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AI Marketing

AI in your marketing function, implemented properly

Most SMEs are either ignoring AI or using it badly. I build the operating model in between: a small number of AI workflows that genuinely return hours to your team, with governance that protects your brand, your data and your search visibility.

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The problem

The problem is not the technology. It is the absence of a plan.

Two failure modes dominate. The first is paralysis: the leadership team knows AI matters, nobody owns it, and the function carries on doing manual work that a model could have done before lunch. The second is scattergun adoption: six subscriptions, one enthusiastic user, generic content published without review, and no measurable change in output or enquiries.

Both come from the same gap. AI is being treated as a tool purchase rather than an operating decision. Nobody has asked which specific tasks in the function are repeatable enough to delegate, who reviews the output, what data may be used, and what the returned hours are meant to be spent on instead.

There is a commercial risk too. Unreviewed AI content sounds like everyone else's, which is the fastest way to lose credibility with a technical buyer, and mass-produced pages are exactly what search engines have spent two years learning to discount. Done well, AI is a compounding advantage. Done casually, it quietly erodes the trust your marketing exists to build.

Outcomes

What changes for your business

  • A written AI operating model: which tasks are AI-led, human-led or hybrid.
  • Five to fifteen hours a week of specialist time returned to higher-value work.
  • Faster campaign and content turnaround without dropping editorial standards.
  • A usage policy that protects customer data, brand voice and search visibility.
  • A team that is genuinely capable, not dependent on one enthusiastic user.
Scope

What the engagement involves

AI opportunity audit

Every recurring marketing task mapped by frequency, time cost, risk and suitability for AI — so effort goes where the return is provable rather than where the hype is loudest.

Workflow build

The prompts, custom assistants, templates and integrations that deliver the shortlisted tasks reliably: research and synthesis, content drafting, keyword and intent clustering, ad variants, CRM data hygiene and reporting narrative.

Content and brand guardrails

A tone and evidence standard every AI-assisted asset must meet, with named human review before anything customer-facing is published.

Governance and policy

A short, usable AI policy covering approved tools, prohibited data, UK GDPR obligations, disclosure and record-keeping — written for a marketing team, not a legal department.

Enablement and measurement

Hands-on training for your team, then measurement against hours saved, output volume, cycle time and — the number that matters — cost per qualified enquiry.

Fit

Who this is right for

  • SMEs with a small marketing team producing less than the business needs.
  • Leaders who want AI adopted deliberately, with governance, not experimentally.
  • B2B, industrial and SaaS businesses where technical accuracy is non-negotiable.
  • Retail and ecommerce teams producing high volumes of product and campaign content.
  • Businesses that have bought AI tools and seen no measurable change yet.
In detail

How this works in practice

The detail behind the headline: how the work is structured, what it depends on and how progress is judged.

Where AI genuinely earns its place in an SME marketing function

The tasks where AI reliably pays share one characteristic: the work is high-volume, structurally repeatable, and cheap to verify. Research and synthesis is the clearest example — competitor and market scanning, summarising fifty customer calls into recurring objections, condensing technical documentation into buyer-readable language. A specialist doing this manually spends days on inputs rather than decisions.

The second cluster is drafting at scale: first-draft articles a specialist then corrects, ad and email variants for testing, product descriptions across a large catalogue, sales-enablement one-pagers per segment. The third is classification and structure: clustering keywords by intent, tagging enquiries by theme, cleaning and de-duplicating CRM data, turning reporting exports into a written commentary a board can read.

What consistently does not work is delegating judgement. Positioning, pricing narrative, which segments to prioritise, whether a claim is defensible, what a qualified enquiry is worth — a model has no commercial exposure and no accountability for those calls. The operating model I write makes that boundary explicit, because most AI failures in marketing are a boundary problem rather than a capability one.

Building AI workflows that survive contact with a real team

A workflow only sticks when it is faster than the habit it replaces. So each one is built as an artefact rather than a conversation: a saved assistant with your context loaded, a template with the inputs named, a defined output format, and a review step with a person's name against it. Ad-hoc prompting produces inconsistent quality and no institutional memory — the moment the enthusiastic user is on holiday, output stops.

The context layer does most of the heavy lifting. Models perform dramatically better when they have your proposition, segments, tone reference, product specifications, objection library and past best-performing assets available to them. Assembling that once is usually the single highest-return hour in the whole engagement.

Then it is deliberately narrowed. I would rather leave an SME with four workflows that run every week than twenty that impress in a workshop and are abandoned within a month. Adoption, not sophistication, is what determines the return.

