Part 1 of 4: Augmented Intelligence in the Enterprise
We’re deep into the AI era—but most organizations are still stuck at square one.
They’ve run pilots. Built prototypes. Dabbled with GPT. Hosted innovation weeks. Maybe even hired a “Head of AI.”
And yet, when you walk into the average enterprise, very little about how people work has actually changed. Processes are the same. Decisions are the same. Outcomes? Also the same.
So what’s going on?
We don’t have a technology gap. We have an execution gap.
The Pattern: AI Pilots That Go Nowhere
Let’s be honest about what’s happening in most enterprises:
- A small innovation team prototypes something with OpenAI or internal models.
- The demo impresses leadership.
- It gets some attention—maybe a pilot or two.
- But the initiative dies quietly in legal, risk, compliance, procurement, or “other priorities.”
Sound familiar?
This is not a one-off. It’s a pattern.
The reasons are always some mix of:
- “We don’t have the right infrastructure”
- “This doesn’t align to our roadmap right now”
- “This makes people nervous—it could replace jobs”
- “Let’s revisit this next quarter”
Translation: no one knows how to make it real.
AI Isn’t the Problem. The Organization Is.
Most companies aren’t set up to operationalize AI. Not because they don’t want to—but because the way they work today is incompatible with the speed, ambiguity, and experimentation that AI demands.
The blockers are rarely technical. They’re structural:
- Org charts built around predictability and control
- Decision-making processes optimized for low-risk, linear work
- Incentives that punish failure and reward “steady as she goes”
- Political structures that resist anything that threatens power or budget
Innovation gets stuck because execution is designed to protect the status quo.
And AI—especially the kind that could actually change how work gets done—isn’t neutral. It challenges workflows. It challenges ownership. It challenges control.
The Reframe: From Artificial to Augmented Intelligence
This is why I stopped talking about “Artificial Intelligence” and started talking about Augmented Intelligence.
It’s not just semantics. It’s strategic positioning.
Most people hear “Artificial Intelligence” and think:
- Replacement
- Automation
- Black-box decisions
- Risk
But “Augmented Intelligence”?
That’s different.
It means:
- Enhancing human decision-making
- Speeding up existing workflows
- Eliminating busywork, not judgment
- Helping teams focus on what matters
It’s a way to lower the threat level—without lowering the ambition.
Augmented Intelligence asks:
How do we make our smartest people faster, more effective, and better supported by the systems around them?
That’s a question every leader can and should engage with.
This Series Is About Making It Real
Over the next three posts, I’ll break down what it actually takes to move from AI theory to execution inside large, complex organizations.
We’ll cover:
- A three-lens framework for assessing adoption readiness
- Why so many initiatives die trying to cross the chasm
- What it takes to make Augmented Intelligence drive real business impact—both top line and bottom line
Because here’s the truth:
You don’t need more AI pilots. You need smarter execution.
And that starts by understanding where your org really stands today.
Coming up next: Part 2 — Where AI Breaks (and Where It Works): A 3-Lens Framework for Adoption.