AI adoption consulting for marketing teams
Practical adoption. Not enterprise transformation.
Our AI adoption consulting is deliberately narrow: marketing, content and revenue teams. We map which workflows are worth changing, pilot one, then write the standards and train the team.
- Marketing and content workflows only — no model building
- Fixed-scope AI readiness assessment as the entry point
- Data handling and EU AI Act documentation inside scope
What we mean by AI adoption consulting
Elsewhere, AI adoption means a board-level programme with a roadmap across every department. We do something smaller: take a team that already produces work and change how a few workflows get done. We are not a management consultancy.
In scope
Repeatable work with a clear input, a clear output and a named sign-off.
Out of scope
Model training, pipelines, custom software. We will say when you need a data engineering firm.
The unit of work
One workflow, one named owner, measured against your team’s own numbers.
Who this is for
Three situations come up repeatedly. If you need a model built or a company-wide programme run, we are the wrong firm and will say so on the first call.
Already using AI, informally
People paste work into a chat window. Nobody knows which prompts are in use, or what data leaves.
Told to “use AI”
Leadership has asked for a plan. Nobody owns it, and fear of choosing badly keeps it stalled.
Agencies delivering client work
You need consistency across accounts, and an answer when a client asks where their data went.
Where AI actually helps a marketing team
We agree what better means before anything is built, and the baseline comes from your team, not an industry figure. The stack: a general-purpose assistant, transcription, translation, somewhere to keep prompts — run daily in our own SEO and content delivery.
Research and briefing
Search results, competitor pages and transcripts into a usable brief.
Drafts, then review
Drafting from a structured brief, not a one-line prompt. Someone senior signs off.
Translation
One campaign into several markets. A native speaker reviews customer-facing copy.
Support triage
Classifying, routing and drafting replies to repetitive enquiries.
Internal knowledge
Tone rules, approval steps, client quirks your team re-explains weekly.
Where it does not help
Judgement calls. Anything carrying professional accountability. Client-facing copy nobody has read. And relationship work — outreach, negotiation, the conversations behind link building — where the value is a person on the other end.
Volume-first AI content is a liability in search, not an advantage, and we say that from the SEO side of the business. The fundamentals Moz documented did not change when drafting got faster. AI earns its place in research and quality control — our AI SEO services.
No review gate
Work goes out unread because the draft looked confident.
No owner
A tool gets bought, enthusiasm lasts a month, the subscription renews.
No baseline
Nobody measured the old way, so nobody can prove the new one is better.
How an AI adoption consulting engagement runs
Four stages, typical rather than promised. One workflow goes first — a department-wide launch leaves you unable to attribute anything. From your side: a decision-maker, the person doing the work, and whoever owns data policy.
Readiness assessment
How the team works, which tools are in use, what data policy exists.
Workflow mapping
Each workflow mapped step by step, review gate marked, baseline agreed.
Pilot one workflow
Live on real work, with a named owner and a prompt library you can edit.
Measure, then roll out
We report against the baseline, then extend to the next workflow or stop.
Governance, data and the EU AI Act
Before a tool goes near your work we write down what leaves your organisation, which vendor receives it, how long it is kept, and whether the terms allow training on your content. Tiers differ sharply.
Then we document practice: which workflows have a review gate, who reviews, and how you would explain which parts were AI-assisted. For a deployer, that record is most of what EU AI Act compliance asks.
We are not lawyers. Classification under the Act sits with your counsel.
What you get
Everything we produce is written down and yours: your team runs these workflows after we stop.
Ongoing support is optional, never a condition. Most teams run the pack themselves.
The handover pack
What AI adoption consulting costs
Work is quoted three ways: a fixed-scope readiness assessment agreed before we start; a project covering mapping and a pilot; or a monthly retainer, only if you want continuing review. Nothing renews automatically.
What moves the number: how many workflows, how many people need training, how many languages, and how documented your process is. Undocumented teams take longer to map, so they cost more.
Our time, the assessment, the maps, the prompt library and the training sit inside the quoted scope. Software does not: you buy subscriptions directly and keep the tools if we stop. We take no commission on anything we recommend; your exact quote comes with the free audit.
Start with the assessment
A short call first; fifteen minutes is usually enough to establish fit. Then the readiness assessment: which workflows are worth changing, where governance stands. About a week, and the document is yours.
If you already have a review gate, an owner and a metric, you do not need us.
Fair questions, straight answers
What does an AI adoption consultant actually do?
Assess how the team works, map which workflows are worth changing, pilot one, then write the standards. No models built, no pipelines engineered — you keep documented workflows and a team that can run them.
How much does AI adoption consulting cost?
Three shapes: a fixed-scope assessment quoted as one figure, a project for mapping and a pilot, or an optional retainer. It moves with workflow count, people trained and languages, and comes with the free audit.
How long before we have a workflow actually in production?
Assessment in the first week, mapping over the next two, then a pilot on real work. We promise no date: unclear ownership, no review capacity or slow procurement stall it.
Will AI replace our marketing team?
No, but it changes what the team spends its hours on. Some task-level work does go: first-pass transcription, mechanical translation, manual report assembly. Judgement does not, so seniors stay on the review gate for content production.
What happens to our data if we use AI tools?
It depends on the vendor and plan: consumer and business tiers differ on retention and on whether inputs train the model. Client data and anything under NDA stays off public models.
Does the EU AI Act apply to us?
Possibly; it turns on your role. A marketing team using commercial tools is normally a deployer, not a provider, and typical uses sit low on the risk classification. Classification belongs to your counsel.
Which AI tools do you recommend?
No single answer. The criteria matter more than the brand: retention and training terms, EU hosting, integration with what you run, and exit cost. We take no commission from any vendor we name.
How do you measure whether adoption actually worked?
Agree the metric before the pilot starts: time to first draft, throughput, rework rate. The baseline is your team’s own performance, not a benchmark — as in a conversion rate test. If it does not pay, we stop.
Find out which workflows are worth changing
Tell us how your team works today. You get the workflows we would change first, the governance questions to settle, and a quote.
Prefer to talk it through? Book a free 15-minute intro call →