I empower leaders with AI Workshops & B2B digital strategy | LinkedIn Top Voice | Public Speaker | Board Director | Podcaster🎙️

AI won’t replace communicators, but communicators who use AI will define the future.

The communications industry is evolving faster than most teams are prepared for. Those who integrate AI today, ethically, strategically, and confidently, will lead tomorrow’s conversations.

𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗿𝗲 𝗜 𝗰𝗼𝗺𝗲 𝗶𝗻.

I help PR and communications leaders future-proof their teams with practical, hands-on AI strategy. Through custom workshops, audits, and advisory programs, I bridge the gap between curiosity and capability, showing teams how to integrate AI into real-world workflows without losing the human voice that makes communications powerful.

I’ve trained and advised leaders across a variety of organizations like Ragan, MacMillan Publishing, Penguin Random House, PPG, etc., helping them build AI readiness, sharpen strategy, and unlock new efficiencies in content, reputation, and storytelling.

If your team wants to:
✅ Use AI responsibly, with clear governance and guardrails
✅ Build workflow efficiencies that actually stick
✅ Strengthen credibility in an AI-driven media landscape

Then 𝗹𝗲𝘁’𝘀 𝘁𝗮𝗹𝗸. Visit chrisgee.me
or send me a message here on LinkedIn.

Don't wait to future-proof your team.

Communicators used to write for journalists.

Now we also have to write for 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝘁𝗵𝗮𝘁 𝘀𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗲 𝘂𝘀.

If your message falls apart when AI condenses it to three sentences, you have a problem.

This week I found myself saying the same thing in two different conversations with communications leaders.

More and more often, 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗿𝗲𝗮𝗱𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆.

They are reading 𝗮 𝘀𝘂𝗺𝗺𝗮𝗿𝘆 𝗼𝗳 𝗶𝘁.

AI assistants, search tools, and productivity platforms are increasingly acting as intermediaries. They scan long documents, extract key points, and present condensed answers to busy executives, journalists, and stakeholders.

Which means communicators now have a new challenge.

Your message has to survive 𝘀𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻.

If the essence of your story disappears when an AI compresses it into three sentences, you have a problem.

I mentioned this a couple times this week and realized it was worth sharing more broadly.

So in tomorrow’s, this Prompt of the Week is designed to help you test exactly that.

Use it to pressure test whether your message still holds together once an AI system distills it down.

Because communicators used to focus on how journalists interpreted our stories.

Now we also need to think about how 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁 𝘁𝗵𝗲𝗺 𝗳𝗶𝗿𝘀𝘁.

Curious if others are already thinking about this.

Are you designing messages that survive AI summaries?

Chris Gee

Helping PR & Comms leaders future-proof with AI strategy | Speaker + Trainer | Keynotes + Workshops | Ragan Advisor

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Most comms teams don’t have an AI problem.

𝗧𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝗮 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.

I’ve seen this across agencies and corporate teams.

People are “using AI.”

But:
→ Tone is off
→ Outputs are generic
→ Risk flags are missed
→ No one knows what “good” looks like

AI doesn’t fail.

Instructions fail.

𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼𝘄 𝗮 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝘀𝗸𝗶𝗹𝗹, 𝗻𝗼𝘁 𝗮 𝗷𝘂𝗻𝗶𝗼𝗿 𝗼𝗻𝗲.

If your team can’t write clear instructions for a machine, what does that say about clarity internally?

I’m running small team prompting sessions this quarter.

If you’re trying to standardize how your team uses AI, happy to share what that looks like.

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We may be approaching the saturation point for synthetic content.
We tend to assume technology spreads exponentially until it becomes ubiquitous. That’s the default narrative... 𝗮𝗻𝗱 𝗶𝘁'𝘀 𝗳𝗹𝗮𝘄𝗲𝗱.

The data suggests something more complex is happening with synthetic content on social media.

AI-generated supply is rising quickly across platforms. At the same time, we’re seeing more labeling systems, more “see less AI” controls, and a sharp increase in public conversation about “AI slop” (9x year-over-year growth in usage of that term).

In one YouTube-focused estimate, between 21–33% of a new user’s Shorts feed may already be AI-generated or low-value synthetic content.

