Many marketing teams are using AI, but not all of them are using it in a way that changes how the function actually works. Someone uses it to draft a LinkedIn post; some use it to summarize a meeting, and another person uses it to brainstorm campaign ideas or rewrite an email. Those are useful starting points, but they are still individual tasks.

At our recent Prosh Marketing roundtable, the conversation moved beyond simple prompting and into a bigger question: how should marketing teams build AI into the way they work? 

Using a tool once to save time is helpful, but building a repeatable workflow around it is where the real value starts. For example, after a webinar or roundtable, a team could use AI to help summarize the transcript, identify recurring discussion themes, pull audience questions, draft a follow-up email, create a LinkedIn recap, and turn the strongest insights into future content ideas.

The issue is that many teams are still using AI casually. Different people use different tools, which results in different output quality and inconsistency in brand voices. No one is fully clear on what needs to be reviewed, what can be automated, or what standard the work needs to meet. A speedier workflow is only beneficial if the team has clarity on audience, brand voice, approvals, and business goals. Artificial intelligence can certainly provide more marketing output, but it’s not automatically better without that.”

Marketing teams should not expect strong results from vague instructions; the tools need direction. They need to understand the brand, the audience, the campaign objective, the tone, the offer, the channel, and what good work looks like. That might mean giving them past campaign examples, messaging guidelines, customer personas, sales objections, performance learnings, or approved brand language.

The stronger the input, the more useful the output, because even with better context, human review still matters. A summary still needs interpretation, a content draft still needs editing, and a campaign recommendation still needs to be checked against the business goal. 

A good starting point is to look at the work the team repeats every week or every month. Whether it is newsletter creation, event follow-ups, campaign reporting, customer feedback reviews, or meeting summaries.

Then ask:

• Where are we spending too much time on repetitive work?
• What context would improve the quality of the output?
• Where do we still need human judgment?
• Who reviews the final version?
• What should never be published or sent without approval?

This makes AI a more considered element of the marketing role, rather than a haphazard shortcut. It’s not about automating everything, the idea is to clear up more room for the activity that really creates value: strategy, insight, creative thinking, consumer knowledge and improved decision making.