The Three Faces of AI: Chat, Embedded, and Headless

Kristi Cantor

And How They Work Together to Get Real Results

Everybody wants AI to do the heavy lifting.
But the real trick? Getting it to lift the right things.

I can’t tell you how many times I’ve seen it now. Someone gets excited, spins up an AI project, loads it with data, and… nothing. Or worse — something that looks smart but doesn’t move the needle one bit.

The problem isn’t AI. The problem is we keep treating it like one giant brain that should magically “get” our business.

It doesn’t. Not yet, anyway.

And yet, every quarter, another executive buys into the myth of an all-knowing AI oracle that’ll solve everything. You can almost set your watch by it.

What actually works? Understanding that there are different types of AI in business, each with a distinct job to do. There are three of them — Chat, Embedded, and Headless AI — and when they start working together, that’s when things get interesting.

If Chat AI is your front desk, Embedded AI is your team, and Headless AI is the wiring behind the walls — none of it works if you don’t connect the circuits.

The pressure to “get AI right” has never been higher. Here’s the honest truth: most of what’s being built right now is aimed at the wrong layer — or all of them — and it’ll need a rebuild once the dust settles.

Chat AI: Great at Conversation, Terrible at Context

This is the one everyone knows. Chat AI is the friendly face — Copilot in your apps, natural language queries like “Show me late vendor invoices.”

It’s friendly. It’s flashy. It makes execs feel like, finally, we’ve democratized data!

And it’s not wrong — Chat AI is great for discovery. It lowers the barrier to insight. You can just ask questions like a human instead of begging someone in IT to write a query.

But here’s the catch: Chat AI’s only as good as what’s under the hood. No semantic model, no structure, no context — and it’s basically guessing with confidence. You’ll get an answer that sounds right, formatted nicely, maybe even with a chart… but was it the right data?

That’s the gamble.

It’s like having a brilliant intern who talks fast, looks sharp, and has no idea where the files are.

Chat AI lowers friction. Great. But that only matters if the foundation underneath it can hold the weight.

Embedded AI: Quietly Making Everyone Smarter

This is where AI starts actually doing work instead of just talking about it.

Embedded AI lives inside the tools you already use. Power BI suggesting visuals. Excel quietly catching formula errors before they spread. Fabric Copilot spotting patterns you didn’t know to look for.

It’s not headline material, but it’s where the wins stack up:

  • Faster close cycles because anomalies flag themselves.
  • Cleaner forecasts because the system knows what “normal” looks like.
  • Fewer surprises because predictive insights surface before problems become crises.

Nobody notices it’s happening — they just feel like work got easier. But the CFO sure notices when the errors stop.

This is where ROI stops living in PowerPoint decks and starts showing up in the real world. It’s not sexy. Nobody’s tweeting about it. But it’s the difference between “AI project” and “AI that’s actually useful.”

Headless AI: The Engine That Just Works

Now we’re talking about the quiet stuff — the kind of Headless AI that hums in the background while everyone else takes the credit.

Headless AI doesn’t chat, it doesn’t explain, it doesn’t ask for feedback. It just does things.

A vendor invoice comes in late, and the schedule adjusts itself. Forecasting models can rebalance inventory when consumption patterns shift. Metrics anomalies route to the right people before anyone even knows there’s a problem.

It’s the AI equivalent of fixing the leaky faucet you didn’t realize was wasting half your water bill.

Headless AI isn’t about making people faster. It’s about making the system faster.

And here’s the thing — when it’s done right, you barely notice it’s there. You just stop fighting fires that never should’ve existed in the first place.

But it only works if the other two layers are solid. Chat and Embedded AI might get the spotlight, but Headless AI quietly runs the business.

Click the image above and listen to the Raw Data with Rob Collie episode that inspired this blog.

AI That Works Together Works Best

Here’s what separates the companies doing AI from the ones just talking about it.

They’re not chasing the “one platform to rule them all.” They’re building a network of small, purpose-built intelligences — each doing one job really well, and all of them talking to each other.

  • Chat AI gives leaders visibility.
  • Embedded AI gives teams speed.
  • Headless AI keeps the whole machine humming without the drama.

That’s the formula.

The goal isn’t AI everywhere — it’s AI where it belongs.

At P3, we see it across every industry. The companies winning with these types of AI aren’t the ones spending the most. They’re the ones who start small, prove value fast, and build on a foundation that can actually handle it.

You don’t need one big brain. You need a nervous system — sensors, reflexes, and instincts that work together.

If you haven’t read our post on Why AI Needs a Data Model, start there — it’s the foundation that makes all three layers work.


Where Should You Start?

Start small. Always.

Find one process that’s painful and measurable — the kind that keeps showing up in meetings because nobody owns it and everyone hates it.

Fix that one. Make it smarter. Learn what your data needs to look like for AI to actually help. Then move on to the next one.

That’s how the right type of AI scales — one real win at a time.

Focus AI before you scale.

When you’re ready to see where AI could make the biggest impact in your business, start with a conversation, not a transformation. We help companies find the high-leverage spots — the ones where automation pays for itself before the next budget meeting.

Forget the big-bang transformation. That’s how the hype gets sold. Real progress happens quietly, in the systems that finally start working the way they always should have.

That’s the trick — not getting AI to lift everything, just getting it to lift the right things..

Big firms talk strategy. We build systems that work.

If you’re ready to see how AI can start paying for itself, let’s build the first piece together.

Read more on our blog

Get in touch with a P3 team member

  • This field is for validation purposes and should be left unchanged.
  • This field is hidden when viewing the form
  • This field is hidden when viewing the form

This field is for validation purposes and should be left unchanged.

Related Content

Why Small AI Wins Big

The Oracle Myth Let me tell you about Bill. Bill Krolicki runs

Read the Blog

Why the Best AI Solutions Are Only 10% AI

The Hybrid System People expect AI to do everything.That’s the problem. The

Read the Blog

Why AI Needs a Data Model (Just Like Power BI Did)

The Myth of the Magic Brain Pet Peeve of the week: everyone

Read the Blog

AI Fatigue Is Real (And Your Context Window Is to Blame)

When AI Gets Punch-Drunk You know that feeling when you’ve been in

Read the Blog