𝗧𝗿𝘂𝘀𝘁 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗨.𝗦. 𝗮𝗿𝗲 𝗱𝗲𝗰𝗹𝗶𝗻𝗶𝗻𝗴 𝗲𝘃𝗲𝗻 𝗮𝘀 𝗔𝗜 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝘃𝗼𝗹𝘂𝗺𝗲 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝘀.

That combination matters.

When supply rises faster than trust, friction emerges.

This doesn’t mean AI content disappears. It means it meets resistance.

The research outlines three possible paths:
1️⃣ Continued acceleration
2️⃣ A plateau where synthetic content becomes common but less effective
3️⃣ Or a trust feedback loop where audiences shift toward verified creators and authenticity signals

We are already seeing early signs of an “authenticity premium,” where content that signals human authorship or limited AI use outperforms baseline synthetic volume in certain categories.

And there’s something deeper here.

Humans are biologically wired for social connection. We rely on faces, voice, and perceived intent as trust shortcuts. 

𝗪𝗵𝗲𝗻 𝗳𝗲𝗲𝗱𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝘀𝗮𝘁𝘂𝗿𝗮𝘁𝗲𝗱 𝘄𝗶𝘁𝗵 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝘃𝗼𝗹𝘂𝗺𝗲, 𝘁𝗵𝗲 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗹𝗼𝗮𝗱 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝘀. 𝗣𝗲𝗼𝗽𝗹𝗲 𝗲𝗶𝘁𝗵𝗲𝗿 𝗱𝗶𝘀𝗲𝗻𝗴𝗮𝗴𝗲 𝗼𝗿 𝗴𝗿𝗮𝘃𝗶𝘁𝗮𝘁𝗲 𝘁𝗼𝘄𝗮𝗿𝗱 𝗰𝗹𝗲𝗮𝗿𝗲𝗿 𝘀𝗶𝗴𝗻𝗮𝗹𝘀 𝗼𝗳 𝗮𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗶𝘁𝘆.

This isn’t anti-AI.

It’s a reminder that adoption curves bend when they collide with human psychology.

AI content will continue to grow. But growth does not automatically equal acceptance.

For communicators, the question is no longer “How much AI can we use?”

It becomes:
→ Where does AI enhance experience?
→ Where does it erode trust?
→ Where does authenticity become a differentiator?

𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝘀𝗰𝗮𝗹𝗲𝘀 𝗾𝘂𝗶𝗰𝗸𝗹𝘆. 𝗛𝘂𝗺𝗮𝗻 𝘁𝗿𝘂𝘀𝘁 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁.

Curious how others are seeing this play out in their campaigns and feeds.

The story around “AI layoffs” is no longer just a business headline.
𝗜𝘁’𝘀 𝗮 𝗿𝗲𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗿𝗶𝘀𝗸.

Across industries, executives are framing workforce reductions as AI-driven, even when their AI deployments are still early and delivering limited automation gains.

𝗧𝗵𝗮𝘁 𝗱𝗶𝘀𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝗳𝗲𝗲𝗱𝘀 𝗮𝗻𝘅𝗶𝗲𝘁𝘆. 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝗹𝘆 𝗮𝗻𝗱 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹𝗹𝘆.

When people hear “AI layoffs,” they don’t hear restructuring or strategic pivot. They hear replacement. And that perception creates a breach of trust that lingers far longer than the headlines.

💡 This is where communications leadership matters.

A few principles are critical:

✅ Name the real drivers. If it’s cost optimization or a strategic shift, 𝘴𝘢𝘺 𝘵𝘩𝘢𝘵. Don’t oversell AI as the scapegoat.
✅ Ground the change in human agency. Explain what is evolving in the work 𝘢𝘯𝘥 𝘸𝘩𝘺.
✅ Lead with capability building. Show investment in reskilling, mobility, and support systems.

The perception of an event can be more consequential than the event itself.

𝗔𝗜 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗲𝗿𝗼𝗱𝗲 𝘁𝗿𝘂𝘀𝘁. 𝗣𝗼𝗼𝗿 𝘀𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 𝗱𝗼𝗲𝘀.

How are leaders navigating these comms in your organization? 

Let me know in the comments! 👇🏽👇🏽👇🏽

We keep talking about AI as if it’s about to replace most white-collar workers.

𝗕𝘂𝘁 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝘀𝘂𝗴𝗴𝗲𝘀𝘁𝘀 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘃𝗲𝗿𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁.

Researchers recently constructed what they called a “Remote Labor Index,” testing AI agents across 240 real freelance projects spanning architecture, graphic design, video production, marketing assets, and game development.

