Useful AI without the theatre
Every marketing team has been told to use AI. Very few have been told what for. I help teams work out which parts of their process genuinely get better with a model in the loop, which get measurably worse, and how to tell the difference without betting the brand on finding out.
- AI consultancy for marketing teams covers a workflow audit, tooling and setup, explicit quality standards for what gets published, and training the team to run the new workflows independently.
- AI consultancy suits small marketing teams with a wide remit, content teams under volume pressure, and regulated businesses that need guardrails before adopting AI.
- An AI consultancy engagement runs a one-week diagnosis, a pilot on two or three workflows from week two to week four, and standardisation in month two.
- The AI workflow audit and pilot are a fixed-fee project, and AI enablement training is priced by team size.
- The AI operating system I built for a regulated financial firm’s marketing function has 53 task-specific skills, 50 eval cases and a 36-check quality gate before anything publishes.
Why most AI rollouts in marketing do not stick
The pattern is consistent enough to be predictable: a burst of enthusiasm, a spike in output, a quiet decline in quality, and six months later nobody is using it for anything that matters.
- Tools without a job
- Licences for everyone and no decision about what they are for. Usage spikes, plateaus, and dies, and the only measurable outcome is the invoice.
- Volume as the only win
- More content, faster, is the easiest thing to measure and usually the least valuable. Ten times the output at half the quality is not a productivity gain.
- No quality floor
- Nobody has defined what “good enough to publish” means, so the standard drifts downward one acceptable-seeming piece at a time until the brand sounds like everyone else.
- Nobody owns the risk
- Particularly in regulated categories, where a fluent, confident sentence that turns out to be wrong is a compliance problem rather than an embarrassment.
Who is AI consultancy for?
Teams that want the actual gains and can do without the pilot-project theatre.
Teams told to “use AI”
A mandate from above, no brief, and no idea which parts of the job it should touch.
Small teams, wide remit
Two or three people covering what ought to be six roles. This is where AI saves the most time.
Regulated businesses
Where the upside is obvious and the failure mode is a compliance incident, so the guardrails have to come first.
Founders doing their own marketing
You are the strategist, the writer and the analyst. Getting the workflow right buys back the most time.
Content teams under volume pressure
Being asked to publish more without more people, and wanting to do it without the quality collapsing.
Teams that already tried
Adoption stalled, output got blander, and people quietly went back to the old way. That is a workflow problem rather than a tooling one.
What does AI consultancy include?
Each workstream is designed to move a specific metric, not to pad a report.
Workflow audit
I map how the work actually gets done now (the real process rather than the documented one) and mark where a model in the loop would help, where it would hurt, and where it would just add a review step. Most teams are automating the wrong half.
Tooling and setup
Which tools, configured how, with what context. Most of the value is in the setup rather than the subscription: the prompts, the source material, and the connections into the systems you already use.
Quality standards
An explicit floor for what gets published, who reviews what, and which categories of work never go out without a human decision. This is what stops the slow drift toward generic.
Enablement
Training your team to do this themselves, with worked examples from your own work rather than a generic deck. The goal is that you do not need me afterwards.
How does the work run?
Four phases with real durations. Nothing here is a proprietary framework. It is the order the work has to happen in.
Diagnose
How the team works now, what it spends time on, and where the genuine constraints are. Often the answer is that AI is not the highest-value fix available, and I will say so.
Pilot
Pick two or three workflows and rebuild them properly, with a quality bar agreed up front and a way to tell whether it worked. Narrow and real beats broad and theoretical.
Standardise
Turn what worked into something repeatable: documented prompts, review steps, and the standards that keep quality from sliding once the novelty wears off.
Hand over
Train the team, document the decisions, and leave. Check in periodically as the tools change, because they will.
How much does AI consultancy cost?
Most work starts with a fixed-fee audit. Scope moves the number, not seniority: you get me either way.
Audit and roadmap
From £3,000
I look at what you are running now, find what is structurally broken as opposed to merely underperforming, and hand back a prioritised plan you could execute without me. Most engagements start here.
Ongoing retainer
From £4,000
Strategy plus execution across the channels that matter for your stage. Two or three disciplines working together rather than all five at once.
Advisory
£450 per day
For teams with people in place who need someone senior to pressure-test the plan, review the work, and say the unpopular thing.
What results has AI consultancy produced?
I have built and run the AI operating system for a regulated financial firm’s marketing function, including the skills, the eval suites, the governance layer and the quality gate that decides what is actually good enough to publish.
- Task-specific skills covering research, drafting, SEO, design, and compliance review
- 53
- Eval cases across the six skills where a bad prompt change would do the most damage
- 50
- Checks a piece passes before it can publish: 28 diagnostic, 8 that block it outright
- 36
Questions people actually ask
Including the ones about money.
Written by
Oliver Carding
Marketing consultant in Manchester. Previously Head of Marketing at Improbable and Senior Digital Marketing Lead at eBay; founded CoinJournal and CryptoKaiju. Career, press and references.
Other services
Most engagements use two or three together. A strategy sets the direction; the rest do the work.
Digital marketing strategy
Audience research, channel planning, go-to-market, and measurement frameworks. The decision-making layer that tells you what to do, in what order, and how you’ll know it’s working.
Search engine optimisation
Technical SEO, keyword strategy, content optimisation, and authority building. I fix the structural problems that stop search engines finding you, then build the visibility that compounds over time.
Content marketing
Content strategy, thought leadership, newsletters, and distribution. The aim is content that answers the questions your buyers actually have, in places they’ll actually find it, rather than volume for its own sake.
Social media management
Channel strategy, community management, content publishing, and paid social on the channels your audience already uses: X, Discord, Telegram, and LinkedIn.
Paid advertising
Google Ads, Meta advertising, tracking, and bid optimisation. Paid media in crypto is tricky: platform policies shift, audiences are sceptical, and most budgets leak. I stop the leaks.
Digital transformation
Replatforming, measurement infrastructure, and the way a marketing team actually runs. Most problems that look like strategy problems are plumbing problems: the tracking is wrong, the site can’t ship a page, and nobody agrees what a lead is.
AI search visibility
Answer engine optimisation. Being the source an AI cites when someone asks it a question in your category. That is a different job from ranking, and increasingly the one that decides whether you get considered at all.