How can AI transform the way a marketing team works?

AI can change how a marketing team works when leaders connect it to a business problem and redesign the workflows, skills, and decisions around that problem. Define what the team needs to do better, identify what is limiting it, and use that diagnosis to choose the work.

A team may need faster reporting, more useful customer research, clearer campaign briefs, or the capacity to test an idea it could not previously pursue. State the constraint specifically. Record who experiences it, how it affects the work, and what evidence would show improvement. This gives the team a reason to change and a basis for judging the investment. A small marketing team can begin with one recurring task and an agreed review routine. A larger organization may need to coordinate the same work across functions, regions, or approval teams.

Follow one actual deliverable from request to use. Capture the inputs, systems, handoffs, approvals, and corrections. Ask experienced people which decisions they make from memory and which exceptions regularly require a conversation. Their answers reveal what needs to be explicit in a redesigned workflow.

Decide where AI can help with analysis, preparation, or recurring steps, and where a person must interpret context or approve a result. Clarify who owns the outcome, who reviews the work, and who can resolve an exception. A faster intermediate step is useful only if it helps the work reach the person who needs it.

Ask → Automate → Expand gives teams three ways to approach that redesign. They can use AI to explore a problem, turn recurring work into a repeatable process, or develop a capability that opens up new possibilities. These approaches can overlap within one project.

At Salesforce, I inherited a cumbersome reporting spreadsheet. I asked Gemini for help, and it suggested separating raw data from a pivot-table reporting view. I had never built a pivot table, so I asked it to guide me. With further automation and AI steps, the broader monthly reporting process went from a couple of days to roughly one or two hours. That was my experience with a particular workflow; it gives a concrete example of learning a capability and putting it to work.

Give marketers supported practice using their own approved tasks. Teach them to frame a problem, provide context, check evidence, and recognize when an output needs further review. Managers should agree on the time available for this work and how useful practices will become part of normal delivery.

Illustrative example: a campaign team could use its regular review to test AI-assisted analysis of approved results. The campaign owner checks the evidence and records which recommendations are actionable. The team then updates the next brief with the lessons it accepts. This connects learning to an existing decision and makes the review standard visible.

Compare the redesigned workflow with its baseline. Examine elapsed time, staff effort, quality, corrections, and the relevant marketing result. Include people's ability to use the approach independently and the support it requires. Record where the evidence is incomplete so an early improvement does not become an unsupported claim about business impact.

A marketing leader can begin with one documented workflow, an accountable owner, a defined quality standard, and a scheduled review. Use the findings to decide whether the team needs better inputs, different skills, more capacity, or a broader rollout. My consulting work brings AI strategy, workflow implementation, and enablement together around those decisions. The readiness assessment on this site can help identify the constraint to address first.