🚫 Not toy prompts.
🚫 Not lab simulations.
✅ Real deliverables companies actually paid for.

Across more than 6,000 hours of paid work worth over $140,000, the best AI agent automated about 2.5 percent of the work. 𝗠𝗼𝘀𝘁 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 𝘀𝗼𝗹𝘃𝗲𝗱 𝗹𝗲𝘀𝘀 𝘁𝗵𝗮𝗻 𝟯 𝗽𝗲𝗿𝗰𝗲𝗻𝘁.

🚫 45.6% failed on quality
🚫 17.6% produced corrupted files
🚫 14.8% included inconsistencies
🚫 35.7% produced incomplete work

These weren’t minor glitches, they were breakdowns.

𝗘𝘅𝗽𝗲𝗰𝘁𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝘄𝗼𝗿𝗸𝗲𝗿𝘀 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄 𝗳𝗲𝗲𝗹𝘀 𝗹𝗶𝗸𝗲 𝗲𝘅𝗽𝗲𝗰𝘁𝗶𝗻𝗴 𝗮 𝗵𝗮𝗺𝗺𝗲𝗿 𝘁𝗼 𝗱𝗿𝗶𝘃𝗲 𝗮 𝘀𝗰𝗿𝗲𝘄.

Yes, you can re-engineer the hammer.
You can keep pouring money into improving it.
But it’ll never be as effective as a $15 screwdriver. 

They have totally different strengths and are meant to solve totally different problems. 

Global AI infrastructure investment is already in the hundreds of billions annually, with projections reaching into the trillions over the next decade.

But if the best systems can only automate 2–3 percent of real-world creative and knowledge work, 𝘄𝗲 𝘀𝗵𝗼𝘂𝗹𝗱 𝗵𝗼𝗹𝗱 𝗼𝗳𝗳 𝗯𝗲𝗳𝗼𝗿𝗲 𝗱𝗲𝗰𝗹𝗮𝗿𝗶𝗻𝗴 𝗳𝘂𝗹𝗹 𝗱𝗶𝘀𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗶𝗻𝗲𝘃𝗶𝘁𝗮𝗯𝗹𝗲.

The math just ain’t math-ing.

The real opportunity is not wholesale worker replacement. It’s building the ideal human co-pilot.

✅ AI excels at narrow tasks.
✅ It accelerates drafting, summarizing, iteration.
🚫 But it struggles with end-to-end ownership and messy coordination.

𝗧𝗵𝗮𝘁’𝘀 𝗻𝗼𝘁 𝘄𝗲𝗮𝗸𝗻𝗲𝘀𝘀. 𝗧𝗵𝗮𝘁’𝘀 𝗱𝗲𝘀𝗶𝗴𝗻 𝗿𝗲𝗮𝗹𝗶𝘁𝘆.

The real win re: AI?

💪🏽 Human judgment + AI speed.
💪🏽 Human accountability + AI leverage.

Maybe the future isn’t about building the perfect hammer.

Maybe it’s about recognizing when the screwdriver already works better to drive screws? 🤷🏽

What do you think? Are we evaluating AI the wrong way? Let me know in the comments. 👇🏽👇🏽👇🏽

I recently joined Monica Talan on her “𝘐𝘧 𝘐 𝘖𝘯𝘭𝘺 𝘏𝘢𝘥 10 𝘔𝘪𝘯𝘶𝘵𝘦𝘴” podcast to talk about what’s actually changing in communications right now.

A few themes:

1️⃣ AI is exceptional at speeding up output. Slides, drafts, repurposing. That means our value moves to the 20 percent that drives real impact: judgment, empathy, and creativity.

2️⃣ Prompting matters. But imagination matters more. Models follow instructions. Humans decide what’s worth asking.

3️⃣ Agents are not automations. Automation is linear. Agents have autonomy. That difference is a real governance flag.

We also discussed how AI is accelerating the fractional model and why human connection becomes more valuable in a sea of synthetic content.

Just a short, 10-minute conversation. Check it out and let me know what you think! 

https://lnkd.in/ear2mzNT

AI can draft the slides.
But are we using it to think bigger?

AI expert Chris Gee breaks down where the real opportunity lives, where the risk sits, and how communicators can lead instead of react.

Get clear perspective on the future of comms in just 10 minutes...

