#46 - Stop Calling Copilot Your AI Strategy (at home or at work)

Whether it’s Copilot, Gemini, or ChatGPT, we are in the first inning. Adoption is not the finish line. Embodiment is.

I hear two very different stories almost every day.

One version goes like this:
“We’ve rolled out Copilot and people don’t love it. It doesn’t feel like the future of work. The AI tools at home seem more capable.”

And then, just this past Sunday at lunch, my uncle leaned across the table and said with pride:
“We use Copilot for everything. And we love it.”

Both are true. Copilot is not failure, nor is it transformation. It is inning one. For some, that feels like letdown. For others, progress. The reality: we are still at the beginning.

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City Cars and Freight Trucks

At home, ChatGPT or Gemini feels like a city car: nimble, fun, quick to get you from point A to B.

At work, enterprise Copilot is a freight truck: heavier, slower to maneuver, but built for scale and safety.

Neither is better — they’re built for different roads. Frustration shows up when people expect the truck to drive like the city car.

Home AI is a city car. Enterprise AI is a freight truck. If you expect one to act like the other, you are bound to be disappointed.

Where frustration shows up

Here is what I hear from employees all the time.

“Copilot takes longer to explain what I need than just doing it myself.”
“I get a nice summary of my meeting, but it doesn’t tell me what decisions were actually made.”
“It rewrote my draft email, but it sounds generic, not like me.”
“I tried asking a bigger question, but it only pulled from my files. It didn’t give me the broader context I needed.”

For them, Copilot feels like promise without payoff.

But then there are the uncles, the aunts, the teams inside some companies who say, “This is incredible. We are using it all day long.”

Two truths. Both valid. Both signals that we are only at the beginning of a long game.

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Why this frustrates employees

This split between home AI and work AI is exactly why frustration shows up at the office.

At home, people are experimenting with ChatGPT or Gemini. It feels fast, fun, and creative. You can ask anything and get something back. It feels like freedom.

At work, Copilot often feels slower, more constrained, and less “magical.” It is limited to the files and systems inside Microsoft 365. That makes it safer, but it also means employees bump into walls they don’t experience at home.

The result is a mismatch in expectations. People expect the city car to drive like the freight truck. When it doesn’t, they assume the truck is broken. In reality, it is just doing a different job.

So what do you do with this?

• First, acknowledge the difference out loud. Do not oversell Copilot as if it is the same as ChatGPT. Communication is so important here.

• Second, show people the kinds of questions and tasks Copilot is best at — quick productivity boosts inside the flow of work. I highly recommend “prompting parties”

• Third, make the bridge to bigger value. Help employees see how small Copilot wins can connect into larger workflows and decisions.

The frustration is not a failure. It is an opening. Employees are telling you they believe AI can and should do more.

Frustration is not rejection. It is expectation waiting to be met.

Adoption is not the win

Most organizations stop at adoption. They measure success by how many licenses were purchased or how many people logged in. That is like celebrating gym memberships instead of fitness.

Adoption is necessary, but it is not sufficient. It proves availability, not impact.

This is why, according to MIT research, nearly 95 percent of GenAI pilots are failing. At first glance, that looks dire. But the truth is, most of those pilots never defined success. If the only goal was “turn it on and see what happens,” of course it looks like failure.

The question is not whether 95 percent of pilots fail. The question is: What were they trying to prove?

If success means “Did people log in?” then you are already failing.
If success means “Did we change a workflow, save measurable time, or improve a decision?” then now you are testing for value.

The 95 percent statistic does not mean GenAI is not working. It means organizations have not agreed on what winning looks like.

Logins are the receipt. Embodiment is the result.

Micro AI vs. Macro AI

Micro AI is the city car. Task helper, quick win, focused on the flow of work.

Netflix recommending your next show.
Copilot drafting your email reply in Outlook.
Teams automatically summarizing your meeting.

Macro AI is the freight truck. Workflow transformer, system integrator, reshaper of entire processes.

ChatGPT planning an entire trip with flights, hotels, and weather.
AI predicting workforce needs by connecting HR, finance, and sales data.
AI monitoring supply chains in real time to adjust logistics.

Both matter. Micro AI builds comfort and trust. Macro AI delivers transformation.

Micro AI gets you through your to do list. Macro AI asks if you are working on the right list in the first place.

Why Employees Feel Let Down

Here’s what I hear most often:

• “It takes longer to explain what I need than just doing it myself.”

• “The meeting summary doesn’t tell me what decisions were actually made.”

• “It rewrote my email, but it doesn’t sound like me.”

For them, Copilot feels like promise without payoff. Meanwhile, other teams rave: “We use it all day long.”

Two truths. Both valid. Both signals we’re still early.

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Discontent is data

If Copilot feels underwhelming, it is not rejection. It is feedback.

Discontent shows us the gap between what AI is doing today and what people believe it could do tomorrow. Leaders should not hide from that gap. They should use it.

Acknowledge the difference between city car AI at home and freight truck AI at work.
1) Deliver quick wins tailored to roles.
2) Create secure sandboxes for experimentation.
3) Celebrate stories of time saved or work improved.
4) Connect AI to purpose, not just efficiency.

Discontent is data. Your people are not rejecting AI. They are asking for a version that matters.

Prove It with Value

Run an AI Value Shark Tank. Have teams pitch use cases. Judge them by impact: What problem did it solve? How much time did it save? Did it improve quality? Would people choose it voluntarily?

If it can’t survive the tank, it’s not value — it’s noise.

If your AI use case cannot survive the shark tank, it is not creating value. It is just treading water.

The journey ahead

So which is it? Is Copilot a disappointment or a breakthrough? The answer is yes. Both truths exist.

Some will say, “It takes longer to explain than just do it myself.” Others will say, “We use it for everything and we love it.” Both are right, because we are only in the first inning.

Mile one is not the marathon. Inning one is not the game. The only failure is treating the beginning like the end.

“Ok Copilot, send this to everyone at work so we can stop debating if you’re good or bad and start asking how to make you matter.”

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About Jason Averbook

Jason Averbook is a globally recognized thought leader, advisor, and keynote speaker focused on the intersection of AI, human potential, and the future of work. He is the Senior Partner and Global Leader of Digital HR Strategy at Mercer, where he helps the world’s largest organizations reimagine how work gets done — not by implementing technology, but by transforming mindsets, skillsets, and cultures to be truly digital.

Over the last two decades, Jason has advised hundreds of Fortune 1000 companies, co-founded and led Leapgen, authored two books on the evolution of HR and workforce technology, and built a reputation as one of the most forward-thinking voices in the industry. His work challenges leaders to stop seeing digital transformation as an IT project and start embracing it as a human strategy.

Through his Substack, Now to Next, Jason shares honest, provocative, and practical insights on what’s changing in the workplace — from generative AI to skills-based orgs to emotional fluency in leadership. His mission is simple: to help people and organizations move from noise to clarity, from fear to possibility, and from now… to next.

You can email at jasonaverbook@gmail.com or send message at LinkedIn to connect.