Governance, data and quality control that a board can sign off

Three risks need managing in writing. Data: what may never be pasted into a public model — customer personal data, commercially sensitive pricing, unreleased product information, anything under NDA. Accuracy: models produce fluent, confident, wrong statements, which in technical and regulated markets is a commercial liability rather than an inconvenience. Brand: unguided output converges on the same bland register your competitors are publishing.

The controls are unglamorous and effective. An approved-tools list with business-tier accounts so your inputs are not used for training. A prohibited-data list. A rule that no external-facing asset publishes without a named human reviewer who is competent to verify the claims. A tone reference supplied to the model every time. A simple record of where AI was used, so questions can be answered later.

This is also where formal grounding matters. Member of the Chartered Institute of Marketing and an MSc in Digital Marketing Management means AI recommendations here are tested against marketing theory, UK data-protection obligations and commercial reality — not against whichever tool is trending this week.

AI, search and the visibility question nobody asked five years ago

AI has changed discovery as much as production. A meaningful share of buyers now ask ChatGPT, Gemini, Copilot, Perplexity or Google's AI Overviews before they ever click a blue link, and those systems summarise rather than list. If your business is not the source they synthesise from, you are invisible at the exact moment a buyer is forming a shortlist.

That makes AI adoption and AI search visibility two halves of one strategy: use AI to produce genuinely expert, well-structured content faster, then structure that content so answer engines can quote it. The companion service page on AI search visibility covers the second half in detail — entity clarity, question-led structure, schema, citable evidence and measurement.

The businesses that will win the next five years are not the ones with the most AI subscriptions. They are the ones who used AI to publish more genuine expertise than their competitors could, and made sure the machines summarising their market had their name in front of them.

Free playbook

The AI Marketing Playbook for SMEs

Everything in these articles, consolidated into a nine-page playbook you can work through with your team — the operating model, the workflows worth building first, and the guardrails that keep AI from diluting your brand.

  • The three-layer AI operating model for a small team
  • The first five workflows to build, in order
  • AI search visibility (AEO) and how to measure it
  • Governance, accuracy and brand-voice guardrails
  • A 90-day adoption plan and scorecard

PDF · 9 pages · your details are used to send the playbook and nothing else.