I recently joined Monica Talan on her “If I Only Had 10 Minutes” podcast to talk about what’s actually changing in communications right now.

Not the hype.
The practical shifts.

A few themes:

The execution layer is being commoditized.
AI is exceptional at speeding up output. Slides, drafts, repurposing. That means our value shifts to the 20 percent that drives real impact — judgment, positioning, risk, and context.

Creativity beats coding.
Prompting matters. But imagination matters more. Models follow instructions. Humans decide what’s worth asking.

Agents are not automations.
Automation is linear. Agents have autonomy. That difference isn’t technical — it’s governance.

We also discussed how AI is accelerating the fractional model and why human connection becomes more valuable in a sea of synthetic content.

Short conversation. Big implications.

https://lnkd.in/ear2mzNT

The AI debate is stuck on the wrong premise.
Instead of asking “Will AI replace workers?” we may need to ask something more uncomfortable.
What happens if there simply 𝗮𝗿𝗲𝗻’𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 𝘄𝗼𝗿𝗸𝗲𝗿𝘀 𝗹𝗲𝗳𝘁?

Let's look at the demographics.

Across the developed world — and increasingly in the United States — populations are aging rapidly. Birth rates are falling. Labor force growth is slowing.

The constraint may not be too many workers displaced by AI.

It may be too few workers available without it.

Think about the math:

• The U.S. fertility rate is around 1.6 births per woman, well below the replacement rate of 2.1.
• By 2034, adults 65+ will outnumber children for the first time in U.S. history.
• The worker-to-retiree ratio continues to shrink, placing pressure on productivity.

That creates a different framing.

AI isn’t just a cost-cutting technology.

It may become a demographic stabilizer.

Automation in:

• healthcare
• logistics
• software development
• design
• marketing production
• research

...could partially offset structural labor shortages rather than cause mass unemployment.

In other words:

The biggest economic impact of AI might not be replacing workers.

It might be helping societies function with fewer of them.

We may look back and realize the real question was never:

“Will AI take jobs?”

It was:

“Could our economies keep growing without it?”

Curious how others see this.
Are we underestimating the demographic side of the AI story?

Instead of asking “Will AI replace workers?” we may need to ask something more uncomfortable.
What happens if there simply 𝗮𝗿𝗲𝗻’𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 𝘄𝗼𝗿𝗸𝗲𝗿𝘀 𝗹𝗲𝗳𝘁?

Let's look at the demographics.

Across the developed world — and increasingly in the United States — populations are aging rapidly. Birth rates are falling. Labor force growth is slowing.

𝗧𝗵𝗲 𝗶𝘀𝘀𝘂𝗲 𝗺𝗮𝘆 𝗻𝗼𝘁 𝗯𝗲 𝘁𝗼𝗼 𝗺𝗮𝗻𝘆 𝘄𝗼𝗿𝗸𝗲𝗿𝘀 𝗱𝗶𝘀𝗽𝗹𝗮𝗰𝗲𝗱 𝗯𝘆 𝗔𝗜.
𝗜𝘁 𝗺𝗮𝘆 𝗯𝗲 𝘁𝗼𝗼 𝗳𝗲𝘄 𝘄𝗼𝗿𝗸𝗲𝗿𝘀 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗶𝘁.

A few numbers to consider: 

📉 The U.S. fertility rate is 𝗮𝗿𝗼𝘂𝗻𝗱 𝟭.𝟲 𝗯𝗶𝗿𝘁𝗵𝘀 𝗽𝗲𝗿 𝘄𝗼𝗺𝗮𝗻, well below the replacement rate of 2.1.
📉 By 2034, 𝗮𝗱𝘂𝗹𝘁𝘀 𝟲𝟱+ 𝘄𝗶𝗹𝗹 𝗼𝘂𝘁𝗻𝘂𝗺𝗯𝗲𝗿 𝗰𝗵𝗶𝗹𝗱𝗿𝗲𝗻 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘁𝗶𝗺𝗲 𝗶𝗻 𝗨.𝗦. 𝗵𝗶𝘀𝘁𝗼𝗿𝘆.
📉 The 𝘄𝗼𝗿𝗸𝗲𝗿-𝘁𝗼-𝗿𝗲𝘁𝗶𝗿𝗲𝗲 𝗿𝗮𝘁𝗶𝗼 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗲𝘀 𝘁𝗼 𝘀𝗵𝗿𝗶𝗻𝗸, placing pressure on productivity.

