Proven use cases of AI:

he signal is simple: when AI reduces error, delay, or waste in a repeatable workflow, value compounds. Below are proven patterns—across scale and sector—that move from slide-ware to balance-sheet impact.

1. Customer support copilots
   What it is: LLM-assisted agents that summarise history, draft responses, and suggest next actions.
   Why it works: Cuts handle time, improves first-contact resolution, and standardises tone.
   How to start: Fine-tune a small model on your past tickets and knowledge base; route high-risk cases to humans via confidence thresholds.
   Watchouts: Hallucinations without retrieval; privacy in live chat transcripts; drift as policies change.
2. Sales enablement and pricing
   What it is: Propensity models that prioritise leads, recommend bundles, and nudge next best actions.
   Why it works: Time is finite; reps focus on the deals that are both winnable and valuable.
   How to start: Combine CRM history, product usage, and firmographics; use interpretable models first to build trust.
   Watchouts: Leakage of protected classes; incentives that optimise for short-term bookings over long-term value.
3. Demand forecasting and inventory
   What it is: Probabilistic forecasts at SKU-location level, updated daily.
   Why it works: Fewer stockouts, fewer write-offs, better cash conversion.
   How to start: Begin with key SKUs and seasonal effects; add weather and local events as features if relevant.
   Watchouts: Data sparsity; overfitting holiday spikes; supply variability.
4. Logistics and routing
   What it is: Optimised routes that respect delivery windows, driver hours, and traffic patterns.
   Why it works: Shorter miles, lower fuel, happier customers.
   How to start: Pilot on one depot; feed telematics data back to retrain weekly.
   Watchouts: Unrealistic time windows; change-management with drivers.
5. Visual quality inspection
   What it is: Computer vision that detects defects on lines or in the field.
   Why it works: Machines do not blink; they keep standards consistent at speed.
   How to start: Collect diverse images of acceptable and defective items; label carefully; deploy near-line for shadow mode before go-live.
   Watchouts: Domain shift when lighting or components change; edge deployment constraints.
6. Document processing
   What it is: OCR + LLMs to extract entities from invoices, claims, contracts, and lab reports.
   Why it works: Straight-through processing, reduced rekeying, faster cycle times.
   How to start: Tackle one form type; build evaluation sets; put a human in the loop until accuracy stabilises.
   Watchouts: Privacy, redaction, and auditability; versions proliferate over time.
7. Energy and cooling optimisation
   What it is: Control systems that tune setpoints in real time to reduce energy use.
   Why it works: Continuous small improvements add up across facilities.
   How to start: Deploy in a single plant or server room; validate savings against a weather-normalised baseline.
   Watchouts: Sensor drift; safety interlocks; rebound effects.
8. Predictive maintenance
   What it is: Models that forecast failure based on vibration, temperature, and usage.
   Why it works: Planned downtime is cheaper than surprise downtime.
   How to start: Instrument critical assets; collect failure labels; start with threshold rules and evolve.
   Watchouts: Rare-event modelling; false positives that erode trust.
9. Risk and fraud
   What it is: Real-time scoring of transactions and applications.
   Why it works: Catches anomalies faster than rule-only systems.
   How to start: Blend supervised models with graph features; tune for explainability for regulators.
   Watchouts: Concept drift and adversaries who adapt; fairness and appeal processes.
10. Software engineering copilots
    What it is: Code generation and refactoring assistants.
    Why it works: Accelerates boilerplate and test writing; improves consistency.
    How to start: Enable in non-critical repos; track cycle time and defect escape.
    Watchouts: Licensing of training data; secret leakage; reviewers becoming complacent.

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