AI Consulting & Education

You know AI matters.
You don’t know what to do with it.

I help businesses, founders, and growing teams figure out where AI actually pays off. Then build the part that does. 21 patents. 25 years. $2B+ in shipped product revenue.

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21
Patents Across Medical Devices,
Product Systems & AI
20+
Products Shipped
$2B+ in Revenue
2000+
Companies Reviewed
Across VC Work
The Honest Take

AI isn’t hard to use.
It’s hard to start.

Founders, firms, and growing companies all land in the same place: curious about AI, a little skeptical, and unsure what to actually build first. That’s not a failure. That’s the starting point.

01
They buy before they understand

Tools pile up. Subscriptions go unused. Nothing connects to how you actually work. The problem was never the software.

02
They think it’s too complicated for their size

The opposite is true. Smaller teams move faster, adopt cleaner, and see results sooner. Scale is not a prerequisite.

03
They paid for AI tools nobody uses

The team signed up. Nobody adopted it. Subscriptions renew. People go back to copy and paste. The problem wasn’t the tool. Nobody figured out which tasks AI actually fit, before signing up.

04
They build the wrong product first

A founder hears “use AI to build it.” Nobody explains which AI, for what, or in what order. Six months disappear. The product ships. Nobody wants it. The problem wasn’t the build. It was the product.

What We Do

Four ways to work together.

Each starts the same way. Understand where you actually are. Figure out where AI would help, and where it’s a waste. Do only the part that matters.

01 — For Your Business

Practical AI for Small & Growing Businesses

You know AI should be helping your business. It’s not. The work: find the two or three places in your operation where AI would actually pay off. Build it. Make sure your team uses it. By the end of an engagement, real AI is doing real work inside your business.

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02 — For Founders

Product Strategy & MVP for Founders

You have an idea. You’re not technical. You don’t know what to build first, what’s worth building at all, or how to use AI to get there cheaper and faster. Most non-technical founders waste months on the wrong product. The work starts by figuring out the right one.

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03 — For Growth-Stage Companies

Fractional Chief Product & AI Officer

Senior strategic judgment without the full-time hire. For companies making big AI and product decisions. What to build. What to skip. What to do first. Drawing on 25 years of product leadership, 21 patents, and 20+ products shipped.

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04 — For Teams

Team Training & AI Literacy

Practical sessions on what AI can and can’t do, which tools match which tasks, and how to build habits that stick. For teams that are skeptical, busy, and tired of buzzwords.

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Featured Work

Three engagements.
Same decision pattern, very different stakes.

Deciding what to build, when to build it, and what to leave on the table. The featured cases sit across robotic surgery, autonomous vehicles, and venture capital. Different industries, different decades, same kind of call.

Intuitive Surgical
2012–2015
Internal Product Strategy · ION product line

The decision that determined which patient came first.

The ION platform was being built toward two possible customers. Not two variations of the same customer—two fundamentally different users with different workflows, different clinical paths, and different economics.

Choosing wrong meant designing for the wrong person. It meant clinical trials pointed in the wrong direction. It meant the platform reached market late, or not at all.

I led the work to resolve it. The answer was clear. The team moved.

Outcome
ION reached market and became a significant revenue driver for Intuitive Surgical—one of the world’s most successful medical robotics companies.
The pattern repeats at any scale. Pick the customer who matters most. Build vertically into that need. Prove it works. Then expand. Throwing darts at multiple markets and hoping one lands is not strategy.
Drive.ai
2017–2019
Head of Product · Autonomous vehicles

Building a real business out of unproven AI.

Self-driving was state-of-the-art and largely unproven. Many companies chased every feature simultaneously. Many ran out of money—not because the technology failed, but because they ran like university labs instead of businesses.

I pushed scope reduction. Intentionally delayed or removed the technically difficult work to accelerate what mattered. Reduced risk where I could. Built toward a sustainable business, not a demo.

