Work I have anchored here
Direct work with enterprise marketing teams at FMCG, financial services and industrial brands. Plus full-spectrum SEO and AI workflow input alongside a multi-office creative and marketing agency.
London
Enterprise marketing, financial services and FMCG teams
AI Consultant
in London.
Practical AI consulting for teams that want useful systems, not a folder full of prompts. For London teams, the value is in making the work specific: where the data comes from, who reviews it and what the first useful version should do.
// London context
London teams usually run multi-agency setups with parallel programmes. The work that lands is the work that sequences, not the work that adds more. Most engagements here are second-opinion advisory for enterprise marketing or hands-on diagnostic for an in-house lead who already knows what they need.
// local proof
What this looks like in London.
Local market read
London buyers shortlist three agencies plus one senior independent for a second opinion. That second-opinion slot is where most of my London work sits.
How I work here
Monthly day on-site for strategy reviews. Two to three day blocks for migration sprints or workshops. Otherwise remote.
Where I sit vs other options
Heavy agency saturation - the established names (Builtvisible, Aira, Distilled-era alumni) cover most of the enterprise market. I sit alongside them, not against.
// 01 - the offer
What you are buying.
I help map the work, choose the first sensible use case, build the workflow and leave the team with clear rules.
What You Are Buying
Senior judgement, then a usable AI workflow.
The work is not a bundle of prompts. It is a decision about where AI belongs, what information it can use, who checks the output and how the team keeps the system tidy after the first version.
Use-case choice
Prompt rules
Review model
Team handover
// 02 - diagnosis
Why London teams call.
The patterns I see most before the work starts. If two of these sound familiar, the first review call is usually worth the time.
AI ideas are too broad
The team can see potential, but nobody has narrowed it into one useful workflow with clear boundaries.
Prompts drift
Outputs change because the inputs, examples and review rules are not documented well enough.
Nobody owns the system
AI sits between marketing, operations and leadership, so decisions slow down or become tool-led.
Risk stops progress
The business needs source checks, human review and sensible limits before AI can become useful.
// 03 - how the work runs
Phases that end in something usable.
I help choose the AI project that is worth doing first, then define what the system should and should not do.
That means mapping the work, setting source rules, building the first workflow and keeping human review where it matters.
The result is a controlled AI process your team can use without guessing the method each time.
// 01 - locate
Find the right AI use case
Start with the repeated work, the risk level and the people who need to trust the output.
// 02 - limit
Set the rules
Define approved inputs, review points, source checks and what the workflow must avoid.
// 03 - build
Create the first workflow
Prompts, examples, handoffs and checks joined into one usable operating model.
// 04 - adopt
Make the team confident
Handover notes and review habits so the system can improve without becoming loose.
// 04 - proof
Past project profiles.
Two examples of the shape this work takes. Real engagements, anonymised. Not every project looks like these, but the discipline is the same.
Example project profile
AI research assistant for a multi-stakeholder marketing team
The team wanted AI support, but every idea was too broad. The first useful project became a research workflow with approved sources, summary rules and human review before anything reached a brief.
Source library
Prompt chain
Brief template
Review checklist
// outcome The team gets a repeatable research pack instead of a loose chat history.
Example project profile
Internal AI policy turned into day-to-day working rules
Leadership needed AI use to move without creating risk. The work became a practical operating guide: what AI can draft, what it cannot touch and where people must check the output.
Use-case register
Risk notes
Approval flow
Team handover
// outcome AI becomes usable because the boundaries are clear.
// 05 - shape check
Where this works, and what you leave with.
// where this works
Use cases
- 01 Choose the first AI use case
- 02 Turn a repeated task into a workflow
- 03 Set rules for sources, review and handover
- 04 Works well for teams with several stakeholders and too many disconnected tools.
// what you leave with
Outcomes
- AI use-case map
- Workflow prototype
- Prompt and source rules
- Team handover notes
// 06 - questions
London AI Consultant FAQ.
Do you work with London teams remotely?
Yes. Most work can run remotely. If a workshop would help, the first step is still a short call to understand the problem and decide whether a session is worth the time.
What does a AI consulting project usually start with?
It usually starts with one narrow problem: a slow process, a technical blocker, a content system that has become messy or an AI idea that needs turning into a controlled workflow.
How quickly can you find the first useful project?
In most cases, the first useful route is visible after a review call and a small sample of the existing process, crawl, content, report or workflow.
// ready when you are
Find the first useful project for your London team.
Book a review call. We will look at the work, the risk and the first sensible step before agreeing anything.
Book a review