Reveal Growth Consultants, Inc.

04/23/2026

For decades, roughly 40% to 45% of startup failures have been tied to poor product-market fit. The surprising part is that this stayed true while Lean Startup, Design Thinking, Customer Development, and Agile became mainstream.

These processes are excellent at reducing solution risk (the risk of not being able to make or deliver what customers need), but they were never built to reduce market risk (the risk of misunderstanding customer needs so they don't buy what you make).

AI makes the need to address market risk before you build even more urgent. It can generate code, prototypes, and experiments at machine speed. But speed does not equal insight. We need a compass before we accelerate.

If you do not know what is worth building, you just reach the wrong answer faster, and waste more resources.

I explored this in my latest newsletter article by reviewing The Enterprise AI Playbook: Lessons from 51 Successful Deployments by the Stanford Digital Economy Lab:

What Stanford Found Stanford's Digital Economy Lab spent five months interviewing executives at 41 organizations to document 51 enterprise AI deployments that delivered measurable value. The Enterprise AI Playbook, published in April 2026 by Pereira, Graylin, and Brynjolfsson, reports that 77% of th

03/31/2026

Innovation failure rates have not improved in 30 years despite widespread adoption of Lean Startup, Design Thinking, Customer Development, the Business Model Canvas, and Effectuation.

Why? Because there's one key part of the front-end of innovation that none of these methods were designed to address.

This article is my response to Jerry Neumann's recent essay 'We Have Learned Nothing' in Colossus.

Link it the first comment.

03/19/2026

Airbnb nearly went bankrupt iterating on the wrong problem.

The founders spent months tweaking growth hacks and site design. When they finally talked to hosts and guests, they discovered the real job was not 'find a place to crash.' It was 'feel safe booking a stranger's home.'

The iteration loop eventually worked. But the conversation that changed everything could have happened first.

Most innovation processes are disciplined and well-executed. They just start in the wrong place.

Last week, I explored why the starting point matters, and what changes when teams discover what customers need before they decide what to build:

Most innovation efforts do not fail because of poor ex*****on. They fail because the process begins after the most important decision has already been made.

03/05/2026

Your AI knows everything except what's actually worth building.

Most teams have excellent methods for refining solutions. Very few have a systematic discipline for discovering which problems are worth solving first. This piece explores why that gap exists, what AI can and cannot contribute, and why iteration is the right tool for solutions but the wrong tool for discovery.

The one thing is knowing what's worth building. AI can process data, generate insights, and produce recommendations faster than any team.

02/12/2026

Have you ever had strong strategy discussions with your team only to watch momentum stall because taking action seemed too risky?

Most leadership teams can recognize this moment. A strategy offsite ends with a long list of initiatives everyone agrees are “promising.

01/27/2026

After my own repeated failures early in my career, one lesson became unmistakable: innovation usually fails before ideation, when teams skip discovering what customers truly struggle with.

This article explains what changed once that became clear.

Memory expert Jim Kwik says: "There's no such thing as a good or bad memory; just a trained or untrained memory." The same applies to innovation: There's no such thing as creative or uncreative teams; just trained or untrained teams.

12/30/2025

Feeling like you're on a treadmill, shipping features faster but never getting ahead? You're not alone. Here's why AI product-market fit feels so fragile (and what to do about it):

Elena Verna, Growth Leader at Lovable, an AI-native company that has reportedly reached $200M ARR, recently described what she calls a “product-market fit treadmill.” Her point is stark.

12/09/2025

The phrase "what customers want" has two completely different meanings. When we confuse them, innovation fails.

When we distinguish them, breakthrough innovation becomes systematic. I clarify this distinction in this edition of "Consistently Win at Innovation."

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"If I'd asked people what they want, they would have told me a faster horse." - Henry Ford This quote is almost certainly apocryphal, but it persists because it reflects a challenge many practitioners feel: getting reliable customer is difficult.

12/02/2025

Most companies have more customer data than at any point in history. So why are their innovation bets still so unreliable?

Innovation Myopia is a widely observed pattern where teams rely on operational knowledge instead of studying the job customers are trying to get done. When leaders fix this, innovation becomes far more predictable.

My latest article breaks down this pattern and shows how companies can consistently win at innovation with Jobs to Be Done.



Read it here:

Last time, I wrote about why companies that have more customer information than ever still make poor innovation bets. Today, I want to show you the pattern that causes this, a pattern so common it deserves its own name: Innovation Myopia.

11/12/2025

Why successful companies make bad innovation bets
The problem isn’t competence—it’s using the wrong customer data to choose what’s next.
Short, plain-English guide + example:
https://revealgrowth.com/why-successful-companies-make-bad-innovation-bets/

Here’s something I see repeatedly: companies that excel at operations still make poor innovation bets. It’s not a competence problem. It’s a data problem. They have delivery knowledge in abundance. What they lack is jobs-and-outcomes knowledge. And that gap is where market risk lives. The Blin...

10/21/2025

American music producer Rick Rubin says the best art comes when you create what you love, when you follow your own truth without worrying about the audience.
That kind of conviction works beautifully in art.

But in product and service innovation, conviction must be learned, not assumed. Here, the customer—not the creator—is the source of truth.

Recognizing that difference helps innovators focus their creativity where it truly matters.

“Create from conviction,” say the artists. “Follow what feels true.