Liza Adams

One marketing team now has 100+ AI teammates working alongside 25 humans. The results: 75% faster content creation, 98% better lead qualification, 35% improvement in campaign performance.

I’m speaking at MAICON on October 14-16 in Cleveland to share exactly how. The Marketing AI Institute team wrote a thoughtful blog that covers this approach and what makes it different.

My work has always centered on three things: elevating the strategic value of marketing, using business as a force for good, and ensuring diverse voices are heard at every table. AI amplifies all of that when we use it thoughtfully.

We’re at a turning point where AI can make us better at the strategic work we love. Digital twins that understand how you think. Campaign strategists that build on your expertise. Creative partners that amplify your ideas without losing your voice.

The teams getting real results are reimagining how work gets done when humans and AI complement each other’s expertise, overcome each other’s limitations, and collaborate together.

The blog (link in comments) covers my frameworks, newsletter, and the story behind this work. Worth reading (4 mins) if you’re ready to move beyond basic AI productivity hacks.

Ready to join us in Cleveland? I have a discount code ADAMS200 that saves you $200 off registration. I’ll drop the link in the comments.

Looking forward to seeing you there.

MAICON blog: https://www.marketingaiinstitute.com/blog/marketing-ai-conference-liza-adams

Register here: https://www.marketingaiinstitute.com/events/marketing-artificial-intelligence-conference

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Yesterday, I wrote about the risk of getting stuck using AI just for speed. So, how do we get to the next level of quality and innovation?

It starts with a simple but critical shift in how we talk to our AI teammates.

Your custom GPT follows your brand guidelines better than your team. It can write a persona-based email in seconds. Maybe too convenient.

Go-to-market teams are doing the hard work of building AI teammates. They’re training them on brand guidelines, messaging frameworks, customer personas, and strategic templates. This is a good, solid foundation.

But I’m noticing that many teams are unintentionally outsourcing the thinking along with the execution to custom GPTs.

For routine, high-volume tasks like summarizing call notes or categorizing support tickets, this automation makes perfect sense. The issue is when we do the same for strategic work that defines our value.

I’m seeing teams with GPTs with instructions like “Make sure this article is aligned to our brand, tone, and voice” or “Write an email for this persona aligned to our messaging framework.” The work gets done well and fast. Everyone feels productive.

But the more we outsource our judgment, the harder it becomes to get it back. Imagine a marketer who can’t explain their own positioning unless they ask the GPT first.

You need to be able to still reason through messy, complex trade-offs without AI. Custom GPTs make this even more critical because teams share them. One GPT designed to think for you can train your entire org out of thinking.

The biggest difference is in the GPTs’ instructions. See the examples below.

Your AI teammate knows your brand, personas, and frameworks. Ensure your instructions use that knowledge to think with you, not for you.

That means:

• ➡︎ Ask for analysis and assessments before recommendations

• ➡︎ Request pros and cons, not just answers

• ➡︎ Make sure humans still make the final decision

Each conversation teaches you something. You learn how to evaluate ideas, understand trade-offs, and make better decisions.

If you’re not sure whether you’re outsourcing too much: Try asking your GPT “What assumptions are you making?” or “What would a different persona think?”

If the response surprises you, you’ve got thinking work to get back.

This is how you move past using AI for speed and start using it for quality and innovation.

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Many companies using AI are unintentionally building the case for why they don’t need humans.

I’m not sure what the exact split is, but based on my work with GTM teams, it feels like AI use breaks down roughly like this:

• ➡︎ 80-85% focuses on speed – “Do this task faster”

• ➡︎ 10-15% focuses on quality – “Do this task better”

• ➡︎ 3-5% focuses on innovation – “Do it differently”

I put together some examples of what each approach looks like in practice. See the chart below.

Focusing on productivity is a natural starting point. The risk is when we stop there. If we only teach AI how to do our jobs faster, we risk becoming unnecessary in the workflow.

But when humans push into quality and innovation, we potentially become essential collaborators.

Companies should ask questions that go beyond how AI can make their current processes faster.

• ➡︎ Instead of “How can AI write our emails faster?” ask “What messaging approaches would we never consider without AI’s help?”

• ➡︎ Instead of “How can AI analyze our data quicker?” ask “What patterns might AI spot that change our strategy?”

Ask what becomes possible when expertise flows freely across the entire org.

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