Liza Adams

Many are sharing charts about Reddit and Wikipedia dominating AI search mentions, desperately trying to crack the code. The answer is in plain sight, but they’re too busy looking for shortcuts.

These platforms get sourced because they directly answer real questions using language real people actually use.

I’ve been watching GTM leaders react to this data. They nod along when I explain the approach, then immediately ask for the algorithm hack. They want the Ozempic for AI search that bypasses the actual work of understanding customers.

I’m noticing that teams fall into two buckets:

• ➡︎ Teams that do the work – They audit their content, analyze customer language, map out funnel questions, and create genuinely helpful answers. Yes, it’s hard and takes time. But they’re treating the cancer, not putting a bandaid on it.

• ➡︎ Teams chasing shortcuts – They say “that will take too long” and keep searching for the algorithm. They want to look trustworthy without being trustworthy.

Reddit works because real people solve real problems in real language. Wikipedia gets cited because decades of helpful content builds trust. But note that Business/Service sites are getting 50% of citations, showing the opportunity exists.

Many companies don’t understand their customers well enough, especially given that markets have shifted and behaviors have changed. They haven’t:

• ➡︎ Figured out what customer AI inquiries look like at each funnel stage

• ➡︎ Created content that directly answers those questions using customer language

• ➡︎ Been honest about when they’re the best fit and when they’re not

• ➡︎ Put content and engaged authentically in trusted watering holes (e.g., communities, publications, review sites, social channels) where customers hang out

AI is looking for signal, not noise. When your content shows up consistently across channels answering real questions, that becomes signal.

AI amplifies what’s already there – whether what’s there is good or bad. Be genuinely helpful and let the cards fall as they may.

Chasing algorithms is futile. The AI companies probably don’t even know their own algorithms, and it changes constantly.

This is about building trust. What would it take for someone to earn your trust? Mowing your yard versus finding daycare for your children. The bigger and the more important the pain, the more intense the trust-building needs to be.

High-intent buyers choose companies they trust to solve real problems, not AI search hacks.

Stop chasing algorithms and start earning trust. Be genuinely helpful to real people with real problems. I shared links to a couple of newsletters in the comments below that show you how. Something for your consideration for those of you who like to do some weekend learning.

AI Search Related Newsletters:

1. 1) Make Your Brand Relevant in AI Search – https://www.linkedin.com/pulse/make-your-brand-sourced-top-result-ai-search-practical-liza-adams-qmarc

2. 2) When AI Judge Your Brand Before Humans Do – https://www.linkedin.com/pulse/when-ai-judges-your-brand-before-humans-do-liza-adams-8xuyc

3. 3) Creating Standout Content When 50% of Web Traffic Disappears – https://www.linkedin.com/pulse/beyond-ai-generated-content-guide-standing-out-when-50-liza-adams-asnpc.

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Many teams use AI like a Magic 8-Ball. They ask a question, get an answer, and move on to the next task.

The teams making real impact go beyond using it to do a task quickly. They’re using AI to make better decisions and find approaches they’d never think of alone. As they say…

we can’t reimagine the future by simply automating the past.

While everyone else stops at productivity gains, some leaders are going further, using AI to pinpoint blind spots and challenge their assumptions. Most just haven’t seen how.

This week, I saw this change happen IRL with 60+ marketers at Cox Automotive Inc., the company behind Kelley Blue Book and Autotrader that helps dealers and partners buy and sell millions of vehicles each year.

When teams stopped asking AI to do tasks and started asking it to poke holes in their plans, the team dynamics changed. Conversations went deeper and were more insightful. People started spotting problems they’d missed.

Here are some key learnings:

• ➡︎ Teams using AI just for speed miss the real opportunity to make better decisions and come up with fresh ideas

• ➡︎ The probing questions that make your AI conversations better show up in every meeting and planning session

• ➡︎ This approach works on everything from email subject lines to campaign plans and expansion strategies

Because we’ve long been rewarded for good answers, many may not be as adept at asking thoughtful questions. Every AI conversation becomes critical thinking practice. Pushing AI then becomes automatic and a habit.

I break down the complete three-level critical thinking framework with sample questions and a starter kit to help you apply this to your specific projects. Full details in the newsletter below.