Based on those numbers, we may be looking at this the wrong way. 

Automation in:
→ Healthcare
→ Logistics
→ Software development
→ Design
→ Marketing production
→ Research

Could partially offset structural labor shortages rather than cause mass unemployment.

In other words:

𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗔𝗜 𝗺𝗶𝗴𝗵𝘁 𝗻𝗼𝘁 𝗯𝗲 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗲𝗿𝘀.
𝗜𝘁 𝗺𝗶𝗴𝗵𝘁 𝗯𝗲 𝗵𝗲𝗹𝗽𝗶𝗻𝗴 𝘀𝗼𝗰𝗶𝗲𝘁𝗶𝗲𝘀 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗳𝗲𝘄𝗲𝗿 𝗼𝗳 𝘁𝗵𝗲𝗺.

We may look back and realize the real question was never:

“Will AI take jobs?”

It was:

“Could our economies keep growing without it?”

Curious how others see this. 🤔

Are we afraid of the wrong thing regarding AI? 

Let me know in the comments. 👇🏾👇🏾👇🏾

𝗔𝗜 𝗷𝘂𝘀𝘁 𝗰𝗼𝗹𝗹𝗶𝗱𝗲𝗱 𝘄𝗶𝘁𝗵 𝗮𝘁𝘁𝗼𝗿𝗻𝗲𝘆-𝗰𝗹𝗶𝗲𝗻𝘁 𝗽𝗿𝗶𝘃𝗶𝗹𝗲𝗴𝗲.
𝗧𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲 𝗵𝗮𝘀 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗮𝗿 𝗯𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗰𝗼𝘂𝗿𝘁𝗿𝗼𝗼𝗺.

A federal judge in the Southern District of New York just issued a ruling that should get the attention of anyone using AI for strategy work.

In this case, a defendant used Anthropic’s Claude to help think through a legal defense. When federal agents seized the AI outputs, he argued they should be protected under attorney-client privilege and the work-product doctrine.

𝗧𝗵𝗲 𝗰𝗼𝘂𝗿𝘁 𝗱𝗶𝘀𝗮𝗴𝗿𝗲𝗲𝗱.

The key reason: the AI interaction happened outside the direction of his attorneys. Claude is not a lawyer, and its terms of service state that user prompts may be used to train the model and may be shared with authorities. Because of that, the judge ruled there was no reasonable expectation of confidentiality.

In other words, the AI conversation was discoverable.

𝗙𝗼𝗿 𝗮𝗻𝘆𝗼𝗻𝗲 𝘄𝗵𝗼 𝗵𝗮𝘀 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁𝗹𝘆 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗲𝗱 𝘁𝗵𝗮𝘁 𝗔𝗜 𝘄𝗶𝗹𝗹 𝘀𝗼𝗼𝗻 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗵𝘂𝗺𝗮𝗻 𝗹𝗮𝘄𝘆𝗲𝗿𝘀, 𝘁𝗵𝗶𝘀 𝗿𝘂𝗹𝗶𝗻𝗴 𝗼𝗳𝗳𝗲𝗿𝘀 𝗮 𝘂𝘀𝗲𝗳𝘂𝗹 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝗰𝗵𝗲𝗰𝗸.

The legal system runs on relationships, responsibility, and accountability. None of those concepts map neatly onto a chat interface.

There are a few broader implications here.

1️⃣ First, AI prompts are not automatically private. If you are using public AI tools to think through strategy, assume those interactions could become part of a record.

2️⃣ Second, AI is not a substitute for privileged relationships. Legal protections rely on a human relationship and a clear expectation of confidentiality.

3️⃣ Third, this reinforces something many organizations are still catching up to: AI governance is not just about productivity. It’s about risk management.

If teams are using generative AI to explore sensitive strategy, litigation risk, or internal planning, 𝘁𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝗰𝗹𝗲𝗮𝗿 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝘄𝗵𝗲𝗻 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗵𝗼𝘀𝗲 𝘁𝗼𝗼𝗹𝘀 𝗰𝗮𝗻 𝗯𝗲 𝘂𝘀𝗲𝗱.

The takeaway isn’t “don’t use AI.”

It’s understand that how you use it changes the legal and reputational context around the work you produce.

Curious how others are thinking about this as AI becomes embedded in more professional workflows. Let me know in the comments! 👇🏾👇🏾👇🏾

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