Put AI to work inside your business.
Beyond experimenting with chat tools: identifying the workflows where AI genuinely helps, then embedding it with the checks a business needs.
Why most AI pilots stall
The technology is rarely the blocker. Pilots stall because the AI sits outside the workflow, so using it is an extra task rather than a faster route through an existing one.
- A tool everyone tried once and nobody opens now
- Output nobody trusts enough to send
- Information trapped in documents no one has time to read
- Enquiries sorted by hand before anyone can act on them
- Knowledge that only exists in one person’s inbox
- No agreed rule for what AI is allowed to decide
How I approach it
Find the workflow, not the use case
I look for steps where a person reads, sorts, drafts or searches — those are where AI pays back first, because the work already exists and can be measured.
Keep a person where judgement matters
AI drafts, classifies and summarises; people approve what leaves the business. Where that line sits is a decision we make deliberately, per workflow.
Ground it in your own information
Answers come from your documents, your records and your policies, so the output reflects how your business works rather than the internet’s average.
What that looks like in practice
Illustrative examples of the work, not client case studies.
Enquiries sorted before anyone reads them
Intent, urgency and likely value identified on arrival, so routing and first response happen immediately.
Documents that read themselves
Key details pulled out of PDFs, forms and emails and written straight into the system that needs them.
A first draft every time
Replies, summaries and reports drafted from your own material, ready for a person to check and send.
Search that covers everything
One place to ask a question across documents, records and past correspondence.
The process
Questions
Is our data safe?
Data handling is agreed before anything is built: what is sent where, what is retained and what stays inside your systems. If a workflow cannot meet that standard, it is not a good candidate.
Do we need a large budget to start?
No. Most useful AI work starts with one workflow, tested against real examples, before anything wider is committed.
What if the output is wrong?
That is expected, which is why review sits in the design. Workflows where an error would be costly keep a person in the approval step.
Do you build models?
No. This is applied implementation using established models, focused on fitting them properly into your business.
Tell me what's frustrating you.
I'll tell you whether I think there's something worth exploring. If there isn't, I'll say so.
