The Use Case Selection Trap
Most organizations choose their first AI use case based on what sounds ambitious or what a vendor demo made look easy. Both are unreliable selection criteria.
The right first use case has two qualities:
- The problem is specific enough that you can define “good output” in advance.
- The data needed to solve it already exists in a usable format.
That second point gets skipped more often than the first.
A concrete use case:
“We want an AI agent to handle first-contact customer inquiries about order status.”
That’s testable. You can define what a correct response looks like. You probably have historical inquiry data. You have a system with order information you can connect to.
A vague use case:
“We want AI to improve the customer experience.”
That’s not a use case – it’s a goal. Before an implementation plan can be written, it needs to become specific.
Filtering question:
Can you write, in two sentences, exactly what success looks like at 90 days?
If not, the use case isn’t ready.
---
Build, Configure, or Integrate
There’s a spectrum from off‑the‑shelf AI tools through configurable platforms to fully custom‑built models. Where you land on that spectrum for a given project should be decided early – it determines timelines, costs, and your ongoing operational commitment.
Custom model training is expensive, requires data you often don’t have in the right format, and takes longer than vendors estimate. It’s the right choice when:
- Your domain knowledge is genuinely proprietary and central to differentiation, or
- Your integration requirements are unusual enough that standard platforms won’t cover them.
For most customer service, internal knowledge management, and intake processing problems – the most common AI implementation targets for businesses in 2026 – a configurable platform trained on your content will outperform a custom‑built solution in the first year, simply because it gets to production faster and iterates faster.
Key question before you finalize the plan:
What would have to be true about your business for a configurable platform not to be enough?
If the honest answer is “nothing specific,” start with configuration.
---
What Data Readiness Actually Means
“Ensure your data is ready” hides a lot of work.
In practice, data readiness means you:
- Know exactly what data you need.
- Know where it lives.
- Know who controls access to it.
- Know whether it can flow into the AI system automatically or requires a manual export each time.
That last point matters more than people expect. A system that requires someone to manually re‑upload a spreadsheet every Friday is not a production system – it’s a prototype with extra steps.