I design the machine
that creates the product.

I’m Michael Storey, a product design and development leadership executive helping organizations transition from reactive feature factories into AI-led, strategically driven product design organizations.

Stop designing screens. Start designing the systems, careers, and operating models that drive real business value. For 25 years, I’ve helped product design, and development organizations shed complexity at the moment constant shipping hides unclear outcomes and fragile trust.

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By the numbers

25+ years

100M+ users

50% growth

30% drop

in revenue YoY

in support volume from automation

impacted across platforms

in design, product, and org leadership

200+ team lead

130% lift

globally across multiple time zones

in feature adoption

The Core Narrative

From Chaos to Clarity

I am a builder. Whether I'm structuring a 200-person organization at Amazon, leading product strategy for a fast-growing PropTech company, or shaping wood in my Seattle garage, the impulse remains the same: create frameworks that eliminate risk and ensure certainty.

My career has taken me from design studios in Bath and London to the global scale of Amazon, and now to the strategic challenges of AI-powered product development at RentSpree. Along the way, I've learned that the most valuable design work happens long before anyone opens Figma. It happens when you redesign how decisions get made, how teams are structured, and how organizations define success.

I specialize in the messy middle of growth, that inflection point where what got you here won't get you there. It's the moment when a startup's scrappy, founder-led decision-making collides with the need for repeatable processes. When a successful product organization realizes that adding more people isn't making them move faster. When an enterprise division discovers that best practices from five years ago have become anchors holding them back.

This is where I do my best work. I don't just fix your UI. I fix the decision-making engines behind it.

The Problem

The Build Trap

Many companies get stuck in the Build Trap, and it's insidious because it feels like progress. Teams are talented. Backlogs are full. Shipping is constant. Stakeholders are getting what they asked for. But underneath, the foundation is cracking.

The symptoms are predictable. Design leaders struggle to secure a strategic voice. User research happens after decisions are made, if it happens at all. The design system exists but sits unused or inconsistently applied. Product managers and designers engage in territorial battles over customer ownership. Engineers grow frustrated as requirements shift without clear rationale. Leadership questions why design takes so long and costs so much, while designers feel undervalued and misunderstood.

I've seen this pattern at venture-backed startups racing toward their next funding round, at mid-stage companies confronting their first real scaling challenges, and at Fortune 100 enterprises trying to inject innovation into processes that have calcified over decades. The contexts differ, but the underlying disease remains constant: organizations optimized for output when they desperately need to optimize for outcomes.

The Build Trap isn't a failure of talent. It's a failure of systems. You can't hire your way out of it. You can't sprint your way past it. You need to pause just long enough to build the operating model that will carry you forward.

The Fix

Redesign the Decision-Making Engine

My approach combines the operational rigor I learned over a decade at Amazon with the innovation velocity required in startup environments. I learned that scale does not have to mean slow. With the right mechanisms, discipline accelerates decisions instead of blocking them.

The goal is not to copy Amazon. Most companies cannot and should not. The real leverage comes from understanding the principles beneath the practices and adapting them to your context, culture, and stage of growth.

In startups, this approach has delivered results. We drove roughly 50% year-over-year revenue growth in AI-powered features, reduced support volume by 30%, increased conversion from 19% to 47%, and cut customer tasks from 45 minutes to about 5, resulting in 130% uplift.

This did not come from hiring more, working harder, or pushing people to take on more. It came from redesigning how decisions get made and elevating design to a true strategic partner.

My Three Pillars of Transformation

Over 25 years and dozens of organizational transformations, I've developed a framework built on three interdependent pillars. Each addresses a critical dimension of scaling product organizations, and together they create the conditions for sustainable, outcome-driven growth.

Most org charts are political documents pretending to be strategic. I approach organizational design like architecture. Form follows function, and every element must serve a purpose.

01. Organizational Architecture

Before redrawing a chart, I ask one question: What work must actually get done? Not titles, not LinkedIn descriptions, but the real jobs that drive product success. This often reveals opportunities traditional structures miss. Maybe you don’t need separate VPs for design, research, and content strategy. Maybe you need a VP of Experience. Maybe discovery and delivery teams should merge into cross-functional pods where product, design, and engineering make trade-offs together.

At Amazon, I built and led organizations of up to 200 people across design, research, content, product, and engineering. Structure is decision rights, communication paths, accountability, and career growth. Get it right, and talent thrives. Get it wrong, and even exceptional people struggle.

The organizational architecture work I do includes:

How do you define an L4 designer versus an L6? What does a Staff Designer do that a Senior Designer doesn't? These questions seem administrative until you realize they determine who gets hired, how they're evaluated, what they're paid, and whether they stay. I've built career frameworks spanning L4 through L8 that provide clarity while leaving room for different types of excellence. An IC (individual contributor) track that recognizes senior designers shouldn't be forced into management to advance. Clear expectations that reduce bias in performance reviews and promotion decisions.
The trio model, product manager plus designer plus engineer working as a unit, is powerful but surprisingly hard to implement. It requires rethinking how work gets assigned, how decisions get made, and how success gets measured. I help organizations move from functional silos to genuinely collaborative pods without losing the benefits of discipline-specific craft development.
Who you hire should reflect where you are and where you're going. Early-stage companies often need versatile generalists who can shift between UI design, user research, and content strategy. Later-stage companies need specialists who can drive depth in areas like accessibility, design systems, or research operations. I help leaders make these distinctions clearly and hire intentionally rather than reactively.
As design teams grow, complexity grows faster. You need systems to manage tools, govern design, coordinate research, plan capacity, and maintain quality. I build DesignOps functions that let designers focus on design, not process.
At RentSpree, I reorganized a feature team into a product-led organization by unifying design, research, and content. I created clear career ladders and implemented cross-functional trios, changing how decisions get made. The result was not just happier designers but measurable business impact.

