimportant, but because they’re unclear, uncomfortable, or difficult to prioritize.

It shows up in subtle ways:

  • Vague priorities
  • Unclear ownership
  • Recurring “alignment” meetings
  • Pilots that never scale
  • Initiatives that quietly fade into the backlog

And like any debt, it carries interest: The longer you delay, the more costly it becomes — in time, energy, trust, and missed opportunity.

 

Why Data & AI Projects Are Especially Exposed

Decision debt can hit any type of initiative. However, Data and AI initiatives are an area that seems to build up decision debt faster — for three reasons:

  1. They cross organizational silos.
    Data is never just one team’s responsibility. When everyone is involved, no one feels empowered and decisions become complicated and slow.
  1. They challenge old ways of thinking.
    Successful AI use cases need to go beyond automating the status quo process. That provoces ambiguity and resistance — which makes people hesitate or seek too much consensus. Consensus of course hinders outside the box thinking and solutions.
  1. They require long-term thinking.
    A good data foundation is an investment without immediate return, making it easy to postpone key choices.

As Harvard Business Review points out, organizational silos, internal politics, and unclear cross-functional ownership can significantly slow down decision-making and execution, especially when transformation efforts cut across departments (Mortensen & Gardner, 2019). So, while complexity stalls decisions I believe it’s not complexity itself, but the feared consequences or in other words the ability to take decisions in uncertainty – getting “enough” clarity on how to move forward.

Here’s a real-world example:

A company moves to SAP S/4HANA using a  Brownfield approach. To stay lean, they skip the work of removing unused customizations or code, data cleansing and deeper process redesign. The system goes live.
Now we’re called in to assess and realize AI use cases that leverage SAP data. The sh** hits the fan: the technical debt is a heavy burden. Data is messy. Processes are misaligned. AI is seen as a magic wand that finally realizes the business value.

The team now spends six months for band aiding and cleaning up what could have been addressed during the original project — doubling the effort, delaying AI rollout, and losing precious momentum.

 

The Real Cost of Decision Debt

Every day a decision is delayed, opportunity slips away.

  • Innovation stalls when foundational decisions remain open
  • The Team loses energy and talent disengages
  • Trust erodes when words don’t match actions

In the context of Data & AI I’m not getting tired to repeat:

“If you do not digitalize now and fully embrace AI,

you are actively deciding to be slower than your competition.”

That’s not a threat — it’s a fact. McKinsey assessed that digital laggards are five times more likely to lose market share. (Bughin et al., 2017)

Momentum matters. And when you miss the window, it’s getting harder to catch up.

 

How to Spot and Address Decision Debt

Before trying to fix decision debt, it helps to understand where it’s hiding.
Here’s a short assessment to start with:

  1. What decisions have we avoided over the past 6–12 months?
    Are they related to data ownership, AI use cases, or platform migration?
  2. Is there a pattern?
    Are we avoiding decisions due to unclear ownership, fear of risk, or constant alignment loops?
  3. What are the ripple effects?
    Which teams, goals, or outcomes are blocked as a result?
  4. What is the real cost of not deciding?
    Are we missing opportunities, slowing down progress, or creating internal friction?
  5. Which decisions could we make with confidence — even if they’re not perfect?
    Some choices just need to be made. You can course-correct later.

This kind of reflection brings clarity. And clarity creates motion.

 

Progress Loves Clarity

If your AI or data initiative feels stuck, it might not be a technology issue.
It might be decision debt — and the good news is: you can do something about it.

Start by naming the decisions that are overdue. Look at their impact.
And most importantly: decide to decide.

You don’t need perfection. You need progress.

 

References

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