The Enterprise AI Adoption Ladder
Three sequential stages that separate companies winning with AI from those spinning their wheels
- Difficulty
- Moderate
- Time to result
- ~months to results
- Steps
- 3
- Confidence
- 90%
A repeatable progression Sharma observes in companies that succeed with AI: first make everyone AI-fluent, then apply AI to an existing process to prove measurable impact, then use AI to inflect growth. Companies that fail skip the ladder and launch scattered projects without a blueprint or the ability to swap technologies.
Origin
Distilled by Asha Sharma from patterns across 80,000+ companies building on Microsoft's AI platform (~70,000 enterprise AI tools launched in the prior year alone).
Core principles
- 01Fluency first — nobody is afraid of AI and everybody uses a co-pilot daily
- 02AI raises the ceiling and lowers the floor across skills and tasks
- 03Prove impact on an existing process before chasing growth
- 04Build for the slope, not the snapshot — bet on a swappable platform layer, not any one tool
- 05Treat AI like a real investment with measurement, observability, and evals
How to run it
- 1
Make everyone AI-fluent
Get every person using some co-pilot or AI in their day-to-day workflow so the organization understands and isn't afraid of it. This is job one.
Pro tip Fluency lowers the floor and raises the ceiling for all skills — treat it as universal, not a specialist function.
- 2
Apply AI to an existing process and measure impact
Take a process you already run — customer support, fraud review — map it out, apply AI, and measure the P&L or intrinsic benefit.
Pro tip Pick a process with a clear metric so the win is undeniable, e.g. taking fraud cure time from 15 days to 10 days.
Watch out Don't move on until you can actually feel the impact in the numbers.
- 3
Use AI to inflect growth
Once impact is proven and adoption is broad, deploy AI to bend the growth curve — improve customer experience for higher LTV/retention, co-create new categories, or move from embedded agents to embodied agents taking on exponential tasks.
In the wild
A company mapped its fraud process end-to-end, applied AI, and compressed the cure time.
→ Fraud handling dropped from 15 days to 10 days, a felt P&L benefit that justified further investment.
Common mistakes
AI for AI's sake with no blueprint
Kicking off many projects simultaneously without understanding how they fit the stack, with no measurement, observability, or evals.
Betting on a single tool
With ~70,000 enterprise AI tools and constant change, committing to one technology leaves you unable to swap components as the whole landscape shifts.
Is it for you?
Best for
Enterprise leaders and transformation teams trying to move from scattered AI pilots to compounding, measurable value
Not ideal for
Tiny startups where the whole company is already AI-native and the staged rollout is unnecessary overhead
From the transcript
“everybody becomes AI fluent. So I think everybody's using some sort of co-pilot or some sort of AI in their day-to-day workflows like job one”
“how can I take a process that already exists and apply AI to making it better? That might be uh something like customer support or…”
“where companies fail is that they're doing AI for AI sake. They have a ton of projects that they're kicking off at the same time…”
“you have to actually build for the slope instead of the snapshot of where you are”
From the episode
How 80,000 companies build with AI: products as organisms, the death of org charts, and why agents will outnumber employees by 2026
Asha Sharma (CVP of AI Platform at Microsoft)