Liza Adams

Two weeks ago, I spent an hour with a college class of mostly seniors who will enter the workforce in less than a year. Their first job will require them to work with two types of teammates: humans and AI.

Professor Raul Galang (Professor RG) invited me to speak to his MKT 364 Product Design class at Colorado State University in Ft. Collins about what’s actually waiting for them after graduation.

These students are sharp and curious. They asked about entry-level expectations, what employers look for, and how to build AI skills that translate to real work.

I shared with them that AI isn’t here to replace your thinking. It’s here to make your thinking stronger. Some companies are already building marketing teams where AI teammates outnumber humans four to one. That’s not a distant future scenario. That’s happening now.

Humans and AI have complementary strengths. We bring a moral compass, judgment, context, and the ability to challenge assumptions. AI brings speed, pattern recognition, and the ability to process information at scale. When you combine both, you get better decisions faster. The key is learning to guide AI as a thinking partner, not just an answer engine.

I walked them through three levels of critical thinking with AI. Basic evaluation, multiple perspectives, and assumption-challenging. We compared lazy prompts with strategic ones and saw how dramatically output quality changes when you put in the work up front.

The students who will stand out know how to:

• ➡︎ Ask better questions instead of accepting first answers

• ➡︎ Pressure-test ideas and find blind spots

• ➡︎ Use AI to explore angles they might have missed on their own

• ➡︎ Show their thinking process, not just the final output

I also showed them how to use two practical tools for learning: NotebookLM and ChatGPT’s Study Mode. Both are free. Both help students go deeper on material, test their understanding, and practice in ways that actually build knowledge.

These students aren’t scared of AI. They’re trying to figure out how to use it well. That’s exactly the right mindset.

Parents and educators can help by teaching students to show their work and explain their thinking. Help them understand that using AI to strengthen analysis is different from letting AI do the thinking for you. Give them frameworks and tools to practice before they need it in a real job.

These students are about to walk into workplaces where AI teammates are standard. The ones who learn to guide AI thoughtfully will have a real advantage.

If you’re a parent with a college student, talk to them about building this skill now. I’ve written more about how I’m approaching this with my own daughter (link in comments).

These kids are ready. They just need guidance that matches the reality they’re walking into.

Big thanks to Professor RG for allowing me to share and inspire the next generation.

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The gap between AI experiments and real business impact is where most teams get stuck. They’ve got scattered pilots and individual wins. What they don’t have is a strategy to drive adoption across the team or prove ROI to leadership.

The teams that close this gap do one, two, or all of these:

► Anchor to strategic initiatives

Map your AI use cases to initiatives that already have owners, budgets, KPIs, timelines, and executive visibility. This increases your probability of adoption and success because you’re working with built-in momentum and resources.

One CMO had “Accelerate Customer Growth” as a top priority with a goal to increase net revenue retention by X percent.

The team mapped AI use cases directly to it: spotting upsell opportunities through behavior analysis, personalizing nurture campaigns, and flagging at-risk accounts early. Because these supported a board-level initiative with clear metrics, they got immediate resources and fast adoption.

Sometimes one initiative needs multiple AI use cases working together. Other times, a single use case moves the needle on several initiatives. Either way, you turn AI from a side experiment into a strategic asset.

► Solve biggest pain fast

Focus on work that’s eating up hours, blocking progress, or burning budget. The bigger the pain, the faster you’ll see value.

A global marketing team I worked with was spending tens of thousands of dollars monthly on agency fees to translate and localize every customer-facing asset into eight languages. The process took weeks.

A small group of field marketers who were local language speakers built custom GPTs with brand guidelines, market expertise, and localized examples. Within a week, they had a working solution. AI handled 80-90 percent of the work, with the team checking it and handling the final edits. Translation time dropped from weeks to days, saving thousands in agency fees every month.

Big pain, fast ROI, and proof that’s hard to ignore. The win inspired the team to quickly tackle other big problems.

► Let your trailblazers inspire what’s possible

Your AI trailblazers have been experimenting, building workflows, and learning what AI can and can’t do. They understand AI’s potential and limitations. They’re pushing it to its full capacity.

Find them. Give them space to prove what’s possible. Then let them mentor the team and show others what they’ve built.

At one B2B tech company I guided, a small group of trailblazers quickly grew to 75 across marketing who became the engine for transformation. They created 211 custom AI teammates during experimentation. Today, 57 are integrated into regular workflows across the team. Their wins moved the skeptics faster than any exec mandate could.

The bottom line: Tie AI use cases to strategic initiatives with momentum. Prioritize high-pain, high-value problems. Let your trailblazers inspire what’s possible.

That’s how you close the gap between experiments and real business impact.

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Anthropic’s Claude Sonnet 4.5 just swore and defended itself. 🤣 All good and it’s a decent defense. But it’s so uncharacteristic that it surprised me and gave me a chuckle.

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