02. AI Product Strategy

Most companies treat AI like mobile in 2010: a feature to support existing products rather than a tool to rethink what’s possible. They add chatbots or use language models to summarize content. These shallow applications offer convenience but don’t transform workflows.

I help organizations move beyond shallow AI to Agentic AI. This is AI that acts on behalf of users, orchestrating complex workflows that once required human intervention. A chatbot that answers rental questions is shallow AI. An agent that gathers documents, verifies them, completes applications, submits them, and tracks approval is agentic AI. One eases a task. The other removes it entirely.

At RentSpree, I led the Rental Maestro AI agent, turning multi-step tenant screening into a seamless, orchestrated process. This improved user experience and drove roughly 50 percent year-over-year revenue growth by removing friction from the purchase decision.

Agentic AI also raises stakes. A chatbot giving wrong answers is annoying. An agent submitting a flawed application causes real harm. Designing for this requires a fundamentally different approach.

I developed a framework for mapping AI opportunities across two dimensions: breadth and depth. Breadth measures how much of a workflow AI can cover. Depth measures whether AI informs, guides, or acts. Different capabilities have different profiles, and understanding the trade-offs is key to strategy.
Low breadth, high depth could be AI handling payment processing end-to-end for a narrow case. High breadth, low depth could be a copilot assisting many tasks but requiring human confirmation. The strategic question is always the same: where should we invest first?
When AI acts on its own, users must understand what it does and why. Too much detail creates cognitive overload. I design contextual transparency that shows the right information at the right time, giving users control without drowning them in unnecessary complexity.
AI trained on biased data produces biased outcomes. Systems that work for native English speakers may fail for users with accents or limited English. At RentSpree, serving landlords, property managers, and renters from diverse backgrounds, we couldn’t build AI for a narrow demographic. I created frameworks to identify bias early, test with diverse users, and embed inclusivity into every stage of AI product development.
Traditional software fails predictably. AI creates new risks: hallucinations, misinterpreted intent, and gaps in training data. Handling these failures requires a different approach. I help teams build AI products that fail gracefully, maintain user trust, and learn from mistakes to improve continuously.
Before building AI, I use Wizard of Oz prototyping, letting humans perform tasks the AI will automate. This validates demand, exposes edge cases, and refines interactions before investing in models. I learned this early in my career and adapted it for the AI era.

03. Operational Excellence

Amazon taught me that great outcomes at scale don’t come from working harder or hiring more talent. They come from mechanisms, recurring processes that enforce quality and align decisions as organizations grow.

Most companies fail because their processes don’t scale. What worked when founders attended every meeting fails as teams expand. What worked in shared spaces fails across time zones. What worked when everyone knew each other fails when new people arrive faster than relationships form.

You need mechanisms that work without heroics, processes that ensure quality without VP review, rituals that keep teams aligned without endless meetings, and standards that maintain consistency without stifling creativity.

At Amazon, we started with the customer and worked backwards. We wrote press releases for products before building them and six-page narratives instead of slide decks to force clear thinking about complex problems.
I’ve adapted this approach beyond Amazon. At RentSpree, product proposals define the problem, user value, and expected business outcomes before any design work begins. This shifts discussions from subjective debates to objective evaluation against clear goals.
Regular, structured design reviews ensure quality, provide learning for junior designers, gather cross-functional input, and surface risks early.
Poorly run reviews become rubber-stamp meetings that waste time. I help teams set up design review rituals with clear purposes, the right stakeholders for each stage, and evaluation criteria focused on outcomes instead of opinions.
Amazon’s bar raiser program ensures every hire meets high standards and raises the team’s quality over time. Trained interviewers can veto hires, preventing standards from slipping under pressure.
I’ve adapted bar raiser programs for design organizations outside Amazon, tailored to culture and hiring volume. The principle is the same: protect quality even when the pressure to fill seats is high.
You can’t improve what you don’t measure, yet most design teams track the wrong metrics. Adoption rates and test completions don’t show impact.
I help teams focus on metrics that matter: task success, errors, time to completion, customer satisfaction, and the link between UX and business outcomes like conversion, retention, and revenue. At RentSpree, embedding these metrics into business reviews elevated design from a cost center into a strategic investment with measurable returns.
Design systems promise consistency and efficiency but deliver only when actively maintained. Components must evolve with user needs, and adoption must be encouraged, not forced.
I create governance models that balance central standards with team autonomy, update systems based on real usage, and make the system easier to use than bypass.

Recognition & Organizations

I design the machine that creates the product.