Score each candidate use case on two axes, business value and feasibility, and prioritize the ones that land high on both rather than chasing the single highest-value idea if it also requires infrastructure or data the organization does not yet have. For value, estimate cost saved, revenue influenced or risk reduced using current baseline figures supplied by the process owner rather than rough guesses from the AI team. For feasibility, score data availability and quality, integration complexity with existing systems, regulatory sensitivity, and whether the underlying task is one current models handle reliably or one that still needs significant human review. Plot the candidates on a simple two-by-two grid and start with the quadrant that scores high on both value and feasibility, since early wins build organizational trust and budget for harder projects later in the roadmap. Resist pressure to start with the most visible or most requested use case if it scores poorly on feasibility, since a stalled high-visibility project does more organizational damage than a smaller, quieter win. Revisit the scoring every quarter as data readiness and model capability both continue to improve. Nanobase AI runs this scoring exercise as a structured workshop with stakeholders from IT, security and the business unit in the room together.
Scoring value without guessing
Business value should be estimated using current baseline figures supplied by the process owner, cost saved, revenue influenced or risk reduced, not rough guesses generated by the AI team in a workshop. A process owner who runs the workflow today usually knows its actual cost in hours or dollars far more precisely than a technical team estimating from the outside, and grounding the value score in that real number prevents the common trap of prioritizing whichever use case sounds most exciting rather than whichever one is actually most valuable.
A use case scored on enthusiasm rather than a real baseline number will eventually be judged against that same missing baseline once someone asks what it actually delivered.
Scoring feasibility across four dimensions
| Feasibility dimension | Low score looks like | High score looks like |
|---|---|---|
| Data availability and quality | Locked in unstructured documents, inconsistent formatting | Clean, structured, accessible via API |
| Integration complexity | Needs several systems to read and write data | Self-contained or touches one system |
| Regulatory sensitivity | Regulated data, high compliance review burden | Low-sensitivity internal data |
| Task reliability with current models | Requires nuanced judgment models still handle poorly | Repetitive, rule-bound, models handle it reliably today |
Plotting the two-by-two and choosing where to start
Plot each candidate use case on a grid with business value on one axis and feasibility on the other, and start execution in the quadrant that scores high on both, not the single highest-value idea if it also requires infrastructure or data readiness the organization does not yet have. Early wins in the high-value, high-feasibility quadrant build organizational trust and internal capability that make the harder, lower-feasibility projects further down the roadmap easier to execute once their turn comes.
Resist pressure to start with the most visible or most requested use case if it scores poorly on feasibility, since a stalled high-visibility project does more organizational damage, in credibility and future budget approval, than a smaller, quieter win that actually ships.
Running the exercise as a structured workshop
- Gather stakeholders from the business unit, IT and security in the same room, not sequential one-on-one interviews.
- Have the business unit propose candidate use cases and supply real baseline cost or time figures for each.
- Score feasibility jointly with IT and security present, so data and compliance concerns surface during scoring rather than after a use case is already prioritized.
- Plot the results and agree on the starting quadrant together, rather than having the AI team make the call unilaterally afterward.
- Revisit the scoring every quarter, since data readiness and model capability both continue to improve, which can shift a previously low-feasibility use case into a viable one.
Where this fits into the broader roadmap
Use case prioritization is the input to a roadmap, not a replacement for one; once the highest-priority quadrant is identified, sequencing those use cases against budget and team capacity over the next year is a separate exercise, covered in building a 12-month AI roadmap. For a look at which categories of use case commonly score well on both axes across industries, see highest-ROI AI use cases for 2026.
Frequently asked questions
How many use cases should go through this scoring exercise at once?
Ten to fifteen candidate use cases is a manageable number for a single workshop session, enough to produce a meaningful spread across the grid without making the exercise unwieldy. Fewer than five tends not to reveal much variation; significantly more can be split across two sessions.
What happens to use cases that score low on both value and feasibility?
Park them rather than discarding them permanently, and revisit at the next quarterly review, since feasibility in particular tends to improve over time as data infrastructure matures and models improve. A use case that scores poorly today is not necessarily a poor idea forever.
Should feasibility or value carry more weight in the scoring?
Most organizations weight them roughly equally for the initial prioritization, since a high-value, low-feasibility project consumes resources without delivering results, while a high-feasibility, low-value project delivers results nobody particularly needed. Adjust the weighting only if leadership has an explicit reason, such as needing a fast, low-risk first win.
Can this scoring model be reused for use cases proposed after the initial roadmap is set?
Yes, and it should be. New use case proposals arriving mid-year should go through the same scoring exercise rather than being added to the roadmap based on who proposed them or how urgently they are requested, which keeps prioritization consistent as the backlog grows.
How Nanobase AI helps
Nanobase AI runs this scoring exercise as a structured workshop with stakeholders from IT, security and the business unit in the room together, rather than delivering a prioritized list based on outside assumptions about what a company's data and systems can support. The output is a shared, defensible starting point for the roadmap rather than a list one team has to sell internally afterward.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.