Part 3 of 4: Augmented Intelligence in the Enterprise
By now, the pattern is painfully familiar.
By now, the pattern is painfully familiar.
A team runs a successful AI pilot.
The metrics look strong.
Leadership is excited.
There’s even a slide about expansion.
And then?
The project dies.
Or worse, it limps along, partially adopted, rarely used, and ultimately forgotten. A cautionary tale dressed up as a case study.
Why does this happen?
Because building a proof-of-concept is easy compared to crossing the chasm. The space between early experimentation and meaningful, scaled execution.
What Is the Chasm?
The chasm, originally coined by Geoffrey Moore, is the brutal gap between early adopters and the early majority. It’s not a theoretical idea; it’s a lived experience inside most enterprises.
Early adopters (innovation teams, digital labs, forward-thinking product managers) are:
- Excited by new possibilities
- Comfortable with ambiguity
- Motivated by experimentation and learning
The early majority (core business units, ops teams, compliance stakeholders) are:
- Focused on delivery
- Incentivized to avoid risk
- Accountable for outcomes, not experiments
The chasm is the space where ambition meets organizational reality.
And most AI initiatives fall straight into it.
Why the Chasm Devours So Many AI Projects
Let’s break down the common friction points:
1. Risk Aversion Is Built Into the System
By design, large enterprises are structured to minimize risk.
That’s a good thing—until you need to change something.
AI projects trigger risk in multiple dimensions:
- Data security and privacy
- Regulatory and legal exposure
- Brand and reputational risk
- Fear of job displacement
Even when the pilot proves valuable, the idea of scaling AI often runs into a wall of governance, approvals, and fear.
Insight:
Most orgs don’t say “no” to AI. They just bury it under process until the team gives up.
2. Middle Management Bottlenecks the Momentum
Leadership may be sold. The innovation team is excited.
But middle managers? They’re caught in the middle, with performance targets, budget constraints, and delivery pressures.
For them, AI is a wildcard.
- It threatens existing processes.
- It adds ambiguity to roadmaps.
- It introduces tooling they don’t fully understand.
Without strong support, middle layers act as friction, not force multipliers.
3. Political Capital Gets Spent Elsewhere
AI pilots often rely on a few champions.
But those champions have limited political capital, and eventually they have to pick their battles.
If the broader org isn’t asking for AI (yet), pushing it forward can feel like swimming upstream. Especially if:
- The tech team is focused on platform stability
- Ops is focused on regulatory remediation
- Product is under pressure to ship roadmap commitments
Innovation dies not because it isn’t good—but because it isn’t urgent.
4. The Success Metrics Don’t Translate
What counts as success in a pilot doesn’t always carry over to production.
- Pilots often optimize for precision, novelty, or model performance.
- The business wants reliability, predictability, and user adoption.
That disconnect creates a credibility gap.
And without a clear throughline from pilot metrics to real business KPIs, the AI effort struggles to win the next round of investment.
5. The Integration Cost Was Never Scoped
Pilots are tidy. Real systems are not.
Shipping AI at scale means:
- Data pipelines
- Model monitoring
- UX changes
- Training
- Documentation
- Legal review
- Change management
- Ongoing iteration
Most pilots are scoped like side projects, but scaling them requires real product and platform investment.
And when that investment isn’t accounted for, the effort stalls out mid-flight.
How to Actually Cross the Chasm
Some organizations are starting to make it across. Here’s what they’re doing differently:
✅ They Start With Integration in Mind
From Day 1, they ask:
- “What happens if this works?”
- “Where will this live?”
- “Who owns it after the pilot?”
They treat the pilot as a wedge, not a one-off, so it’s easier to transition from demo to deployment.
✅ They Align Early With Risk and Compliance
Instead of building first and asking for forgiveness later, they bring legal, compliance, and risk teams into the problem-solving process up front.
This doesn’t slow things down, it accelerates credibility.
✅ They Find Real Use Cases With Embedded Demand
The best use cases aren’t “cool.” They’re painful.
- Manual processes
- Decision fatigue
- Bottlenecks in service or fulfillment
- Things people hate doing but have to do
When you embed AI into high-friction workflows, the demand comes from the bottom up, not just the top down.
✅ They Fund the Full Lifecycle, Not Just the Pilot
They know a model is not a product. And a prototype is not a launch plan.
So they budget for:
- Deployment
- Training
- Monitoring
- Support
- Continuous improvement
They treat AI like a capability, not an experiment.
Coming Up Next: Part 4, From Execution to Impact
In the final part of this series, we’ll shift from diagnosis to action.
We’ll explore what it takes to turn Augmented Intelligence into a repeatable execution engine, one that drives measurable business value, at scale.
Because building smart systems is one thing.
Creating smarter organizations? That’s the real goal.
Creating smarter organizations? That’s the real goal.