Slide 1 / The model

E3: Evangelize, Enable, Empower

E3 is the core work product I bring to teams: a recipe for the journey to AI native, earned the hard way at Microsoft FastTrack through experience and iteration, and now run deliberately as a repeatable system.

E1

Evangelize

Create belief and excitement by showing real agents solving real problems. Demand comes from working examples in front of the people who own the work, discussion about how their manual effort and repeatable processes have been solved with AI, not from slideware or generic training.

E2

Enable

Help people do by direct coaching and solutioning their actual problems, with guidance on infrastructure, governance, telemetry, starter patterns, and learning cadences that let people build safely. Enablement turns enthusiasm into governed, measurable outcomes.

E3

Empower

Demo days, production use, and reusable patterns that teams run on their own, without a consultant or central group driving every step. Leaders emerge and coach others.

Step 1: An honest assessment of where your team and leaders are on the journey to AI Native.
Step 2: The E3 motion that accelerates you up and to the right.
Slide 2 / The proof

What the E3 model produced on my team

Most recent measured impact >57%

Reduction in manual work in under a year.

Production agents121

In real workflows, not pilots.

Interactions42,309

Real operational demand.

Hours saved13,188

Capacity returned to the business.

Throughput+36%

More engagements, more impact.

Active users889

Citizen builders, not a central team.

The rule for every engagement: the deliverables are usage and productivity, not documents and plans.
  • Measured from day one. Every agent was treated as an operational investment with telemetry: runs, users, adoption, hours saved. Evidence beat anecdotes, and evidence pointed continuous improvement at the next real opportunity.
  • Citizen development raised the water table. The people who own the work became the builders, product owners, and continuous-improvement leads. The central Garage team became an enablement engine and a governance backstop, not a build factory.
  • The management lesson. AI scales when leaders stop treating it as a tool rollout and start treating it as an operating model: culture, governance, measurement, reusable patterns, and owner-builders together. And leaders lead from the front, ahead of the pack in AI Native practices: teams follow 90% what leaders do and 10% what leaders say. AI requires leadership in both thought and adoption.
Slide 3 / In practice

E3 in practice: the journey to an AI-native team

The three stages translate to any organization. The people who own the work are your citizen developers; the goal is not a technology rollout, it is excitement, then capability, then ownership, one team at a time.

E1

Evangelize with working examples

Belief comes from watching a peer's real work change. Put people in front of their colleagues presenting AI wins they conceived and completed in their own workflows. Working examples in front of peers, never mandates or generic training.

Show, don't tell

E2

Enable with guardrails

Start with simple problems that are real for the users, and coach people through solving their own work. Starter patterns, a team learning cadence, and demo days. Focus on people who self-select, celebrate every win and progress point: oversized celebrations drive momentum.

Usage grows engagement

E3

Empower the owners of the work

Ownership transfers. The people who own the work redesign it themselves, build and run their own agents, and drive the roadmap. Demo days every week, outcomes measured, bright spots celebrated and copied. AI tools are integrated into every activity.

Becoming AI Native

Run the E3 motion team by team, not as one organization-wide program. Every team sits somewhere different on the AI Native journey, so place each one first with the assessment, then run the recipe from where they actually stand. Meet them where they are.
Slide 4 / Lessons

What we learned, and guidance for the journey

  • It ain't all AI. Roughly 80/20 to start with: most of the win is process clarity and plumbing, with the model on top. Map the work before you automate the work.
  • You don't scale with code, you scale with clarity. Clear roles, clean documents, and simple routines are what make AI reliable, in any domain.
  • Value isn't declared, it's instrumented. Decide on day one what evidence of progress looks like for each team, and measure it. Evidence beats enthusiasm in every review.
  • The introduction shapes the adoption. How an AI capability is first shown determines how it is understood and embraced. Demo days by peers outperform mandates from above, every time.
  • Teammates, not tools. Agents get names, owners, and roadmaps. The language shift drives the behavior shift.
  • Sustaining isn't a hobby. Someone has to own the operating model after the demo: cadence, governance, telemetry, and the next cohort. Name that team before scaling.
  • Teach what's underneath. The technology moves fast and is accelerating exponentially, so any specific technique has a short shelf life. The durable skills are judgment, adaptability, and knowing where to put the effort.
The technology isn't waiting, and neither can your people. Start with an assessment, pick one team, set a weekly working cadence and a monthly review, and ask the same question at every checkpoint: where are we now, and what moves us up and to the right from here?
Appendix / Who is talking

Jeff James: three decades, four chapters

26+ years at Microsoft, most of it making enterprise operations run at scale. Now building agent fleets in the open and helping teams become AI native, bringing decades of hard-won experience to teams that are moving too fast to learn it the slow way.

Before Microsoft

Attachmate, then a startup

Attachmate, 1991 to 1998: support technician through program management, systems engineering, and product management. Drove engineering change from direct customer experience.

RevX.net, 1998 to 2001: the startup years, wearing every hat from support and IT to business development and product design while the company grew from six people to fifty, then shrank back to fifteen. First consulting gig at age 11: a Lotus 1-2-3 worksheet for my dad's marketing firm.

Microsoft, 2001 to 2026

Enterprise operations at scale

Engineering GM and core team member that built Office 365: solution architect on MMS and BPOS with the first managed-service customers, then sales executive (Global Black Belt) closing the first productivity-cloud deals in Japan and US life sciences.

Built FastTrack Architects from concept to a global organization, including the Customer Health metric that now sits on an EVP scorecard. Most recently GM of New Commerce FastTrack: the modern-commerce transition for roughly 40,000 enterprise customers, influencing more than $34B in MCA-E cloud revenue last fiscal year.

What FastTrack is

Microsoft's Customer Success Engine within Engineering

FastTrack is the program that helps enterprise customers deploy, adopt, and get value from Microsoft cloud services. It is an engineering and operations organization, not a sales one: delivery PMs, feedback loops into the product teams, and the machinery that keeps thousands of engagements moving.

Most recently

Agentifying FastTrack through E3

The last act at Microsoft: turning FastTrack operations from manual knowledge work into an agentified operating model. The path we took became the E3 model (Evangelize, Enable, Empower), the subject of this deck.

FY26outcomes
121production agents
>57%less manual work
+36%throughput
13%headcount efficiency
13,188hours saved
Appendix / Where to dig deeper

References

Everything here is public or one click away. Lessons from doing, not theorizing.

reboundman.com

The hub: an overview of all the work, the app portfolio, and the SkillWorks library.

reboundman.com/fleet/

The fleet page: the big picture, the stage-by-stage board, and the full roster, animated.

github.com/ReboundMan

The public repos: TokenTray, ReboundMan-WordMD, and ReboundMan-ReleaseToolkit.

signalnotsentiment.com

The blog: AI agents and enterprise transitions, written from the operator's chair.

The X post that started the vault

The spark for the whole system, and the one reference here that isn't my content. Everything else on this slide grew from the idea in this post.

algomintai.com/insight

AlgoMint Insight: the weekly digital-finance updates, researched, written, and published by an agent fleet of its own.