Enterprise Systems
That Outlast
the Initiative

Fifteen years designing the operating infrastructure that lets large, complex organizations scale without losing execution quality. At Amazon, that meant billions in revenue. Everywhere else, it meant the same thing: a system that runs after you leave the room.

$3.51B
Tentpole revenue 2024 (+19.7% YoY)
500
Whole Foods stores on new operating model
70%
Global dashboard adoption, five weeks from zero
23%
YoY total program growth, Prime Day 2024
See the Work Let's Connect
Michael D. Moore

Core Capabilities

Operating Model Design Program Governance Zero-to-One Execution Enablement Architecture AI-Enabled Workflow Integration AI Governance Mechanism Design
Point of View

Individual productivity is not operational efficiency. The gap between them is the job.

Roughly 95% of enterprise AI pilots produce no measurable return, and the tools that do get used mostly make one person faster while the business stays flat. The 6% of companies that get real P&L impact share one trait: they redesign the workflow, the ownership, and the operating rhythm around the tool. The model was never the constraint. Governance was.

I closed that gap for fifteen years before AI existed as a tool, at Amazon scale: 500 stores adopting one operating model at 95%, fifteen teams moving pitch coverage from 32% to 80%, a training academy that only moved the business once it was paired with inspection and owners. Now I build the same layer around AI. Decision rights, review gates, evaluation routines, and cadence, so that what one person can do becomes what the organization does.

Sources: MIT NANDA, The GenAI Divide (2025). McKinsey, The State of AI (2026).

Case Studies

Three Systems. Measurable Scale.

Each engagement started with fragmentation. Each ended with a governed operating system that outlasted the initiative.

Amazon Ads

$3.51B

Tentpole Revenue 2024

Redesigned tentpole events from fragmented vertical efforts into a unified, phase-based commercial operating system. Pitch coverage went from 32% to 80%+. Prime Day 2024 grew 23% YoY. Closed-won AM revenue grew 46% YoY.

Whole Foods Market

500

Store Operating System

Designed a zero-to-one inventory visibility system across 500 stores with no source systems. Created the data layer that did not exist, reduced defects by 25%, and accelerated engineering timelines by six months.

L'Oreal at Amazon

110%

YoY Portfolio Growth

Designed and led the Brand Concierge operating model for a $400M book of business. Achieved 99% fill rate, 100% IDQ, and 110% YoY portfolio growth by treating the account team as an extension of the client organization.
Additional Proof Points 6 engagements

Amazon Restaurants (City Manager scope)

Pilot Authorship and Metro Strategy

3 pilots

Onboarding, Hotel, Apartment proposals

  • 15-module CM Training Leone v4 curriculum
  • 38 to 10 day cycle time reduction proposed
  • Q1 2017 Metro Strategy Doc authored

Amazon Fulfillment OAK4

Inbound and ICQA Operations

11 promos

In 3 months. Team Leader pipeline.

  • 15% stow rate gain (265 to 290 units/hr)
  • ICQA Standard Work 1.0 + PSolver v1/v2/v3
  • 300+ associates across IB Round 2 and Round 3

MDM Enterprises, LLC (DBA Dormroom Deliveries)

Pre-Platform Restaurant Delivery

2006-2009

Before DoorDash, Postmates, Uber Eats

  • Real LLC, QuickBooks, signed Commission Agreements
  • $1M+ annual revenue, ~3,000 student customers
  • Year-end report authored at age 22

Amazon Franchise Operations

Network Playbook Design

$3M+

Quarterly incremental revenue

  • 40 franchise partners scaled
  • 20% defect reduction across network
  • Playbook adopted org-wide from scratch

LCS Academy

Enablement Redesign

8 wks to 5

Ramp time, 100+ account managers

  • Live participation 50% to 85%
  • Curriculum expanded 30% to 40 modules
  • 90%+ satisfaction across six cohorts

Rocket Learning

Sales Infrastructure Design

90%

Win rate on competitive bids

  • $15M+ in contracts closed
  • 33% market share growth in 18 months
  • Sales infrastructure and playbook designed from zero
Track Record

How the Work Gets Described

Observed patterns across six years of cross-functional environments, enterprise programs, and people leadership at Amazon scale.

Sees the system, not just the problem

The feedback that recurs most consistently is the ability to operate at two altitudes simultaneously. Diagnose root causes at ground level, architect solutions that hold at scale. Multiple stakeholders across years independently landed on the same observation.

"His capacity to navigate controversial waters, find alignment between teams, and bring new insights to the table is impressive, allowing key initiatives to move from ideation to implementation."

Installs systems that run without him

The consistent theme across programs is not delivery. It is durability. From Whole Foods inventory operations to global Tentpole governance, what peers and managers noted year after year was not that the work was completed. It was that the work kept producing.

"His talent for taking ownership and scaling solutions across teams was instrumental in addressing challenges during leadout phases, successfully incorporating these approaches into the broader GTM strategy."

Earns the trust that makes cross-functional work move

The most commercially relevant signal in the reviews is how he operates in environments where authority is distributed. Working relationships across product, retail, sales, and engineering earned over time, and those relationships become the mechanism for execution.

"He was able to serve as the trusted expert in his space that stakeholders went to when they had questions. A top-notch partner who can go deep, while also thinking big and strategically at the same time."

