The first AI use case you choose sets the trajectory of your whole AI effort — a strong win builds momentum, a weak one stalls everything. So it’s worth choosing deliberately rather than grabbing the flashiest idea. Here’s how, and how dgm helps. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)
The five criteria
Score each candidate use case on:
- Value — clear, ideally measurable ROI.
- Feasibility — achievable with current tools and models.
- Data readiness — the data it needs is available and usable.
- Boundedness — narrow enough to deliver and measure cleanly.
- Risk — lower-stakes is better for a first project.
The right first use case scores high on all five — not just value or just feasibility.
The best first use cases are often unglamorous
The flashy idea rarely makes the best first project. The winners are usually high-volume repetitive work (automating it delivers measurable hours back) or SaaS consolidation (clear cost savings). They’re not exciting, but they deliver ROI quickly and safely — which is exactly what a first use case should do.
Why the first choice matters
A strong first win builds evidence, savings, and confidence to expand; a failed or inconclusive first project can stall the whole effort and sour leadership on AI. Starting in the right place is how successful AI programs build momentum (see how to choose your first AI project).
What to avoid
- Too big — hard to deliver and measure.
- Too high-stakes — an early mistake is costly.
- Data-dependent on data you don’t have — sets you up to fail.
How dgm helps
dgm’s $399 assessment maps your workflows and data and ranks opportunities by ROI and feasibility — surfacing the high-value, feasible, low-risk first use case — then implements it at $3,999/month with no per-seat fees. If you’d rather explore the platform yourself first, go straight to osFoundry; if you want the right starting point identified, that’s where dgm comes in.