Find the AI use case
worth building first.
Twenty production-ready AI patterns. Answer three questions and we surface the top matches for your situation, with reasoning. Or browse curated collections if you already know roughly what you’re looking for.
Answer three questions. See your top three matches.
No filters. No empty results. We always rank all twenty use cases against your answers and surface the strongest matches. Change your answers and the list updates immediately.
What business are you in?
What would move the needle most?
How ambitious do you want the first move to be?
Most universally high-payoff
Invoice processing and AP automation
Accounts payable teams spend most of their month on the same repetitive work: pulling data from invoices, matching to purchase orders, routing for approval, and chasing exceptions.
Contract review automation
Legal teams spend 30 to 40 percent of their time on first-pass contract review: extracting key terms, checking against standard clauses and flagging risk. Most of that work is repetitive but requires trained judgement to do safely.
AI opportunity prioritisation
Organisations run too many AI pilots in parallel. Few reach production. There is no shared logic for deciding which bets to fund, which to park, and which to kill.
Or browse by what you’re trying to do.
Five collections, each a deliberate point of view about where to start given a specific situation. Not filters. Opinions.
Quick wins most enterprises ship first
Low-effort, high-payoff patterns. Each ships in weeks, delivers measurable value in a quarter, and builds organisational confidence for the harder bets that follow.
Invoice processing and AP automation
Accounts payable teams spend most of their month on the same repetitive work: pulling data from invoices, matching to purchase orders, routing for approval, and chasing exceptions.
AI opportunity prioritisation
Organisations run too many AI pilots in parallel. Few reach production. There is no shared logic for deciding which bets to fund, which to park, and which to kill.
Support ticket triage and routing
Tickets are routinely misrouted, lose context between handoffs, and sit in the wrong queue while the SLA clock runs down. A human triage team is expensive and still slow at peak.
Sales research and meeting briefing
Sellers either walk into meetings cold or spend 30-plus minutes a meeting on account research. Neither is a good use of the most expensive hour in the funnel.
PII redaction at scale
Sharing documents for analytics, training, or third-party work requires PII to be removed. Doing it manually is slow, error-prone and never scales past a handful of files.
Meeting capture and CRM sync
Sellers spend five to eight hours a week updating CRM records, and the data is still patchy. Managers run pipeline meetings on incomplete information.
High-payoff plays for banks, insurers and fintechs
The patterns with the strongest track record across regulated financial services. Document-heavy workflows, compliance pressure, high-volume customer operations.
Contract review automation
Legal teams spend 30 to 40 percent of their time on first-pass contract review: extracting key terms, checking against standard clauses and flagging risk. Most of that work is repetitive but requires trained judgement to do safely.
Regulatory change monitoring
Regulated firms across multiple jurisdictions often miss, or late-detect, important rule changes. When they do catch them, the triage to identify business impact is slow and manual.
Fraud investigation assistant
Fraud analysts spend 60 to 80 percent of case time on evidence-gathering across systems, before the analysis even starts. Backlogs build; the good cases wait.
Multilingual voice support
Serving customers in three to five languages usually means specialised teams per language, or restricting coverage. Both approaches cap service quality at the wrong place.
Management report generation
Finance teams spend five to ten days a month building management packs: pulling numbers from multiple systems, writing narrative commentary and chasing variance explanations.
Due-diligence document review
M&A and regulatory due diligence involves reviewing thousands of documents under a tight timeline. Throughput is capped by the number of lawyers the firm can throw at it.
Serve more customers without adding headcount
The three patterns that reshape the economics of customer service. Deflect volume where it makes sense, speed up triage everywhere else.
First-line support automation
Support teams spend 40 to 60 percent of their time answering the same questions. The answers exist, but the volume caps how much can be served without adding headcount.
Multilingual voice support
Serving customers in three to five languages usually means specialised teams per language, or restricting coverage. Both approaches cap service quality at the wrong place.
Support ticket triage and routing
Tickets are routinely misrouted, lose context between handoffs, and sit in the wrong queue while the SLA clock runs down. A human triage team is expensive and still slow at peak.
Risk, compliance and audit patterns
For organisations where the constraint isn’t cost or speed - it’s getting AI past a risk committee. These use cases are built to be defended.
PII redaction at scale
Sharing documents for analytics, training, or third-party work requires PII to be removed. Doing it manually is slow, error-prone and never scales past a handful of files.
Regulatory change monitoring
Regulated firms across multiple jurisdictions often miss, or late-detect, important rule changes. When they do catch them, the triage to identify business impact is slow and manual.
Fraud investigation assistant
Fraud analysts spend 60 to 80 percent of case time on evidence-gathering across systems, before the analysis even starts. Backlogs build; the good cases wait.