Translates leadership intent into executable systems

The clearest signal from direct reports and leadership alike is clarity under ambiguity. Where others see competing priorities, he sees a sequencing problem. Where others see blocked initiatives, he sees the path through the org chart.

"He excels at breaking down challenges, translating leadership directives into clear, prioritized goals, and instilling a culture of accountability."

Observed across enterprise retail integration, global advertising programs, cross-functional product and operations, and people leadership at Amazon scale.

Six years of formal review signal across multiple organizations

AI Systems

How I Govern AI

Two operating systems, live in the field, built on one rule: the framework decides, the model assists, and a human owns every consequential call. Each one compresses a messy caseload into a single screen, rolls individual signals up into collective visibility, and moves the operator's time from discovery to intervention.

Deployed · Charter School Network, Northern California · 2026

Project Bridge

Counselor-Led Scholarship Readiness Platform

Project Bridge student dashboard showing a 53% readiness score, next best moves, aid readiness, and the Ask Sherpa AI coach
Student view. Readiness score, next best move, and AI coach with counselor escalation.
Solving for
Counselors carry caseloads in the hundreds and find out who is behind when the deadline has already passed. Students with real stories leave scholarship money unclaimed because nobody saw the gap in time.
What it does
Scores every student on profile, activities, stories, and recommenders. Surfaces the single next best move. Matches scholarships against readiness. Tracks FAFSA step by step. An AI coach answers the routine questions; the counselor gets the ones that matter.
Governance built in
AI does triage, the counselor owns the intervention. Extraction runs through a human review gate before it changes a profile. Cohort roll-up gives the network one view of who is behind without anyone digging. Rollout is phased with go/no-go criteria: readiness, then activation, then execution.
Operational result
A caseload compressed to one screen. Counselor time moves from finding the problem to fixing it.
Claude APINext.jsSupabaseVercelHuman review gate
In Deployment · Field Sales Organizations · 2026

BUILD Sales Execution System

Five-Stage Doctrine With an AI Reinforcement Layer

BUILD mobile home screen: Talk to the Coach Now, the five BUILD stages, and stage cards for prepping a call, knowing the customer, finding a deposit, and making the sale
Rep view. One coach, five stages, and a card for every moment a deal can break.
Solving for
Sales failures do not start at the close. They start in the prep. The manager is not in the truck before the call, not in the parking lot after it, and the debrief never happens. Every rep works the deal alone in exactly the moments where deals break.
What it does
Runs every deal through Begin, Understand, Improve, Lock In, Deliver. Pre-call walkthroughs set the objective, the minimum acceptable outcome, and what to avoid. Account-level tools, objections, and first moves. Stuck deal recovery, pre-close readiness, and a manager-facing call review.
Governance built in
The doctrine decides, the model assists. Every prompt is subordinate to the five-stage framework and a pattern library built from lived sales experience, not generic advice. Weekly momentum (sessions, calls prepped, objections worked) makes reinforcement inspectable, so a manager sees the cadence, not just the close rate.
Operational result
Every call gets prepped, worked, and debriefed against one doctrine. Individual reps get better on their own time, and the manager sees it as a weekly operating rhythm instead of a quarterly number.
Claude APIStructured promptingPattern libraryManager review loop
Saka One

Enterprise Discipline. AI Leverage. Real Access.

Saka One Enterprises is the operating company behind Project Bridge and BUILD. The thesis: the problem is rarely knowledge. It is access to guidance, coaching, and operational support that has always been rationed by budget and institution. AI changes the economics of access. The operating discipline decides whether anyone benefits.

Operating model · Step 1

Identify the access gap

Constraint first

Map the real bottleneck, define the outcome

  • Field discovery before product decisions
  • Outcome and owner defined before a line of code
  • Same method that produced the Whole Foods data layer

Operating model · Step 2

Create the support layer

Tools + playbooks

Systems people use and trust

  • Doctrine and playbook precede the software
  • Human review gates on every consequential output
  • Phased rollout with go/no-go criteria

Operating model · Step 3

Use AI where it expands capacity

Governed

Automate the repeatable, amplify the human

  • Context compression: a caseload or pipeline on one screen
  • Collective intelligence: individual signals roll up to the team
  • Operational improvement: time moves to intervention

See the Saka One operating bench and portfolio

Where This Work Applies

Director-level roles where execution breaks across teams, AI is being adopted faster than it is being governed, and no single group owns the full outcome.

Director, AI Transformation and Enablement

Adoption governance, Center of Excellence design, review gates and evaluation routines, adoption metrics tied to workflow outcomes and P&L

Chief of Staff to a CEO, COO, or Head of AI

Priorities into operating cadence, decision rights, executive materials, and follow-through across the organization

Director, Strategy and Operations

Operating model design, program governance, zero-to-one execution in fragmented environments

Director, Client and Program Operations

Enterprise partner operating models, executive cadence, escalation systems, implementation through adoption

Director, GTM and Revenue Operations

Pitch coverage governance, sales execution mechanisms, enablement architecture, deal planning cadence

This is ownership of execution systems and outcomes, not advisory support.

Contact

I design operating systems that keep teams executing after I leave the room.

If the work requires executive clarity, cross-functional alignment, and ground-level execution, this is the lane I operate in.

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