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AI Readiness Audit

Know whether your marketing is ready for AI, before you spend on it

Most SMEs buy an AI tool before they know whether their data, team and governance can support it. This audit answers that question first — what is genuinely ready, what is not, where the real risk sits, and what a sensible 90 days looks like. It is the entry-level AI engagement and the one that stops expensive mistakes.

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The problem

Buying the tool is the easy part. Being ready to use it is not.

The pattern is familiar: a leadership team reads about AI, buys a subscription, and hands it to whoever showed most interest. Two months later the licence is dormant, the enthusiast has moved on to something else, and nothing measurable has changed. The tool was never the constraint — the constraint was data quality, team confidence, a governance gap, or the simple absence of a decision about which tasks AI should touch.

The opposite failure is just as common and more dangerous: AI gets used enthusiastically with no policy, no review and no data boundary. Customer details pasted into a public model, unverified claims published as fact, generic content that erodes the brand, and no record of what was generated or by whom. The risk is commercial and reputational, and it usually goes unnoticed until something goes wrong.

What is missing in both cases is an honest assessment before the spend. Which tasks are repeatable enough to delegate to a model? Is the data those tasks need actually clean and permitted to use? Who will review the output? What must never be entered into a public model? An AI readiness audit answers those questions in writing before budget is committed.

Outcomes

What changes for your business

  • A scored picture of your readiness across tools, data, skills, risk, governance and operating model.
  • A risk register naming what could go wrong and how to prevent it — in plain language a board can act on.
  • A prioritised 90-day roadmap: what to do first, what to leave, and what to stop.
  • A clear answer on whether to accelerate, pause or consolidate your current AI activity.
  • A document your own team can execute without an ongoing dependency.
Scope

What the audit covers

Tool and usage landscape

What is bought, what is genuinely used, what is dormant and what is missing — so spend is aligned to actual adoption rather than enthusiasm.

Data and content readiness

Whether the inputs AI needs — customer records, product specs, content archives, CRM notes — are clean, accessible and permitted to use under your data obligations.

Skills and confidence assessment

Who on the team can use a model well, where the capability gap is, and what training would close it — assessed through interview, not assumption.

Brand and compliance risk review

What customer and commercially sensitive data must never enter a public model, whether that rule exists today, and how it should be enforced in practice.

Governance gap analysis

Whether there is a written AI policy, an approved-tools list, a review step and a record of AI-assisted work — and what a usable version of each looks like for a marketing team.

Prioritised 90-day roadmap. The findings turned into a ranked plan: quick wins, governance essentials, the handful of workflows worth building, and what to leave alone — ordered by return and effort.

Fit

Who this is right for

  • Leadership teams deciding whether and how to invest in AI, who want an independent view first.
  • Businesses where AI use has started in pockets with no governance, and risk is unclear.
  • Marketing teams with limited AI experience who need to know where to start safely.
  • Businesses that have bought AI tools and seen no measurable change.
  • Any SME that needs to show a board or owner that AI spend is justified and safe before committing.
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.

Why readiness fails before the tool does

AI adoption in an SME marketing function rarely fails because the model is not capable enough. It fails because the inputs are poor — dirty CRM data, content that lives in heads rather than files, product specifications scattered across PDFs nobody can find — or because nobody owns the output once it is generated. A model given incomplete context produces confidently wrong work, and a team with no review step publishes it.

The second failure mode is governance absence. Without a written rule about what may be pasted into a public model, people default to convenience: customer emails, pricing, unreleased product detail. Most of the time nothing happens. The time something does, the commercial cost dwarfs the entire AI budget. Readiness means knowing where that line is before it is crossed, not after.

The audit exists to surface these issues while they are still cheap to fix. A data gap found in week one costs an afternoon to document; the same gap found three months into an AI project costs a stalled initiative and lost credibility with the board.

