Stop Building Use Cases in Reverse

I mentioned in a post this week that I would write a longer article that would cover the extent of the problem I see in many organizations that attempt to do any form of data transformation, or data strategy etc.

Let me be clear, this isn’t anything new. Many organizations have been told to build use cases to unlock the value of data and AI. Again, this isn’t anything new. I’m not saying that they haven’t, many know, and in fact many choose to forget.

Why? I ask you say.

Because many build their use cases “backwards.”

Instead of starting with business outcomes and value, they started with:

• “We’ve got this data; what can we do with it?”
• “We’ve just launched this platform, what use cases can we spin up?”

What has the result been? Loads of activity and hubbub! Sounds great right?

But in  reality, the outcome tends to be “not enough impact.”

So how do we flip the model?

Four Layers to Build Use Cases That Deliver

I’m just going to call this a “value-led use case framework” which has four distinct but connected layers:

1. Business Use Case
2. Solution (Product or Algorithm)
3. Data Use Case
4. Technology Use Case

This isn’t just a nice hierarchy. Its purpose driven and hangs off the North Star which is the business use case. This is the value piece, whether that’s revenue/growth focused, or looking at cost efficiencies etc. This is the discipline and focus that is required, and one that that separates wasted investment from real ROI.

Business Use Case: Anchor to Outcomes

Every use case should begin with a crisp, commercial intent. It should answer the fundamental question:

What are we trying to achieve?

Is it revenue growth, cost reduction, improved customer experience, risk mitigation? Just as I explained in the previous paragraph.

So, what do business use cases sound like? They typically sound like this:

• “We want to reduce customer churn by 10 percent in the next 12 months.”
• “We need to shorten onboarding time by 30 percent for new merchants.”
• “Our aim is to improve early fraud detection by 20 percent in real time.”

What are the distinctions here?

There is absolutely no mention of AI, cloud, machine learning, dashboards etc, just the business challenge or opportunity. You can even spin up a problem statement as part of this phase and tie it back to a specific business problem.

Solution (Product): Deliver the Outcome

Now that we know the business problem, opportunity, problem statement etc., we can now start to shape the response. Are we focused on creating:

• A churn prediction model
• A self-serve onboarding portal powered by intelligent workflows
• A real-time fraud scoring engine

Some solutions are algorithms, some are tools,  and some are full-on products. What matters is that they directly support the business outcome as described.

I often find this step often gets skipped when teams are in a rush to play with data or deploy the latest tech stack. You can see the legacy of this type of rushed decision making in the adoption of products, no real ROI, and loads of shelfware lying around!

Data Use Case: Fuel the Solution

Once you have defined the business outcome and the solution, then you can identify what data is actually needed. It’s not just the data that you have, it might be external types too. So you need to think about what you need and why.

Think about it through three simple questions:

• What data sources are critical?
• Where is the data?
• Is it trusted, timely, accessible?

Here’s the other benefit in looking at it this way, data quality becomes a business conversation, not a technical one. Because it’s now tied to a clear outcome.

Technology Use Case: Enable Efficient Execution

Finally, and only now comes the tech chat. I bet you have been gagging to talk about tech from the beginning! Nope, leave it out of the conversation, we need to think wider than the tech piece, and remember, it’s an enabler and that’s why it’s the last point.

• Do you need real-time APIs or batch jobs?
• Do you need MLOps or a lightweight scoring engine?
• What’s the simplest tech stack to achieve the goal?

Yep, and you know it too, that many organizations start here, trying to retrofit value into architecture. But architecture without purpose is just expensive design.

What Happens When You Flip the Flow?

You get huge advantages from this value led conversation, some of these are:

• Clearer prioritization
• Faster time to value
• Better stakeholder alignment
• Less AI hype, more AI delivery

Most importantly, you get outcomes that matter to your board, your business stakeholders, the C-Suite, external stakeholders and not just the data team.

Bottom Line

If your AI and data teams are struggling to show value, don’t focus on building pipelines or models. Focus on business intent.

Ask the question that too often gets skipped:

“What problem are we solving, and why does it matter to the business?”

That’s where every successful use case begins.