AI Agent Use Case Prioritizer: Score Your Next Automation Candidate
This free assessment scores any candidate AI agent use case across ten factors that predict deployment success, from process volume and data accessibility to error risk and sponsorship. It is designed for IT leaders, operations managers, and automation champions at manufacturers who have a long wish list of automation ideas and need an objective way to sequence them. In about two minutes you get a 0-100 prioritization score, a clear verdict, and specific recommendations for what to fix before you commit budget.
1. How frequently does this process run?
2. How well documented are the rules and steps of the process?
3. Where does the input data for this process live?
AI agents handle unstructured content well, but the data must at least be digital and reachable.
4. What share of cases require genuine human judgment today?
Some judgment is fine for AI agents; a process that is mostly novel edge cases is not a good first candidate.
5. Can you quantify what this process costs today?
6. Do the systems this process touches have usable APIs or integration points?
7. What happens if the agent gets a case wrong?
Consider the worst realistic error, not the average one.
8. Can agent outputs be reviewed by a human before they take effect?
9. How stable is the process itself?
10. Who is sponsoring this automation?
The methodology behind the score
The ten questions map to the four factors that separate successful AI agent projects from stalled ones: economic weight (volume and measurable cost), technical feasibility (data access, APIs, process stability), risk manageability (error impact and human review), and organizational readiness (documentation and sponsorship). Each answer scores zero to three, and your total is expressed as a percentage of the maximum. The weighting is deliberately flat because in our project experience a single zero in any area, such as no integration path or no sponsor, does roughly equal damage regardless of which area it lands in.
What good candidates look like
Across ERP automation projects in discrete manufacturing, the use cases that reach production fastest share a recognizable profile. They are boring, frequent, and well understood, not novel or strategic. The strongest first candidates we see repeatedly:
- AP invoice capture, matching, and coding against purchase orders in the ERP.
- Sales order entry from emailed POs, PDFs, and customer portals.
- Order status, delivery date, and document requests handled by a customer-facing agent.
- Report preparation and data reconciliation between the ERP and satellite systems.
How to use your score
Do not treat the score as a verdict on whether AI can ever handle the process; treat it as a sequencing tool. A 75 and a 45 are both automatable, but the 75 will be live and paying back while the 45 is still fighting data access issues. Score your full candidate list, pilot the top one or two, and use the momentum and credibility from those wins to fund groundwork on the lower scorers. Re-score quarterly, because scores move as documentation improves, APIs get exposed, and sponsors emerge.
How Netray helps you go from score to production
Netray builds production AI agents that operate inside enterprise ERP systems, including Infor SyteLine, Infor LN, and Baan. We run structured use case discovery workshops that apply this same prioritization logic against your actual transaction data, then design, build, and deploy the winning agents with human-in-the-loop controls from day one. For aerospace, defense, and other regulated manufacturers, we deploy fully on-prem so sensitive data never leaves your environment. Bring us your top-scoring use case and we will validate it for free.
Frequently Asked Questions
How many use cases should I score before picking a pilot?
Score at least three to five. The value of the tool is comparative: a 62 looks decent in isolation but weak next to a 78. Most organizations we work with maintain a scored backlog of eight to fifteen candidates, pilot the top one or two, and revisit the list quarterly. Scoring is cheap; a mis-sequenced pilot that stalls for six months is not.
My use case scored low but it is strategically important. Should I still do it?
Not as your first project. A low score means the project will be slow and expensive right now, and a struggling flagship pilot poisons the well for every automation that follows. Instead, spend one or two quarters fixing the specific blockers the assessment surfaced, such as data access or documentation, while you bank a quick win on a higher-scoring process. Strategic value is a reason to invest in readiness, not to skip it.
Does a high score guarantee the AI agent project will succeed?
No assessment can guarantee that, but high scorers fail for predictable and avoidable reasons: skipping the baseline measurement, granting autonomy before accuracy is proven, or under-investing in the ERP integration. If you pair a 70+ score with a measured baseline, a human review step during ramp-up, and real API-level integration rather than screen scraping, your odds are excellent. The assessment removes the biggest risk, which is picking the wrong process.
Send us your highest-scoring use case and get a free feasibility validation from Netray's AI engineers.
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