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Questions

Frequently asked questions

What does an AI marketing consultant actually do?
I decide where AI belongs in your marketing function and where it does not, then implement it. In practice that means auditing every repeated marketing task you run, identifying which are genuinely suited to AI (research, synthesis, first drafts, clustering, classification, reporting narrative, personalisation at scale), building the prompts, assistants and workflows that do them reliably, training your team to use them, and putting review and brand-governance steps around anything customer-facing. The output is not a set of tool licences. It is a documented AI operating model: which tasks are automated, who reviews what, which data may be used, and how many hours a week the function gets back.
Will AI-generated content damage our SEO or our brand?
Unreviewed AI content will do both. Google's guidance targets unhelpful, mass-produced content regardless of how it was made, and generic AI prose reads exactly like everyone else's — which is fatal in technical B2B and considered retail purchases. The workflow I implement uses AI for scale (research, outlines, variants, summarisation) and humans for the parts that create trust: real specifications, real customer language, real commercial judgement and a named expert reviewer. Content produced that way outperforms both pure-AI output and slow manual publishing.
How much time and money does AI realistically save an SME marketing team?
For the SME functions I have worked in, the honest figure is five to fifteen hours a week of specialist time returned, concentrated in research, first-draft content, reporting write-ups, campaign variants and sales-enablement material. That usually converts into more published output and faster campaign turnaround rather than a smaller team. The cost is modest — typically £20–£60 per seat per month plus setup — so the risk is not spend, it is wasted attention on tools nobody adopts.
Which AI tools should we actually be using?
Fewer than you think. Most SMEs need one strong general model with a business workspace, whatever AI is already inside their CRM and ad platforms, one research or search-visibility tool, and nothing else until those are used properly. I stay deliberately tool-agnostic: I recommend against buying anything until the task, the reviewer and the measurement are defined, because unused licences are the most common AI cost in SMEs.
How do you keep AI use safe, compliant and on-brand?
With a written policy your team can follow: which data may never be entered into a public model, which tools are approved, what must be human-reviewed before publishing, how AI-assisted work is recorded, and how customer data is handled under UK GDPR. Alongside it sits a brand and tone reference the models are given every time, so output sounds like your business rather than a chatbot.
Do you use AI in your marketing consultancy work?
Yes — deliberately, and I help clients do the same. AI is used for research and synthesis, keyword and intent clustering, first drafts that a specialist then corrects, campaign variants and reporting commentary, all under human review with a written data and brand policy behind it. It is never used for the parts that carry commercial risk: positioning, pricing narrative, segment priorities or any claim that has to be defensible. I also work on the other half of the AI shift — making sure your business is the source ChatGPT, Google AI Overviews, Gemini and Perplexity cite when a buyer asks who the credible options are, because a growing share of shortlists are now formed inside an assistant rather than on a search results page.
Why should I use you as my marketing consultant rather than an agency?
An agency sells you delivery. I sell you judgement. Before anyone writes an ad or a blog post, someone has to decide which segments you are targeting, what your proposition is, which channels deserve budget and what a qualified enquiry is actually worth. That is the work that decides whether the delivery pays for itself. I do that work first, in writing, then either brief your existing agency properly or build the plan in-house — and because I have no media to sell you, there is no incentive for me to recommend spend you do not need.
What makes you different from other Fractional CMOs and marketing consultants?
Three things. First, a client-side operating background rather than a pure agency one: I have run marketing inside an industrial B2B manufacturer, dealing with technical buyers, distributor networks, long sales cycles and a board that wants commercial numbers rather than impressions. Second, formal grounding — Member of the Chartered Institute of Marketing (MCIM) and an MSc in Digital Marketing Management, so recommendations are based on tested frameworks rather than whatever is trending on LinkedIn. Third, I work across B2B, retail and SaaS, which means the retail pricing and merchandising discipline informs the B2B work and the SaaS retention thinking informs both.
How much does a marketing consultant cost compared with hiring a Marketing Director?
A full-time Marketing Director in the UK typically costs £70,000-£110,000 plus employer's NI, pension, recruitment fees, holiday and the risk of a bad hire — realistically £100,000+ a year all-in before they have spent a penny on marketing. Consultancy and Fractional CMO retainers give you the same seniority for one or two days a week, at a fraction of that cost, with no notice period and no recruitment risk. For most SMEs turning over £1m-£20m that is the difference between having senior marketing judgement and having none.
Do you work with B2B, retail and SaaS businesses?
Yes — all three, and the differences matter. B2B work centres on pipeline: proposition clarity, technical content, sales and marketing alignment, cost per qualified enquiry. Retail and ecommerce work centres on unit economics: contribution margin after ad spend, repeat purchase rate, lifetime value and the seasonality of demand. SaaS work centres on efficient acquisition and retention: activation, trial-to-paid conversion, churn and payback period. The strategic method is the same; the metrics I hold the plan to are different.
How quickly will I see results from working with a marketing consultancy?
You get clarity in the first two to three weeks: a written diagnosis of where marketing is losing money, what to stop and a prioritised plan. Quick operational wins — tracking that actually works, tightened paid search, fixed conversion paths, a CRM that reports honestly — typically land inside 30 to 60 days. Compounding channels such as SEO and content usually show meaningful movement in three to six months, and that is exactly why the plan sequences fast wins first: they fund the patience the slower channels require.
Will you replace my team or agency?
No. The aim is to make what you already have work harder. In most engagements your team and your agencies keep delivering; what changes is that they receive clear priorities, a proper brief and a measurement framework, and someone senior holds the whole thing to commercial outcomes. Where a supplier genuinely is not performing, I will tell you plainly and help you replace them — but replacement is a conclusion, not a starting assumption.
How do you measure success, and how am I kept accountable to it?
Every engagement is tied to commercial metrics agreed up front: qualified enquiries, cost per qualified enquiry by channel, pipeline value, conversion rate and, where the data allows, revenue and contribution. You get a monthly review pack you can put in front of a board, showing what was done, what it produced and where the next pound of budget should go. If a channel is not paying for itself, you will hear it from me before you have to ask.
What does the first conversation involve, and is there any commitment?
It is a one-hour call, free, with no pitch deck. We cover your growth target, how you sell today, what marketing is currently producing and where the obvious gaps are. You leave with an honest view on whether consultancy, a Fractional CMO retainer, a defined project or nothing at all is the right next step. There is no obligation, and I will say so directly if I do not think I am the right partner for your situation.
Next step

Put AI to work in your marketing function

A 30-minute discovery call to understand your targets, your current marketing and whether I'm the right partner for the next stage.

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