Prefer audio? There’s a 13-minute AI podcast version in the comments, perfect for your commute or while multitasking.

Huge thanks to Mandy Dhaliwal (CMO of Nutanix), Ramon L. Cortes (AVP of Cox Automotive), and Sydney Sloan (CMO of G2) for sharing insights on how they use AI as a thought partner for both tactical and strategic work.

How much do you use AI to speed up work? Make better decisions? Find new ways?

Here’s the 13-min AI podcast version of this newsletter to support different learning styles. Listen while driving, walking the dog, or having lunch 😉

https://drive.google.com/file/d/1KZzMxGeR-dSiTgugukPVqxJigyRKaaDv/view?usp=share_link

Give it a few seconds to load after you hit play.

Note: AI-generated using Google’s NotebookLM and reviewed by me for accuracy and responsible AI use. See original post here

Some worry AI will make us intellectually lazy. They’re right, but only if we let it.

Teams that push beyond the first AI answer are finding out they can ask more thoughtful questions (like the ones below) that expose blind spots:

➡ Based on our customer reviews and feedback, what problems are we claiming to solve that we’re actually not solving?

➡ What assumptions am I making about why our campaigns work that might be completely wrong?

➡ What would happen to our business if our biggest differentiator became table stakes next year?

These questions force you to test assumptions you didn’t even know you were making.

When you put AI outputs to the test and ask for reasons behind every recommendation, you develop stronger analytical skills than most traditional training provides.

Teams using AI as a thinking partner have become sharper decision-makers. The same discipline that improves their AI conversations changes how they approach everything from email subject lines to market expansion plans.

Tomorrow I’m sharing in my newsletter the complete three-level framework that shows you how to apply increasing levels of critical thinking with AI. Subscribe to the newsletter here to get it directly in your inbox: https://lnkd.in/eg48-RXp

See orignal post here

Most people think messy sketches can’t become professional decision tools. I just turned chicken scratch into two polished guides for choosing between ChatGPT features.

I had to figure out a better way to frame and understand when to use ChatGPT Chats vs Projects vs Custom GPTs.

So I sketched my thoughts and started collaborating with AI (Claude) using plain English prompts. We went back and forth. I challenged assumptions. AI helped organize my thinking. We improved ideas together.

What came out of this were two decision tools, a process diagram and a comparison table. Some people learn better from visuals, others from structured data.

The key insight is that collaboration beats perfection.

I didn’t need to know exactly what I wanted upfront. I just needed to start somewhere and keep working at it. AI helped me get to something neither of us could have created alone.

When I asked for honest feedback on my approach, AI said it was effective. Maybe that’s just training to please, but the process insights feel worth sharing.

Don’t give up if your first AI interaction doesn’t hit the mark. Start messy. Sketch your thoughts. Use plain language. Keep working together.

The real value happens in the collaboration, not in having everything figured out from the start.

BTW, I tried this use case with Gemini and ChatGPT. But neither’s output was as good as Claude’s (yet).

In Thu’s newsletter this week, I’ll cover how to collaborate and push AI’s thinking. Subscribe using the link in the comments to get it directly in your inbox.

Subscribe to the newsletter here: https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7227665669209604096. See orignal post here

Come join us at Pavilion’s Women’s Summit, alongside fellow leaders like Kim Storin of Zoom, Casey Erickson of Captivate Talent, and Deirdre Hudson of YELL in Washington, D.C., on September 23.

I’ll be speaking on a panel session titled “Planning for Uncertainty: Ageism + Macro Economy + AI + Changing Workplace” with Shira Abel, Jennie Blumenthal, and Lindsey Katalan. Attendees can expect to learn:

• How to spot change early so you can move before the market does.

• Ways to stay relevant, especially when ageism or AI say otherwise.

• Real stories and smart strategies from women who’ve made bold pivots.

What sets the Pavilion Women’s Summit apart is its exclusive community of executive-level GTM leaders, where the most accomplished women come together not just to network, but to actively support, challenge, and elevate one another in a space built on shared ambition, generosity, and the power of collective success.

Learn more and sign up (see link in the comments below).

Register here: https://www.joinpavilion.com/womens-summit?utm_campaign=5766660-2025%20Women%27s%20Summit&utm_source=linkedin&utm_medium=social&utm_content=speaker%20toolkit

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