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AI Marketing Adoption Framework
The AI Marketing Adoption Framework is a seven-stage model for introducing AI into marketing in a controlled way: discover where AI could help, assess which processes suit it, prioritise, pilot, measure, scale and govern.
The model
- 1Discover
- 2Assess
- 3Prioritise
- 4Pilot
- 5Measure
- 6Scale
- 7Govern
Developed by Markaigen · Updated
What each part means
- Discover
- Where could AI help us? List the tasks, bottlenecks and decisions in your marketing where speed, volume or analysis is the problem.
- Assess
- Which processes are suitable? Check each candidate for clear inputs, available data, tolerable error cost and a person who owns the outcome.
- Prioritise
- Which opportunities have the highest value? Rank by impact and effort, and pick a small set to start with.
- Pilot
- How do we test them? Run a time-boxed pilot with a baseline, a success metric and a named decision date.
- Measure
- Did AI actually improve the process? Compare against the baseline on time, output, quality and cost, not on enthusiasm.
- Scale
- How do we deploy it across the organisation? Document the workflow, train the team, connect the tools and set service levels.
- Govern
- How do we control quality, privacy and risk? Assign owners, review cadence, disclosure rules and a way to switch things off.
How to use it
- Find the stage you are in today for each AI initiative; most teams are at different stages for different initiatives.
- Answer the stage question with evidence before you move on, and use the exit criteria as a gate.
- Link each stage to its tools: prioritisation for stage three, experimentation for stages four and five, governance for stage seven.
Where are you in the adoption cycle?
Choose a stage to see what to answer, what to do and when you are done with it.
Discover
The question to answer Where could AI help us?
What to do
- Collect tasks that are slow, repetitive or data-heavy.
- Ask the team where they already use AI tools on their own.
- Note where decisions wait for analysis.
You are done when
- You have a list of at least ten candidate tasks with an owner each.
Assess
The question to answer Which processes are suitable?
What to do
- Check inputs, data and error cost per candidate.
- Mark legal, privacy and brand constraints.
- Drop candidates without a clear owner.
You are done when
- Each candidate has a short fact sheet: input, output, data, risk, owner.
Prioritise
The question to answer Which opportunities have the highest value?
What to do
- Score impact and effort with the prioritisation framework.
- Pick two or three quick wins and one strategic project.
- Park the rest with a review date.
You are done when
- A ranked shortlist exists and leadership agrees with it.
Pilot
The question to answer How do we test them?
What to do
- Write a hypothesis and measure the baseline first.
- Run for a fixed period with real work, not demos.
- Keep a human check on every output that leaves the team.
You are done when
- The pilot ran for its full period and data was collected as planned.
Measure
The question to answer Did AI actually improve the process?
What to do
- Compare time, output, quality and cost with the baseline.
- Count errors and rework, not only speed.
- Decide: scale, adjust or stop.
You are done when
- A written decision with numbers exists for each pilot.
Scale
The question to answer How do we deploy it across the organisation?
What to do
- Document the workflow, prompts and checks.
- Train every user and name a process owner.
- Connect the tools to the systems people already use.
You are done when
- The workflow runs without the pilot team and meets its service level.
Govern
The question to answer How do we control quality, privacy and risk?
What to do
- Set review cadence, disclosure rules and escalation paths.
- Log what AI produced and who approved it.
- Keep a way to pause or switch off any automation.
You are done when
- Owners, reviews and logs are in place and were used at least once.
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