Two patents shipped. One later adopted by Waymo’s vehicle systems.

Outcome
~10x development velocity. The business discipline that came from treating this as a company—not a lab—directly contributed to the Series C raise and the eventual acquisition by Apple.
I’ve been making AI product decisions since the technology was new. Ten years on, the work has the same shape. Decide what AI is ready to ship now. Skip what isn’t. Build the part that pays off.
Mighty Capital
2024–26
AI Operations Architect · Bay Area venture capital

Making a small VC firm operate like a much larger one.

Mighty Capital is a small Bay Area VC firm competing for the same deals as firms with several times the headcount. The mandate: build the operational leverage to match without adding people.

I architected and built an AI-first operating system across the firm. Sourcing scaled from 50 outreach touches per day to 500. Bespoke briefing systems convinced in-demand founders to sign with the firm over much larger competitors. 50+ agents and assistants gave partners hours back per day, and turned diligence and board prep from scramble into confidence.

The team didn’t grow. The capacity did.

Outcome
A small firm operating at the scale of much larger ones. 50+ production AI workflows running daily across sourcing, diligence, and portfolio support.
Same approach for any small business or growing team. AI lets a small team operate like a much larger one. The work is deciding what to automate, what to leave human, and how to make adoption stick so the system actually runs after the consultant leaves.
“The deliverable was rarely the product. The deliverable was the decision. What to build. What to leave on the table. What to do first. The rest follows from that.”
Michael Paris — Qnnovation
Michael Paris
About Michael

25 years.
21 patents. 20+ products shipped.
$2B+ in revenue.

Most AI consultants haven’t shipped a product. I’ve shipped over twenty, totaling more than $2B in revenue across companies. I hold 21 patents across medical devices, product systems, and AI. That’s twenty-five years figuring out what survives contact with reality, and what looks good in a pitch deck but dies in production.

That changes the work. I’m not designing a demo. I’m designing what your team will actually use, or what your investors will actually back. The behavioral-economics lens matters here. What people will adopt. What they’ll ignore. What looks good in a meeting and dies in production.

As a Partner at a VC firm, I reviewed over 2,000 companies and learned what separates products that win from products that don’t. I also built one of the first AI-first VC firms. Small team. Big-firm performance. Deals won.

AI consultants build. AI strategists decide. I do both. Same pattern across roles and twenty-five years. Chief Product & AI Officer. Head of Product at a startup acquired by Apple. Partner at a VC firm. Now Fractional Chief Product & AI Officer.

21
Patents in medical devices,
product systems & AI
50+
AI workflows
built & deployed
michael@qnnovation.com LinkedIn
From People I’ve Worked With

The vouches.

A few recent recommendations from people I’ve built, shipped, and figured things out with. More on LinkedIn.

“
Michael has the wisdom to know that saying “no” to one-off feature requests can be just as important as saying “yes” to the initiatives conveyed in the roadmap.
Elizabeth Bacon
Founder & Principal — Devise Consulting
“
A creative powerhouse meets execution excellence. Michael is a force to be reckoned with.
Fabien Blanc-Paques
Product Lead, Gemini — Google
“
I can attest to the thoughtfulness Michael puts into everything he does and especially have been appreciative of his strategic thinking.
Victoria Gambardella
Director of Research, Generative AI — Meta
“
His approach and the way his mind works is highly creative. He gets things done and is so talented.
Carol Reiley
Co-founder & CEO — Drive.ai
See all recommendations on LinkedIn →
Selected Engagements

Where this work has happened.

Fifteen years across medical devices, autonomous vehicles, mental health AI, venture capital, sleep technology, PropTech, and legal services. The featured case studies above are examples, not the full list.

Get Started

Not sure where to begin?
That’s the right place to start.

Fifteen minutes is enough to see what’s possible and where to focus. No deck. No pitch. Just a real conversation. Pick a time that works.

Book 15 Minutes See the Work First