GrowthPath Partners LLC

Empowering Purpose-Driven Growth

Liza Adams

Name Your AI Teammates Right: Get Them Used!

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Too many AI teammates get built and barely used. The problem is the name, not the tech.

A “Sales Assistant” that’s really just a lead scorer. A “Helper” that isn’t clear what it helps with. An “Assistant” that tries too hard to sound human.

I’ve been working with go-to-market teams who are building AI teammates. These are custom AI tools you build, train, and manage for specific tasks or workflows using Custom GPTs, Claude Projects, Gemini Gems, or Copilot Agents.

At first, I thought naming them would be the fun part. Star Wars characters. Marvel superheroes. The Office references. Turns out, fun names can work but only when they match the relationship you’re creating.

Here’s what I’m seeing.

1) Tool

Name it after what it does, like Lead Scorer, Data Categorizer, and Testimonial Finder.

Pure function, no personality. People use it once, get what they need, move on. Clear and to the point.

2) Sidekick

Name it to signal collaboration, like Draft Helper, Campaign Partner, and Strategy Assistant.

It adapts and works with you without pretending to be someone specific. This is where fun names can work like “Robin” or “Chewy” tell you it’s a helpful collaborator.

3) Persona

Name it after a specific person or role, like LizaGPT (my digital twin), CEO Jordan, Enterprise Buyer Persona, and Industry Analyst GPT.

Less about tasks, more about extending someone’s thinking. It tests ideas, challenges logic, finds blind spots. The name tells you whose perspective you’re getting. “Yoda” works here because everyone knows he challenges your thinking.

Match the name to the relationship you’re creating.

Better names set clearer expectations. And clearer expectations mean your AI teammates actually get used.

Where do your AI teammates sit on this spectrum?

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Evolving AR: Managing Signals in the Age of AI

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AI decides which vendors buyers see and evaluates them before buyers ever do.

Analyst Relations teams now manage signals that influence both people and the AI systems they rely on. This is different from managing analyst relationships alone.

I’m keynoting at ARInsights’s Engage Live in San Francisco on November 12 to talk about how AR is evolving in the AI era. We’ll cover:

  • ➡︎ How analysts and buyers are using AI to evaluate vendors faster

  • ➡︎ Which signals AI systems pull from and which ones you can influence

  • ➡︎ Why consistency across every touchpoint drives visibility and trust

  • ➡︎ How AR becomes a strategic insight engine for your business

If you’re leading AR or marketing strategy, I’d love to see you there.

See link in the comments to learn more and register.

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We Spent $309B on AI. Why Aren’t People Ready?

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We’ve spent $309 billion training AI. Only $11 billion training people to work with it.

I joined Julie Williamson, PhD on The Failure Gap podcast to dig into what happens when leaders agree they need to move on AI but can’t get aligned on how. Perfect for background listening (links in comments) this weekend while you’re doing meal prep or putting up your Halloween display (We got that done yesterday. 🎃)

The conversation balances the why behind AI transformation with real steps to take action. We covered everything from why legal at Moderna was one of the first teams to hit 100% AI adoption to how naming your AI teammates can change how teams engage.

Here’s what you’ll walk away with:

  • ➡︎ Three critical mindset shifts that move teams from AI as a tool to AI as a teammate

  • ➡︎ How to protect confidential information while still getting started with AI

  • ➡︎ Why using multiple AI platforms makes your thinking stronger and reduces hallucinations

  • ➡︎ Real techniques for pressure-testing your strategies using AI from different perspectives

  • ➡︎ Why AI literacy is foundational regardless of your title or technical background

Thanks to Julie and the Karrikins Group for creating space for this kind of conversation. Their focus on closing the gap between agreement and alignment is exactly where most organizations get stuck with AI adoption. And thank you Kerrie Luginbill for thinking about me and introducing me to Julie.

The most expensive infrastructure purchase is the one people can’t use well. This conversation gives you a path forward.

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Marketing AI Assessment: Build Your Roadmap

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Here’s what a few minutes of honest assessment showed one marketing team:

Strong AI adoption in social and sales enablement. Zero in demand gen, analytics, and paid media. 19 pilot-ready opportunities worth exploring.

They knew they were early in their AI journey. The data confirmed it and showed them exactly where to focus.

51 potential AI use cases across their marketing org. 15 actively used. 36 still awaiting action.

We mapped their current state, then guided AI to analyze where they stood against the 4 mindset shifts I wrote about a few weeks ago (link in comments):

  • ➡︎ Answer engine → Thought partner

  • ➡︎ Faster → Better → Different

  • ➡︎ Tool → Teammate → Systems

  • ➡︎ Org charts → Work charts

The analysis turned gut feelings into an initial roadmap. Not just “we need to do more AI” but ideas for where to expand, which current tools to leverage, and which gaps to close first.

I used Claude to create an interactive dashboard that visualized the analysis (see video of the dashboard in action below). You can ask other AIs like ChatGPT and Gemini to do the same with your data.

Here’s the approach:

  • ➡︎ List potential AI use cases across your GTM functions

  • ➡︎ Mark what you’re actually doing (be honest)

  • ➡︎ Note which tools, which teams, what value

  • ➡︎ Look for patterns like adoption clusters, tool overlap, missing areas

  • ➡︎ Ask AI to analyze it against the 4 dimensions: “Where am I on each shift? What should I prioritize?”

Wherever you are on this journey (crawling, walking, or running) clarity on where you’re standing gives you a roadmap forward.

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Filipino Women in AI: Seeing What’s Possible

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Asianna Danielle J. Junge, a junior at Rice University, came up to me after my keynote at AMA Houston – American Marketing Association’s Marketing Edge conference today.

She told me she’d learned a lot and wanted to know more about the research I referenced showing women use AI 16-20% less than men at work. (See link in the comments to a post I shared on this topic last week.)

“I was inspired to see an Asian woman executive leading a discussion on AI. I’ve never seen a Filipino woman do this. I’m Filipino too.”

We don’t talk enough about representation in AI and tech. I’ve decided to speak up more in rooms that stay quiet on this.

In my talk, I mentioned that I worry about my aging mother in Manila. Although my daughter is only a few miles away at Colorado University – Boulder, I miss her dearly. I get sick, exhausted, distracted. I’m affected by my environment in ways AI isn’t. But AI doesn’t have a moral compass, so I guide it so it knows right from wrong.

We have different strengths. We can overcome each other’s limitations.

Meeting Asianna reminded me why this matters. If showing up and doing work I care about helps someone see what’s possible for them, that’s enough.

To anyone who wonders if their voice is too small… it isn’t. The vision for AI is to benefit all of humanity. We need your perspective at the table.

Thank you to Sheana Hamill, Henry Adaso, Joel Luks, and the AMA Houston team for inviting me and creating space for these conversations.

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