What a usable AI policy looks like for a small team

Most AI policies written for enterprise are unreadable and unused. A usable policy for a marketing team of one to ten people fits on a page: an approved-tools list, a prohibited-data list, a rule that no external-facing asset publishes without a named human reviewer, and a simple record of where AI was used. That is enough to govern 90% of the risk without strangling the benefit.

The audit assesses whether one exists, whether the team knows it, and whether it matches what people actually do. A policy nobody follows is worse than none at all, because it creates a false sense of control. Where the gap is found, the roadmap includes a draft written for marketers rather than lawyers.

This is also where credentials matter. A Member of the Chartered Institute of Marketing with an MSc in Digital Marketing Management assesses AI governance against UK data-protection obligations and marketing practice, not against a vendor's whitepaper.

The roadmap: sequencing return against effort

The 90-day roadmap is deliberately narrow. Most SMEs can absorb three to five changes in that window — any more and adoption collapses under its own weight. The audit ranks candidates by the ratio of hours returned to effort and risk, then sequences the governance essentials (a data policy, a review step) before the productivity workflows, because the governance is what makes the workflows safe to keep.

A typical first 30 days establishes the policy, cleans or documents the one or two data sources that matter most, and ships one low-risk workflow the team will actually use. Days 30 to 60 build the second and third workflows and measure the hours returned. Days 60 to 90 review what stuck, retire what did not, and decide whether to continue independently or bring in help for the harder integrations.

The roadmap is written so your team can run it. Some businesses use the audit as the start of a longer engagement; many do not. Either is the right answer, and the audit says so honestly.

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Questions

Frequently asked questions

What is an AI readiness audit?
A fixed-scope assessment of how ready your marketing function is to adopt AI safely and usefully. I score six areas — current tool use, data quality and access, team skills and confidence, brand and compliance risk, governance gaps, and the operating model around AI — then turn the findings into a prioritised 90-day roadmap. The output is a written report and a working session to walk through it, not a software trial or a tool recommendation list.
How is this different from the AI marketing engagement?
The audit tells you what to do and in what order; the engagement does it. An audit is the right starting point when leadership wants an independent view before committing budget, when the team is divided on whether AI is worth it, or when adoption has started in pockets and nobody knows whether it is safe. If you already know you want workflows built, go straight to the AI marketing engagement. If you are not sure, start here.
What does the audit actually cover?
Six dimensions. Tool landscape: what is bought, what is used, what is dormant and what is missing. Data: whether the inputs AI needs — customer records, content, product specs, CRM notes — are clean, accessible and permitted to use. Skills: who on the team can use a model well, who cannot, and where the confidence gap is. Brand and compliance: what customer or commercially sensitive data must never enter a public model, and whether that rule exists today. Governance: whether there is a written AI policy, an approved-tools list and a review step. Operating model: which repeated tasks are genuinely suited to AI and which should stay human. Each is scored, evidenced and prioritised.
How long does it take and what do we get?
Typically two to three weeks from kick-off. I review your tooling, sample your data and content, interview the people who would use or be affected by AI, and assess the governance gaps. You receive a written report with scored findings, a risk register and a 90-day roadmap ordered by return and effort, plus a 60-minute walkthrough session with your leadership team. The roadmap is designed to be actionable by your own team — you do not need to retain me to execute it.
We already use ChatGPT — do we still need an audit?
Often, yes. Ad-hoc use by one or two enthusiasts is the most common pattern I see, and it is also the riskiest: no data policy, no review step, no record of what was generated, and no institutional memory when that person leaves. The audit turns scattered experimentation into a governed operating model, and tells you honestly which of the existing usage is worth keeping and which is quietly creating risk.
Is the audit a sales pitch for a bigger engagement?
No. The roadmap is written so a competent in-house team can execute it without me. A meaningful minority of audits conclude that the business should slow down on AI rather than speed up, and I say so plainly when that is the answer. Where the audit surfaces work you want help delivering, the AI marketing or automation engagements are there — but the audit's job is to give you an honest picture, not to manufacture a project.
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.
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