Jennifer Smith got her start in Silicon Valley at venture capital firm Greylock Partners. Over the course of three years, she interviewed 1,200 C-suite executives, asking them about the problems they were trying to solve, what kept them up at night and what they wished Silicon Valley was building and investing in. It was part of her job.

From those conversations, Smith uncovered the problem that her startup, Scribe, is trying to solve now. Scribe captures the workflows that experts know how to do and then uses that information to help improve the way everyone works. 

Scribe watches experts at work and then makes that information available to others who need to carry out the same process, like a new employee at a company. When someone completes a workflow, Scribe’s technology will create a step-by-step guide of the process, including text and screenshots, that can be shared with the team. 

“We will build a data set for the workflows that are done at a particular company and then use that to help identify places they could be better,” Smith tells Entrepreneur in a new interview. 

Scribe has documented more than 10 million workflows so far. 

Scribe raised $75 million at a $1.3 billion valuation in November, making it one of the latest unicorns, or companies that hit a $1 billion valuation. More than 700,000 companies use Scribe, including New York Life, T-Mobile, LinkedIn, HubSpot and Northern Trust. About 98% of the Fortune 500 use the startup’s technology, according to Smith.

Smith’s origin story

Smith grew up in upstate New York near the Canadian border, far from Silicon Valley and without any childhood vision of becoming a startup founder. As an undergraduate at Princeton University, she had no idea what she wanted to do, so she chose McKinsey consulting largely because the people who recruited her seemed curious about the world. She saw McKinsey as a place to learn by working with people who asked big questions and tackled complex problems.

At McKinsey, Smith learned how to break down what she calls “hard, gnarly, ambiguous problems” into solvable parts, a muscle that would later define how she built Scribe. Companies routinely dropped consultants into unfamiliar industries and asked them to solve issues even the CEO could not crack, forcing her to become what she later called a “rapid learning machine.”

More importantly, McKinsey exposed her to the inefficiencies of knowledge work that would become her lifelong obsession. She spent time in large operations centers, tasked with making them more efficient, and habitually shadowed the best performers to understand why they excelled. Those top performers would pull out binders of laminated step‑by‑step guides and then admit they ignored them, having discovered better ways to do the work on their own.

“We would basically pull up a chair and look behind their shoulder and write down the better ways of working for them,” Smith says. 

The challenge of manually capturing and sharing better workflows struck Smith as especially time-consuming. She felt it was “crazy” that organizations still depended on consultants or internal staff watching people work and writing everything down. 

Following six years at McKinsey, Smith eventually moved to Silicon Valley and gravitated toward venture capital, which felt like a safe way to get close to startups while still inside a familiar company structure. Over several years, she systematically interviewed CIOs and CTOs at large companies, asking what problems they faced and what they wished Silicon Valley would build. Those conversations reinforced what she had seen as a consultant — she saw organizations struggle to understand how work got done, and fail to scale their efforts to capture and improve workflows.

When Smith looked back, she saw these interviews as a key dot that, in retrospect, connected directly to Scribe’s founding. “I was always following where I found curious people solving hard, interesting problems,” Smith says. 

Making the idea a reality

After three years in venture capital, Smith quit her job and asked herself, “What do I want?” instead of “What should I do?” She concluded that she wanted to build something that mattered, that could endure beyond her own presence. The problem that obsessed her most was the waste in knowledge work — the fact that organizations lack an efficient way to capture and scale best practices. 

Smith founded Scribe in 2019 as a solution to that problem. She made a personal five-figure investment in the company to get it off the ground. Scribe’s core idea was simple: watch experts do what they know how to do, automatically capture their workflows, and then make that know‑how available to anyone who needs to do the same task, at the moment and in the context they are working. This would build a dataset of real workflows across a company, enabling both immediate knowledge sharing and, later, AI‑driven optimization.

“You’ve got to fall in love with the problem,” Smith says of the advice she would give to founders. “You are going to think about this day in and day out, and you’re going to put some of the best years of your life against it. The opportunity cost of that is very real.”

Scribe launched a free, end-user version of its product in 2020 with a simple goal: learn whether people truly cared about the problem and whether the product resonated. The team did not initially focus on monetization. Users began sending long, detailed emails asking them to make the product better. “We started getting really detailed feedback where people would send me long essays every day,” Smith says. 

Even more telling, users soon started asking if they could pay because they depended on Scribe for critical workflows and were nervous about relying on a free tool. In response, Scribe posted a Stripe link and launched a paid product. Smith decided to “bet the company” on a product-led model because of the passion and feedback they were seeing. 

Over time, Scribe’s free and paid products spread widely, leading to over five million users, according to Smith. Scribe transformed its product into an AI agent that captures workflows and analyzes how to improve them.

In a landscape where Smith says many enterprises are “lighting their money on fire” with “half-baked” AI projects with no return-on-investment, Scribe’s value rests on making invisible work visible so AI can actually improve it.

“AI is not magic at the end of the day,” Smith explains. “It’s only as good as the information you feed it.”

Smith’s biggest challenge right now is recruiting. She spends over 60% of her time on it, explaining that headcount growth has significantly underpaced overall business growth at Scribe. “It’s just the challenge around finding bar-raising talent and being able to do that fast enough,” Smith says.

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