I Stopped Using AI Like a Chatbot. Everything Changed.

For a while, I thought I was pretty good at using AI.

I used it for research, writing, brainstorming, analysis, recruiting, business planning and solving problems faster.

I learned how to prompt it better.

And it was useful.

But at some point, I realized I was still treating one of the most transformative technologies I've encountered like a really sophisticated search box.

Ask a question. Get an answer. Ask another question. Repeat.

The technology was changing.

The way I was using it wasn't.

So over the past several months, I've been experimenting with something very different.

Instead of asking:

“What can AI help me with?”

I started asking:

“What responsibilities can I give AI?”

That sounds like a small distinction.

It isn't.

From assistant to operating system

I started building my AI environment less like a collection of applications and more like an operating system around the way I work.

There is a central AI that understands my priorities, projects and the way I like to operate.

But I don't expect one AI to do everything.

I'm increasingly building specialized capabilities around it.

One focuses on intelligence and research.

Another supports recruiting and Talent Intelligence.

Others can help with sourcing, monitoring, business development, content, projects and operations.

And rather than keeping all of those capabilities isolated, I'm gradually connecting them to the systems where the actual work happens.

Email.

Calendars.

Project management.

Knowledge repositories.

Recruiting systems.

Business-development systems.

And other tools.

The names of those applications aren't particularly important—although I'll admit that building the connections has been half the fun.

The connections between them are.

Because once AI has appropriate context and controlled access to the systems surrounding your work, something interesting begins to happen.

You stop having to initiate every individual task.

That's the part that changed my thinking

I don't want to wake up every morning and ask AI:

“Anything interesting happen overnight?”

I want the system already watching the areas that matter to me.

I don't want to remember every company, technology or business development I wanted to monitor.

I want specialized systems doing that monitoring and bringing something to me when it actually matters.

I don't want AI making every decision.

I want AI gathering the information, organizing it, analyzing it and preparing the decision so I can spend more time making the decisions that require judgment.

That's a very different model.

The old model looks something like:

Human → Prompt → AI → Answer

The model I'm working toward looks more like:

Human → Objective → AI Coordination → Specialized Capabilities → Human Decision

And apparently, some very large companies are reaching a similar conclusion.

Then I saw what Cisco was doing

Cisco recently gave approximately 90,000 employees their own AI agent. Source: Steve Clayton on LinkedIn

That number makes a great headline.

But the number isn't what caught my attention.

The architecture did.

Cisco's system can connect with enterprise applications, use specialized capabilities and help employees execute work rather than simply answering questions.

Cisco executive Steve Clayton described his own experience with the system as a “gamechanger.” The first time he used it, he said he treated it essentially like a chatbot.

Then his approach evolved. The system became something capable of using specialized skills and proactively producing intelligence for him rather than simply waiting for another prompt.

That sounded very familiar.

The LinkedIn News email covering the story led with the 90,000-person rollout and highlighted Clayton's experience, along with a broader discussion among technology leaders about what the architecture could mean.

One reaction to the story captured the idea particularly well:

The headline is the headcount. The lesson is the architecture.

Exactly.

AI needs a job description

This has also changed how I think about AI agents.

I don't think the goal should be creating dozens—or hundreds—of clever little bots because we can.

Each one should have a reason to exist.

A mission.

Responsibilities.

Access to specific information.

Permission to perform certain actions.

And very clear boundaries around what it cannot do.

I've started thinking about those boundaries in three simple categories.

• Green: AI can research, monitor, organize, analyze and prepare.
• Yellow: AI can prepare an external action, but a human approves it.
• Red: consequential actions involving money, contracts, sensitive information, deletion, publishing or other significant decisions require explicit human authorization.

The more capable AI becomes, the more important those distinctions become.

Autonomy without governance isn't an AI strategy. It's just a very fast way to create new problems. I've written about that risk before in “AI Might Kill Us All.”

This isn't about replacing people

That's another reason I think some of the discussion around AI misses the bigger opportunity.

The interesting question isn't simply:

“Which jobs can AI replace?”

A much more useful question might be:

“Which parts of my job shouldn't require my time anymore?”

Searching for information?

Monitoring developments?

Moving information between applications?

Organizing research?

Preparing summaries?

Tracking things I might otherwise forget?

Building a first analysis?

Those activities consume enormous amounts of knowledge-worker time.

Give more of that work to AI and something valuable happens.

The human doesn't necessarily disappear.

The human moves up the decision chain.

More judgment.

More strategy.

More relationships.

More creativity.

More decisions.

Less digital housekeeping.

We're still early

I'm certainly not presenting my own system as finished.

Far from it.

I'm still experimenting.

Connecting things.

Breaking things occasionally—and sometimes on purpose.

Learning what should be automated and, equally important, what absolutely shouldn't be.

But I've become convinced that the next stage of AI adoption isn't going to be defined by who writes the cleverest prompts.

It will be defined by who figures out how to design work around intelligence.

That idea is worth repeating: design work around intelligence.

And it has changed the way I think about AI completely.

The breakthrough wasn't getting AI to give me better answers.

It was giving AI responsibilities.

Once you make that transition, it's difficult to go back to thinking of it as a chatbot.

And I suspect that before long, asking someone—

“Do you use AI?”

—will sound almost quaint.

The more interesting question will be:

“What does your AI team do for you?”