Due-diligence document review
M&A and regulatory due diligence involves reviewing thousands of documents under a tight timeline. Throughput is capped by the number of lawyers the firm can throw at it.
Lift revenue without adding sellers
Where AI earns its place in go-to-market. More RFPs answered, better meeting prep, cleaner pipeline data.
RFP and proposal response drafting
RFP responses take 40 to 200 person-hours each. Win rate is often capped not by capability but by how many RFPs the team can physically respond to.
Sales research and meeting briefing
Sellers either walk into meetings cold or spend 30-plus minutes a meeting on account research. Neither is a good use of the most expensive hour in the funnel.
Meeting capture and CRM sync
Sellers spend five to eight hours a week updating CRM records, and the data is still patchy. Managers run pipeline meetings on incomplete information.
All twenty use cases, organised by function.
For when you know what you’re looking for. Grouped by the business function most likely to own the programme.
- Invoice processing and AP automationAccounts payable teams spend most of their month on the same repetitive work: pulling data from invoices, matching to purchase orders, routing for approval, and chasing exceptions.70–90%Quick win
- Internal knowledge-base AI searchOrganisational knowledge lives in five to ten different systems. Staff spend 30 to 60 minutes a day searching for information that already exists somewhere in the stack.2–3Medium
- Supply-chain document reconciliationPurchase orders, goods receipts and invoices routinely disagree. Payment delays, supplier disputes and reconciliation work consume operations time every month.80–95%Medium
- AI opportunity prioritisationOrganisations run too many AI pilots in parallel. Few reach production. There is no shared logic for deciding which bets to fund, which to park, and which to kill.40–60%Quick win
- First-line support automationSupport teams spend 40 to 60 percent of their time answering the same questions. The answers exist, but the volume caps how much can be served without adding headcount.40–60%Medium
- Multilingual voice supportServing customers in three to five languages usually means specialised teams per language, or restricting coverage. Both approaches cap service quality at the wrong place.30–50%Transformational
- Support ticket triage and routingTickets are routinely misrouted, lose context between handoffs, and sit in the wrong queue while the SLA clock runs down. A human triage team is expensive and still slow at peak.25–40%Quick win
- Management report generationFinance teams spend five to ten days a month building management packs: pulling numbers from multiple systems, writing narrative commentary and chasing variance explanations.60–80%Medium
- Data pipeline accelerationData teams spend 40 to 60 percent of their capacity maintaining and repairing pipelines. Analytics backlogs grow; business requests wait months.2–3xMedium
- Contract review automationLegal teams spend 30 to 40 percent of their time on first-pass contract review: extracting key terms, checking against standard clauses and flagging risk. Most of that work is repetitive but requires trained judgement to do safely.60–80%Medium
- Due-diligence document reviewM&A and regulatory due diligence involves reviewing thousands of documents under a tight timeline. Throughput is capped by the number of lawyers the firm can throw at it.50–75%Medium
- RFP and proposal response draftingRFP responses take 40 to 200 person-hours each. Win rate is often capped not by capability but by how many RFPs the team can physically respond to.50–70%Medium
- Sales research and meeting briefingSellers either walk into meetings cold or spend 30-plus minutes a meeting on account research. Neither is a good use of the most expensive hour in the funnel.5–10Quick win
- Meeting capture and CRM syncSellers spend five to eight hours a week updating CRM records, and the data is still patchy. Managers run pipeline meetings on incomplete information.3–6Quick win
- Content generation at scaleMarketing teams cannot keep up with demand for localised, variant, on-brand content across channels. Quality suffers or production bottlenecks.3–5xQuick win
- Candidate screening and shortlistingRecruiters spend 40 to 60 percent of their time screening CVs, the majority of it on clearly unqualified candidates. The good candidates wait in the queue while this happens.60–80%Medium
- Employee policy Q&A assistantHR teams handle thousands of policy questions a year. The answers exist in the handbook, but employees either cannot find them or do not trust the handbook.50–70%Quick win
- Regulatory change monitoringRegulated firms across multiple jurisdictions often miss, or late-detect, important rule changes. When they do catch them, the triage to identify business impact is slow and manual.80–95%Medium
- PII redaction at scaleSharing documents for analytics, training, or third-party work requires PII to be removed. Doing it manually is slow, error-prone and never scales past a handful of files.85–95%Quick win
- Fraud investigation assistantFraud analysts spend 60 to 80 percent of case time on evidence-gathering across systems, before the analysis even starts. Backlogs build; the good cases wait.40–60%Transformational
Let’sbuildyoursystemnext.
Thirty minutes with someone who’d be doing the work. No slide deck, no intake form. We’ll tell you what’s feasible, where you’ll hit friction, and what we’d pick up first.