Scribe has raised $75 million in Series C funding at a $1.3 billion valuation to develop and expand “Scribe Optimize,” a tool designed to analyze real work activity across organizations and identify where automation and AI could be most useful. This round looks to support broader deployment of the product and growth of the company’s workforce.

Founded in 2019, Scribe first gained traction with its Capture tool that creates step-by-step visual guides of tasks as users perform them. Its newest offering takes a wider view by mining user actions across departments and systems to identify common workflows, how often they occur, and how much time they consume.

The tool is being released amid ongoing interest from IT and operations leaders in finding more structured approaches to AI adoption.

Why It Matters: Leaders are under pressure to deploy AI tools that actually improve outcomes. Scribe’s growing valuation and product expansion reflect the increasing demand for systems that can show where automation efforts are likely to succeed and where they’re likely to underperform.

• Automation Goals and Operational Reality: One of the main challenges in adopting AI and automation tools is that decisions are often based on assumptions about how work is performed that may not reflect day-to-day practices. By continuously logging workflows, organizations can create a detailed record of how employees interact and move between steps. This data can reveal inefficiencies, bottlenecks, and redundancies that are not captured in formal documentation. With this visibility, AI adoption efforts are more likely to target actual pain points rather than perceived ones, improving the odds of delivering meaningful returns.

• Reducing Uncertainty in AI Project Scoping and ROI Estimation: Many AI initiatives stall or fail because teams struggle to define success metrics or quantify potential gains before implementation. Logging workflow data provides a baseline view of current performance. In doing so, technical leaders build more precise forecasts and identify edge cases that might not be obvious from interviews or surveys. The result is a more informed scoping process that reduces the likelihood of over- or underestimating the impact of automation efforts.

• Improving Coordination Between Technical and Non-Technical Stakeholders: Workflow logging creates a shared factual basis for evaluating which processes are suitable for AI interventions. Without this data, stakeholders may rely on differing understandings of how tasks are completed, leading to misalignment and stalled initiatives. With a clear and objective view of current-state workflows, teams can have more productive discussions. This can reduce friction in cross-functional planning and speed up decision-making cycles.

• Enabling Incremental and Adaptive AI Implementation: Rather than attempting large-scale AI deployments that require significant upfront investment and carry high risk, organizations can use workflow data to adopt a more iterative approach. By identifying high-frequency, low-complexity tasks through logging, teams can pilot smaller automations and evaluate their impact in a controlled setting. These initial rollouts can provide feedback that informs broader strategies, while limiting disruption to existing systems. Continuous workflow monitoring also allows organizations to revisit and revise automation logic as work patterns evolve, making AI systems more responsive over time.

• Reducing Mismatch Between Automation Design and Real User Behavior: A common reason AI tools or bots fail to gain traction is that they do not account for how users actually interact with systems. Automations are often designed with ideal workflows in mind, but real-world usage may involve variations or dependencies that are not captured during planning. Workflow logging can uncover these nuances early, giving teams a more realistic foundation for automation design. This reduces the risk of deploying tools that are technically sound but operationally misaligned, helping ensure that AI implementations are practical and usable in daily work.

Go Deeper -> Scribe hits $1.3B valuation as it moves to show where AI will actually pay off