AI Adoption Stages

Three-stage progression from fluency to process optimization to growth inflection

Asha Sharma
How 80,000 companies build with AI

AI Adoption Stages

"More broadly, I think there's a pattern that's starting to emerge for successful companies... Where companies fail is that they're doing AI for AI's sake." - Asha Sharma

What It Is

A three-stage maturity model for how organizations successfully adopt AI, based on patterns Asha Sharma has observed across thousands of companies building on Microsoft's AI platform. The key insight is that AI adoption is sequential—you can't skip stages, and each stage builds capability for the next.

Companies that fail typically jump straight to stage three (pursuing growth), or scatter their efforts across too many AI projects without a coherent blueprint.

How It Works

Stage 1: Universal AI Fluency

Goal: Everyone uses AI in daily workflows

What it looks like:

  • Everyone is using some sort of AI copilot or assistant
  • The organization loses its fear of AI
  • People understand how AI "can raise the ceiling and lower the floor for all sorts of skills and tasks"

Why it matters: This stage builds organizational literacy. You can't effectively deploy AI for business outcomes if people don't understand how it works, what it's good at, and where it fails.

Stage 2: Process Optimization

Goal: Apply AI to existing processes for measurable improvement

What it looks like:

  • Take a process that already exists
  • Apply AI to make it better
  • Measure the impact

Examples:

  • Customer support automation
  • Reducing fraud cure time from 15 days to 10 days
  • Any process with measurable inputs and outputs

Why it matters: This stage creates the organizational muscle for AI deployment:

  • You learn to map processes to AI capabilities
  • You build measurement and observability
  • You see P&L or intrinsic benefits
  • You learn what works (and doesn't) in your specific context

Stage 3: Growth Inflection

Goal: Use AI to create new value, not just optimize existing processes

What it looks like:

  • Improving customer experience → better LTV and retention
  • Co-creating new concepts or categories
  • Moving from embedded agents (tools) to embodied agents (autonomous)
  • Taking on exponentially more tasks

Why it matters: This is where AI transforms the business, not just improves it. But you can only succeed here if you've built the foundation in stages 1 and 2.

How to Apply It

  1. Diagnose your current stage - Where is your organization honestly? Be careful of overestimating—widespread executive AI demos don't equal stage 1 completion.

  2. Complete each stage before advancing - Resist pressure to jump to "transformative" AI projects before the foundation is solid.

  3. Treat it like a real investment - Successful companies "aren't treating AI like a real investment" and "don't have the measurement and observability and evals all set up."

  4. Avoid AI for AI's sake - "Where companies fail is that they're doing AI for AI's sake. They have a ton of projects that they're kicking off at the same time without a blueprint."

  5. Build for the slope, not the snapshot - Technology will keep changing. Build capability to adapt, not just solve today's problems.

When to Use It

  • When planning your organization's AI strategy
  • When evaluating AI maturity (yours or others')
  • When prioritizing AI investments
  • When explaining to leadership why foundational work matters
  • When resisting pressure to jump to "sexy" AI projects prematurely

Anti-Patterns

Scattered Experimentation: Too many AI projects without a coherent blueprint or measurement strategy.

Premature Transformation: Trying to create AI-native products before the organization has basic fluency.

Missing Measurement: No observability or evals to know if AI initiatives are actually working.

Technology Chasing: Adopting new AI tools because they exist, not because they solve specific problems.

Source

  • Guest: Asha Sharma
  • Episode: "How 80,000 companies build with AI: Products as organisms and the death of org charts"
  • Key Discussion: (09:35-11:54) - Describes the pattern of successful AI adoption
  • YouTube: Watch on YouTube

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