How to build an AI-ready marketing team in 90 days
AI readiness is not a technology problem. It is a capability problem. Here is a practical framework for getting a marketing team from curious to competent in three months.
Ninety days is not a long time. But it is long enough to take a marketing team from “we should probably do something with AI” to “we have working systems and know how to build more.”
The key is not trying to do everything. It is sequencing the work so that each phase builds on the last and produces something tangible along the way.
Days 1 to 30: Foundations
The first month is about creating the conditions for adoption, not jumping into tools.
Audit your workflows. Map the repeatable processes your team runs. Content production, reporting, campaign setup, research, brief processing. You do not need a detailed process map. You need a list of workflows with a rough sense of how much time each one takes and how often it runs.
Identify your first workflow. Pick one that is painful, frequent, and visible. This is the workflow you will automate first. It needs to be something the team will notice when it improves.
Assign ownership. Decide who will own AI adoption in the team. This person does not need to be the most technical. They need to be the most committed to making it work and senior enough to make decisions about process changes.
Assess readiness. Run through the AI readiness checklist. If there are gaps — undocumented processes, no time for learning, unclear success criteria — address them in this first month. These are not blockers. They are prerequisites.
Set up the environment. Get access to the tools you will use. For most marketing teams, this means Claude or Claude Code, accessible from a browser-based environment that does not depend on internal IT. This is a logistical step that is often underestimated. If people cannot access the tools when they sit down to learn, momentum stalls immediately.
Days 31 to 60: Build
The second month is where the team gets hands-on.
Structured training. This is the point where formal training makes the most difference. Four to six sessions of 90 minutes each, focused on building real workflows with the tools the team will actually use. Not theory. Not slides. Hands-on work in a live environment.
Build the first workflow. During training, the team should build the workflow identified in month one. By the end of the training period, this workflow should be functional — not a prototype, but a working system the team can use the next day.
Develop prompting discipline. The team should learn how to write effective prompts as part of building workflows, not as an abstract exercise. The skill develops through practice, and the best practice is building something real.
Document what you build. Every workflow built during this phase should be documented: what it does, how to run it, what inputs it needs, what outputs it produces. This documentation becomes the team’s internal playbook.
Days 61 to 90: Embed
The third month is about making AI part of how the team works, not something they do on the side.
Deploy the first workflow. Put it into production. The team should be using it daily or weekly, depending on the workflow’s natural cadence. Monitor the output quality. Iterate on the prompts and process as needed.
Measure the results. Quantify the impact. How much time is saved? Has the output quality improved? Is the team producing more with the same capacity? These numbers are how you build the case for doing more.
Identify workflows two and three. By this point, the team should be able to see other workflows that could benefit from the same approach. The second and third workflows are usually easier and faster to build because the skills and patterns are already established.
Build the internal case. Package the results of the first 90 days into a concise internal narrative. What was done, what it produced, and what the team recommends doing next. This is how AI adoption moves from a team initiative to a function-wide capability.
What this looks like in practice
At the end of 90 days, a marketing team following this framework should have:
One working AI workflow in production, saving measurable time each week.
A trained team that knows how to build and refine AI workflows using real tools.
Documentation of what was built and how it works.
Two or three candidate workflows identified for the next phase.
A clear internal narrative for expanding AI capability across the function.
This is not theoretical. It is the pattern we see with teams that commit to structured training and follow through with implementation. The teams that get the most value are the ones that treat AI adoption as an operational change, not a technology experiment.
The 90-day advantage
Three months from now, your team could be working with AI systems they built themselves. Or they could still be talking about it. The difference is usually not budget, technology, or talent. It is whether someone decides to start and commits to a structured approach.
The AI Marketing Lab is designed to cover the build phase of this framework — the four to six weeks of structured, hands-on training where the team develops real capability. For senior leaders who want private, tailored guidance through the full 90-day process, the executive programme covers all three phases.