94% of Fortune 500 Use This Tool – 44% Actually Pay for It

01 /The Concept

Scribe reached $75 million in new funding last November at a $1.3 billion valuation – and became the newest unicorn. The growth curve tells the story: the platform is now used by more than 600,000 companies, including 94% of Fortune 500 companies. Of those, 44% pay for it. Counting individual users, Scribe reaches 5 million people.

The core product that drove this growth is Scribe Capture. The Capture AI agent sits on employees' computers and tracks everything they do on them. The goal: for any workflow, instantly generate visual step-by-step documentation showing exactly how it should be performed – which button to click, what to select and fill in, where to click next, and so on.

Those recordings serve two main purposes. For internal workflows and enterprise software, they create process documentation, help train employees on new software features, and onboard new hires – and internal developers can study how employees actually use the software to improve it. For companies that also sell software products, the recordings can go directly to end users as step-by-step troubleshooting guides in response to support tickets.

A limited version of Scribe is free. The full individual plan – suitable for a consultant with a small team, for example – runs $23 per user per month. Team and company plans for three or more people start at $12 per user per month.

Despite the large number of companies already using Scribe Capture, this is still just the setup.

02 /Why It Matters

The central question driving Scribe's $75 million fundraise – the one that turned it into a unicorn – is: "How do you actually implement AI into business processes?" Every company needs an answer to that question right now.

Scribe's answer came in the form of a new product launched in November: Scribe Optimize. The Optimize AI engine doesn't just document workflows – it now actively proposes ways to make those documented workflows better.

Scribe's founder uses a sharp analogy: self-driving taxis only hit the streets of San Francisco after Uber and other rideshare services had been operating there for years – long enough for their apps to have mapped every inch of every street, producing a detailed digital model of the city. Only after that could you hand control of the vehicles to AI.

The parallel: before you can implement AI in a company, you first need to "map" all the existing business processes. Then you can start gradually handing them over to AI. Most companies do the opposite – they rush to implement AI while relying only on their own assumptions about what's working and what isn't.

This, according to a recent study, is one of the main reasons 95% of enterprise AI pilots have failed.

With the process map built by Capture, Scribe Optimize makes it explicit which parts of your workflows are currently most inefficient, proposes ways to improve them drawing on a library of accumulated business cases, and provides concrete next steps for optimizing the processes you choose to tackle. The result is a platform covering the full cycle – from mapping and documenting processes, through optimization and automation, to continuous monitoring and improvement as new workflows go live.

All of this became possible only because Scribe Capture already existed – and through it, the startup had accumulated a unique dataset from 600,000 companies with 10 million captured workflows. Without that foundation, Scribe Optimize couldn't deliver meaningful optimization at the necessary level of quality and detail.

Strangely, Scribe's approach is reminiscent of a recent startup called Traini ([related review](/review/ne-otkazhutsja-ot-takogo-prilozhenija)) – which built PetGPT, an app for decoding dog behavior.

The key similarity: Traini's app has a built-in "learning in use" mechanism, where the AI continues training on feedback from users who report whether the app correctly read their dog's emotional state.

This mechanism is how Traini builds a moat against competition. The more users contribute feedback to train the AI, the better the app gets. That creates a genuine network effect: the more users, the more valuable the platform.

Scribe Optimize works the same way: its value isn't a function of AI algorithm quality alone. It's a function of the dataset Scribe Capture has amassed. The more workflows across more companies Capture tracks, the more valuable Optimize – and the platform as a whole – becomes.

03 /Opportunities

The arrival of AI will force every company to rethink its business processes in order to improve efficiency and stay ahead of competitors who are doing the same.

The main trend is therefore AI-driven process transformation across the enterprise. The key opportunity: building AI platforms for business process automation.

Beyond automation, those platforms also need to be able to tell companies how to integrate AI into their workflows in the first place. And that's not a one-time consultation – it's an ongoing conversation, as companies continuously discover new opportunities to improve. Which is excellent news for platform builders: clients never leave.

A meaningful number of startups are already moving in this direction from different angles. A few examples besides Scribe: Quantum Rise ([related review](/review/na-jetom-uzhe-ne-stydno-zarabatyvat)) raised $15 million; Gruve raised $37.5 million; Workhelix raised $30.3 million.

Builders of any AI product need to understand that their most important competitive advantage is not a "perfect" AI that works flawlessly from day one. Because if you can build a perfect AI product – so can your competitors. The real play is to choose a domain where that perfect algorithm is fundamentally impossible, but where you can keep getting closer to it over time by continuously accumulating more data to improve your models. At that point, the data itself becomes the moat – something competitors cannot quickly replicate. The startup that starts first and grows fastest in users who generate training feedback becomes progressively harder to catch.

That principle applies directly here: which AI application in your space has that same property – where more users make the product better, and better products attract more users, in a loop that compounds indefinitely?