Automation, for all its potential, still triggers a hard question across enterprise IT desks: Where should we begin? In a landscape overrun by generic AI tools and process consultants, Scribe is striking a chord with its latest product — Scribe Optimize. With a fresh $75 million raised in a Series C round led by StepStone Group, the company has reached a $1.3 billion valuation and is now focused on helping organizations pinpoint precisely where automation delivers real value.

Founded in 2019, Scribe first earned recognition with its browser-based documentation tool, Scribe Capture, which auto-generates user-friendly process guides in real time. The tool remains popular among teams aiming to lower onboarding costs and reduce repetitive troubleshooting. But the broader vision is taking a giant leap with Scribe Optimize, a new platform designed to mine and map out enterprise processes in granular detail, then highlight which workflows are worth automating.

CEO Jennifer Smith describes the platform as a “single pane of glass” that visualizes how work gets done — revealing frequency, execution time, and impact — based on data, not assumptions. And the timing couldn’t be better. Many companies, having rushed to implement AI, still lack clarity on how to deploy it effectively. By surfacing actual work patterns, Scribe gives these organizations a factual roadmap rather than relying on consultants, interviews, or guesstimates.

The inefficiency of discovery remains a blind spot in digital transformation efforts. “People are still manually timing workflows with stopwatches,” Smith told TechCrunch. “Even now, deploying AI agents remains a manual, tedious process.” Scribe Optimize flips this reality, leveraging the company’s core strength: translating user behavior into transparent, operational knowledge.

To date, Scribe has documented over 10 million workflows across 40,000+ SaaS applications, growing its user base to over 5 million. It counts teams at T-Mobile, New York Life, LinkedIn, HubSpot, and Northern Trust among its 78,000 paid customers, touching operations inside 94% of Fortune 500 companies. These are not peripheral use cases. They reveal an undeniable hunger for scalable, repeatable insight into how digital work actually happens.

By comparison, competitors like Tango, Iorad, UserGuiding, and Spekit often focus on UX-level guides or isolated support content. Scribe’s approach is more architectural. It doesn’t just record processes — it contextualizes them at scale. For enterprise leaders who need strategic clarity on process optimization, that distinction is critical.

The appeal goes beyond just IT departments. According to Smith, Scribe is often discovered and embraced bottom-up. “People come to Scribe because they want to capture and share how things are done, not because it’s mandated from the top. Adoption spreads from users to team leads to central ops functions.”

As automation and AI continue to mature, tools like Scribe become the connective tissue between intention and execution. It’s no longer enough to buy AI software — businesses need intelligence about themselves first. That’s where Scribe is planting its flag.

Moving forward, the San Francisco-based startup plans to significantly scale its headcount (currently at 120), expand globally across the UK, Canada, Australia, and Europe, and double down on optimizing cross-functional workflows through data. As Smith puts it, “To improve work, we must first understand it — with evidence, not guesswork.”

For enterprise SaaS providers, product leaders, and digital transformation officers alike, this is no longer just a nice-to-have — it’s rapidly becoming foundational. And platforms like Scribe are building the lens through which modern organizations can finally answer, ‘Where does AI actually pay off?’

Scribe’s evolution from a documentation tool to an enterprise intelligence platform reflects a broader shift we see across digital transformation strategies: organizations must understand before they can automate. At DevSparks, we often advise clients to start with high-fidelity workflow visibility before executing large-scale AI or RPA initiatives. Without that clarity, automation becomes guesswork and ROI suffers.