A marketing team that can use AI well, not just often
AI tools are easy to access; using them well is not. This training gives a marketing team the prompting skill, the brand-voice discipline, the governance habits and the prompt library to use AI effectively on their own work — and the policy that keeps it safe. Built around your business, not a generic course.
Few workflows, run every week
- Use cases
- Workflows
- Adoption
Access to AI is universal. Skill is not.
Every member of a marketing team now has access to capable AI models. What almost none of them have is the training to use those models well in their actual work — and the governance to use them safely. The result is the familiar pattern: one or two enthusiasts producing inconsistent output, the rest of the team avoiding the tools, and no shared standard for what good use looks like.
The skill gap is specific and fixable. Most marketers prompt reactively — pasting a question into a chat window with no context — and get the generic, hedged output that has given AI a mixed reputation. Better prompting is not mysterious; it is a discipline of supplying context, structure and constraints. It can be taught in a morning to a team that then uses it for years.
The governance gap is the more dangerous one. Without a shared rule about what may enter a public model and what must be reviewed before publishing, the team's enthusiasm creates commercial and reputational risk. A short, usable policy — one page, written for marketers — closes that gap, and the training is where it gets agreed and adopted.
What changes for your business
- A team that prompts effectively — getting reliable, on-brand output rather than generic prose.
- A shared brand-voice prompt library that keeps output consistent across people and over time.
- A one-page AI policy the team actually follows, covering data, review and publishing.
- Playbooks for the team's own most frequent tasks, ready to use the next day.
- Capability that lives in the team and its artefacts, not in one enthusiastic individual.
Diagnose → plan → hand over
Diagnose
Plan
Hand over
What the training covers
Prompting for marketing tasks
Getting reliable output for content, research, variants, summaries and reporting — using the team's own work as the exercises, not generic examples.
Brand voice and consistency
How to give a model the context it needs to sound like your business every time — the tone reference, the proposition, the segments — so output is consistent across people.
Governance and safety
What may and may not go into a public model, the review step for anything external-facing, and a one-page AI policy written for marketers rather than lawyers.
Prompt library and playbooks
A working set of saved prompts and playbooks for the team's most frequent tasks — assembled during the session and left behind as a maintained asset.
Adoption and sustainability
How the team builds, maintains and shares its own prompt library so the capability survives staff changes rather than living in one person's chat history.
Who this is right for
- Marketing teams of one to ten people who use or could use AI in their daily work.
- Businesses where AI use has started but is inconsistent or ungoverned.
- Teams that want a shared standard for what good AI use looks like.
- Leadership teams that need a usable AI policy adopted, not just written.
- Businesses that want capability in the team rather than dependence on a consultant.
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 prompting is a discipline, not a trick
Good prompting is not about finding a magic phrase. It is about supplying the model with what it needs to produce useful work: the context (who the audience is, what the proposition is, what tone to use), the structure (what format the output should take, what sections it needs), and the constraints (what to avoid, what claims must be defensible, what length to hit). When those are supplied, output quality improves immediately and dramatically.
Most marketers learn prompting by trial and error in a chat window, which is why results are inconsistent. A morning of structured practice — on the team's own content, with feedback — turns it into a repeatable skill. The exercises use the team's actual work, so the learning is directly applicable rather than theoretical.
The training also builds the habit of saving what works. A prompt that produced a good product description becomes a reusable template; a prompt that produced a good report summary becomes a standard. Over a session, the team assembles a library that outlives any individual.
The policy that gets adopted, not filed
Most AI policies fail because they are written for auditors, not for the people who have to follow them. A policy no one reads is worse than none at all, because it creates a false sense of control while the team does what it was going to do anyway. The training produces a policy written the other way: one page, in plain English, covering the three things that matter — what may not enter a public model, what must be reviewed before publishing, and who is responsible.
The policy is drafted during the session, with the team in the room, which is why it gets adopted rather than filed. The people who will follow it helped write it, understand the reasons behind each rule, and know where the boundary is. That is the difference between a governance document and a governance habit.
For a Chartered Marketer delivering the session, the policy also reflects professional practice and UK data obligations, not vendor marketing. The aim is a standard a business can defend to a board, a customer or a regulator without embarrassment.
Capability that survives staff change
The single most common failure of AI adoption in SMEs is that it lives in one person. The enthusiast builds a set of prompts in their own chat history, becomes the de facto AI person, and when they leave the capability leaves with them. The team is back to square one, and the business has lost months of accumulated know-how.
The training is designed to prevent that. The prompt library, the playbooks and the policy are artefacts the team owns together, stored where everyone can reach them and maintained as part of the routine. New starters inherit a working system rather than starting from scratch. The capability is institutional, not personal.
This is also why the training is practical rather than evangelical. A team that understands where AI genuinely helps and where it does not — and has the artefacts to use it consistently — is worth more than a team that is merely enthusiastic. The goal is judgement and habit, not hype.
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.
Marketing thinking in short form
Strategy, SEO and paid search explained in a couple of minutes on Instagram and TikTok.
Where to go next
A few things worth reading — and the pages most people move to from here.
Related insights
- AIAI in SME Marketing: Where It Genuinely Helps and Where It Wastes MoneyA practical view of where AI earns its place in a small marketing function.
- AIThe AI Operating Model: What a Small Marketing Team Should Automate FirstWhich marketing tasks to hand to AI, which to keep human, and how to govern the difference.
- AIUsing AI for Content Without Destroying Your Brand or Your SEOThe review workflow that makes AI-assisted content faster without making it generic.
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