# AI Agent Use Case Prioritizer: Score Your Next Automation Candidate > Source: https://netray.co/tools/ai-agent-use-case-prioritizer > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Answer ten questions about a candidate process and get a prioritization score that tells you whether it deserves an AI agent pilot now, later, or never. ## Category ai-automation ## Tool **Kind:** assessment **Questions:** #### How frequently does this process run? **Options:** ##### A few times per quarter or less **Score:** 0 ##### A few times per month **Score:** 1 ##### Daily, in moderate volume **Score:** 2 ##### Continuously or many times per day **Score:** 3 #### How well documented are the rules and steps of the process? **Options:** ##### Undocumented, lives in one person's head **Score:** 0 ##### Partially documented, lots of tribal knowledge **Score:** 1 ##### Documented but slightly out of date **Score:** 2 ##### Fully documented and current **Score:** 3 #### Where does the input data for this process live? **Help:** AI agents handle unstructured content well, but the data must at least be digital and reachable. **Options:** ##### Paper, phone calls, or scattered personal files **Score:** 0 ##### Email attachments and shared drives, inconsistent formats **Score:** 1 ##### Digital systems, accessible with some effort **Score:** 2 ##### Digital systems with clean, API-accessible data **Score:** 3 #### What share of cases require genuine human judgment today? **Help:** Some judgment is fine for AI agents; a process that is mostly novel edge cases is not a good first candidate. **Options:** ##### More than half of all cases **Score:** 0 ##### Roughly a quarter to a half **Score:** 1 ##### Around 5-25% **Score:** 2 ##### Under 5%, almost everything follows the pattern **Score:** 3 #### Can you quantify what this process costs today? **Options:** ##### No idea, never measured **Score:** 0 ##### Rough guess only **Score:** 1 ##### Estimated from headcount and time studies **Score:** 2 ##### Fully baselined with cost per transaction **Score:** 3 #### Do the systems this process touches have usable APIs or integration points? **Options:** ##### No, screen access only and vendors block integration **Score:** 0 ##### Limited, would need file drops or database access **Score:** 1 ##### Partial APIs cover most of the flow **Score:** 2 ##### Yes, modern APIs across all systems involved **Score:** 3 #### What happens if the agent gets a case wrong? **Help:** Consider the worst realistic error, not the average one. **Options:** ##### Safety, compliance, or contractual damage that is hard to reverse **Score:** 0 ##### Significant cost or customer impact, recoverable with effort **Score:** 1 ##### Minor cost, caught and corrected downstream **Score:** 2 ##### Low impact and easily reversible **Score:** 3 #### Can agent outputs be reviewed by a human before they take effect? **Options:** ##### No, actions are immediate and irreversible **Score:** 0 ##### Only with major process redesign **Score:** 1 ##### Yes, with a lightweight approval step **Score:** 2 ##### Yes, a natural review checkpoint already exists **Score:** 3 #### How stable is the process itself? **Options:** ##### Changes constantly, redesign underway **Score:** 0 ##### Changes every few months **Score:** 1 ##### Mostly stable with occasional tweaks **Score:** 2 ##### Stable for years **Score:** 3 #### Who is sponsoring this automation? **Options:** ##### Nobody yet, it is just an idea **Score:** 0 ##### An individual contributor who feels the pain **Score:** 1 ##### A department manager with some budget influence **Score:** 2 ##### An executive sponsor with allocated budget **Score:** 3 **Bands:** #### Not Ready: Deprioritize This Use Case **Min:** 0 **Max:** 39 **Verdict:** This use case is missing too many fundamentals to succeed as an AI agent pilot right now. Low volume, poor data access, high judgment share, or unmanaged risk would make the project expensive and the results disappointing. Park it and pick a stronger candidate. **Recommendations:** - Run this assessment against 3-5 other candidate processes and pilot the highest scorer instead. - If this process matters strategically, fix the blockers first: document the rules, digitize the inputs, and baseline the cost. - Confirm API or integration access for the systems involved before revisiting. - Recruit a named business sponsor; automation projects without one stall in procurement. - Revisit the score in one to two quarters after the groundwork is done. #### Promising: Close the Gaps, Then Pilot **Min:** 40 **Max:** 69 **Verdict:** This use case has real potential but two or three weaknesses will drag on the project if you ignore them. Most candidates in this band become strong pilots after four to eight weeks of targeted preparation on data access, documentation, or review workflow design. **Recommendations:** - Identify your lowest-scoring answers above and fix those specific gaps before writing any code. - Baseline the current cost per transaction so you can prove ROI after go-live. - Design a human-in-the-loop review step so early errors are caught cheaply. - Scope the pilot to the highest-volume, most repetitive slice of the process, not the whole thing. - Set explicit success criteria, such as 70% straight-through processing at equal or better accuracy. #### High-Priority Pilot Candidate **Min:** 70 **Max:** 100 **Verdict:** This use case has the volume, data access, stability, and sponsorship that successful AI agent deployments share. Candidates in this band routinely reach production within one quarter and pay back quickly. Move it to the top of your automation roadmap. **Recommendations:** - Lock the baseline metrics now: cost per transaction, cycle time, and error rate. - Run a 4-6 week pilot on live volume with human review before granting the agent autonomy. - Define escalation rules for the exception share that stays with humans. - Plan the scale path early: adjacent processes, additional sites, or higher autonomy levels. - Engage a partner with ERP-integration experience so the agent works inside your systems, not beside them. ## Content **Intro:** 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. **Sections:** #### The methodology behind the score **Body:** 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 **Body:** 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: **Bullets:** - 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 **Body:** 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 **Body:** 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. **Cta:** Send us your highest-scoring use case and get a free feasibility validation from Netray's AI engineers. **Faq:** #### How many use cases should I score before picking a pilot? **Answer:** 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? **Answer:** 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? **Answer:** 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. ## Search terms AI agent use case prioritization, AI use case assessment, which processes to automate with AI, AI agent readiness score, AI automation prioritization framework, enterprise AI use case selection --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json