Use case assessments
Is it actually AI, and is it worth building?
One assessment per use case, with a verdict. Some of these say no, which is the point — scoring forty AI proposals inside a regulated enterprise, roughly one in ten turned out not to be AI at all.
AI ticket routing
Most ticket routing is a classification problem with a small, stable set of categories and an existing routing table. That is what rules are for.
Usually automation with a label Finance operations · accounts payableAI invoice matching
Matching an invoice to a purchase order and a receipt is deterministic arithmetic. Reading a scanned invoice from a supplier who changes their layout is not.
Split — extraction yes, matching no HR · recruitmentAI CV screening
This is real AI and it is explicitly Annex III high-risk under the EU AI Act. The build is the easy part; the obligations attached to it are not.
Genuinely AI, and high-risk Internal productivity · collaborationAI meeting summaries
Real AI, well solved by existing products, and the interesting question is not whether to build but who is being recorded and whether they agreed.
Genuinely AI — but buy it Customer operations · supportAI customer support chatbot
Real AI with real value. The failure mode is almost never the model; it is a scope that covers everything and is trusted on nothing.
Genuinely AI — scope decides it Legal operations · procurementAI contract review
Finding clauses and comparing them to a playbook is real AI doing real work. Deciding whether to accept a clause is not a job to hand over.
Genuinely AI — with a human gate Supply chain · planningAI demand forecasting
This is a well-established statistical problem. Classical methods frequently match or beat machine learning on business time series, and they are far cheaper to run.
Not new AI — it's forecasting Customer success · subscription businessAI churn prediction
Predicting churn is a solved classification problem. Most projects fail after the prediction, because nobody defined what happens when the score goes red.
Genuinely AI — but the model isn't the gap Operations · records managementAI document classification
Twelve stable categories is a rules problem. Two hundred shifting ones is not. The assessment is mostly counting.
Depends on the category count Sales · marketing operationsAI lead scoring
Most lead scoring runs on company size, industry and behaviour, all of which are rules. The model appears when you have enough closed-won history to learn from, and most companies do not.
Usually automation with a label Finance operations · internalAI expense approval
An expense policy is a written set of thresholds and categories. Encoding it is automation, and it should stay auditable because employees will contest decisions.
Not AI — your policy is already rules Internal productivity · knowledge managementAI knowledge search
Real AI, immediate value, low regulatory surface. The thing that stops it is almost always access control rather than retrieval quality.
Genuinely AI — usually the best first project Engineering · developer productivityAI code review
Real value on a narrow band of review work. Point it at everything and developers stop reading it, which costs you more than not having it.
Genuinely AI — narrow it Engineering · quality assuranceAI test generation
Real value on coverage and boilerplate. The trap is that generated tests assert current behaviour, which means they can lock in a bug and defend it.
Genuinely AI — with one serious caveat Content operations · localisationAI translation
Real AI, mature products, and no differentiation available from building your own. The decision is which content gets human review, not which model.
Genuinely AI — buy it Fintech · payments · riskAI fraud detection
Real AI on a genuine pattern problem. What decides whether it works is not detection rate but what happens to the legitimate customers it stops.
Genuinely AI — the false positives are the product Operations · shared inboxesAI email triage
Sender domain, subject patterns and recipient address carry most of the routing signal. That is a rules problem, and rules explain their decisions.
Usually automation with a label HR · people operationsAI onboarding assistant
New joiners ask the same questions as everyone else, earlier and more often. A separate system duplicates infrastructure and splits the content maintenance.
This is knowledge search — build that Sales operations · revenueAI sales forecasting
Sales forecasts run on rep-entered stage and close date. If those are optimistic or inconsistent, a model learns the optimism and states it with more authority.
Not a model problem — a data problem HR · recruitment operationsAI resume parsing
Extracting structured fields from a CV is ordinary extraction. Ranking candidates is Annex III high-risk. The same product usually does both, and the toggle is not obvious.
Genuinely AI — and not the same as screening Customer insight · HR toolingAI sentiment analysis
Emotion inference in workplace and education contexts is a prohibited practice under the EU AI Act, in force since February 2025. On customer feedback it is permitted and rarely tells you more than reading it would.
Prohibited in workplaces — weak elsewhere Content operations · digital asset managementAI image tagging
Real AI, mature and cheap. Unless you are tagging people, in which case you have left ordinary image tagging and entered biometrics.
Genuinely AI — buy it Operations · back officeAI data entry
Extraction from documents is real AI and works. But a lot of data entry exists because two systems do not talk to each other, and an integration removes the job rather than automating it.
Usually — but ask why it exists Marketing · content operationsAI content generation
Real capability, no differentiation in building it, and an Article 50 transparency duty on published output that has been enforceable since 2 August 2026.
Genuinely AI — with a disclosure obligationMore assessments are being added. If the one you need is missing, the AI Reality Check scores your whole list in a week.