Liza Adams

Many teams think they’re good at AI. They’re actually stuck in patterns they don’t realize.

In my work with GTM teams on AI adoption, I see the same cycles everywhere. Teams hit walls they can’t see that limit what’s possible.

► AI Skeptic – Tries AI for strategic work but gets disappointing results.

“We tested it for competitive analysis and market research. The outputs were generic. AI just isn’t ready for complex thinking.” They’ve given up on AI for anything beyond basic tasks.

► AI Settler – Gets solid value from AI and considers it part of their workflow.

“We use it for email drafts, call summaries, and content outlines. Saves us 5 hours a week. We’ve got this figured out.” They’re satisfied with current results and stopped looking for more.

► AI Trailblazer – Understands that AI’s real power comes from rethinking problems, not just solving them.

They use AI to challenge assumptions, try new approaches, and get better insights by asking better questions.

The difference between these three isn’t skill level or prompting technique. It’s how they think about problems before they ever talk to AI.

Most teams plateau because they optimize what they’re already doing. Big results come from doing things completely differently.

Take this 60-second assessment (link in the comments) to discover your AI working style and get personalized strategies to uplevel how you guide AI.

Let me know what you got. Did anything surprise you?

Tomorrow’s (June 12) newsletter shows the 7 thinking moves that turn AI from a tool into a strategic partner. Subscribe so you don’t miss it (link in the comments).

AI Working Style Assessment – https://claude.ai/public/artifacts/72a476c0-b3fd-40d0-8d3b-adb79d299dd9

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Telling an AI to hurry up and “answer directly” hurts accuracy. A new Wharton study shows the hit is close to 10%. Ethan Mollick posted about the finding. See link to his post in the comments.

I asked ChatGPT to give me some examples of better vs ok prompts. The table below has six examples. Each one pairs the old “answer directly” prompt with a smarter prompt that lets the model think first.

The study says that asking direct answers doesn’t work as well because AI:

• Doesn’t have time to think

• Can’t double-check its work

• Focuses more on format than facts

• Looks confident even when it’s wrong

This is great news because my go-to use case for AI is as a thought partner, not a Q&A machine.

So fascinating, don’t you think?

Ethan Mollick’s post: https://www.linkedin.com/posts/emollick_one-thing-we-discovered-in-our-new-paper-activity-7337615917629034496-EEG-

A few weeks ago I wrote about how traditional management might be changing. Now new data from PwC back this up.

While everyone debates whether AI will replace jobs, companies using AI are seeing some notable results:

• 3x higher revenue per employee

• 56% wage premium for workers with AI skills

• Skills changing 66% faster in AI roles

• Every industry now expanding AI usage

This connects directly to what I shared before about small teams managing AI while focusing on human relationships.

“It is time to stop pretending that the world isn’t changing, and time to start taking control to get the future we want.”

There’s room to be thoughtful about how we respond.

Here are some ways to stay curious:

• Pick one repetitive task and see if AI can help

• Notice what you could focus on if routine work took less time

• Ask your team what they wish they had more capacity for

The data shows clear trends. How we respond is still up to us.

Links to the PwC report and my previous posts relevant to the topic are in the comments.

1) PwC 2025 Global AI Jobs Report – https://www.pwc.com/gx/en/issues/artificial-intelligence/ai-jobs-barometer.html

2) The Re-imagined Era of Entrepreneurship – https://www.linkedin.com/posts/lizaadams_ever-notice-how-many-people-say-i-dont-activity-7306311970809384960-OPLS?

3) Moving from Org Charts to Work Charts – https://www.linkedin.com/pulse/ai-breaking-department-silos-moving-from-org-charts-work-liza-adams-dqumc