The AI automation market has so much hype that most teams freeze. Here are the twelve use cases we have actually deployed for clients in the last twelve months, with real time savings rather than slide-deck claims.

1. AI-assisted customer support triage

An AI reads incoming tickets, classifies them, drafts a reply for the agent, and routes by topic. Time saved: 30-50% of first-response work.

2. Sales call summarisation

AI listens to call recordings, generates structured summaries (pain points, objections, next steps) and pushes them to the CRM. Time saved: 30-45 minutes per call.

3. Lead enrichment and scoring

New leads get auto-enriched from public sources and scored on fit. Reps only call leads above the threshold. Time saved: 60% of SDR time on bad leads.

4. Invoice and document extraction

OCR + LLM extracts line items from supplier invoices and pushes them into accounting. Time saved: 4-6 hours per finance person per week.

5. Content generation for product catalogues

AI drafts product descriptions, SEO meta, and category copy from raw product data. Humans review and refine. Time saved: 70% on catalogue copy work.

6. Email drafting for sales follow-up

AI drafts contextual follow-up emails using prior thread, CRM data and product fit. Time saved: 2-3 hours per rep per day.

7. Meeting note distribution

Auto-generated minutes from Zoom/Meet, sent to attendees with action items extracted. Time saved: 15-20 minutes per meeting.

8. Code review assistance

AI reviews pull requests, flags obvious bugs, suggests improvements before a human reviewer touches the PR. Time saved: 20-30% reviewer time on routine reviews.

9. Customer churn prediction

AI scores customer accounts on churn likelihood weekly. Success teams act early. Outcome: 20-40% reduction in surprise churn.

10. Bug report triage

AI classifies bug reports by component, severity and likely duplicates. Engineering picks up pre-prioritised tickets. Time saved: 1-2 hours of triage daily.

11. Knowledge-base search and answers

Internal AI search across docs, Slack and wikis. Employees stop asking the same questions repeatedly. Time saved: hard to measure, big.

12. Personalised marketing copy

AI generates segment-specific email and ad variants. Marketers test more, faster. Outcome: 15-25% lift on email open and click rates.

The rules that separate AI projects that ship from ones that don't

• Start with a workflow, not a model. Pick an annoying weekly task. Automate that.
• Human-in-the-loop. AI suggests; humans approve. Trust gets built that way.
• Measure time saved, not "AI-powered" claims.
• Build evaluation harnesses. Without evals you cannot tell if a model upgrade is safe.

The teams winning with AI in 2026 are not the ones with the most models. They are the ones with the clearest workflows.

Our AI Automation service ships these workflows end-to-end — see the service page for engagement options.