Scribe Secures $75 Million in Funding by Showing Where AI, Automation Make Money

Scribe, a San Francisco–based AI startup, has secured $75 million in a Series C funding round, boosting its valuation to $1.3 billion. The funding round was led by StepStone and included participation from previous investors such as Amplify Partners, Redpoint Ventures, Tiger Global, Morado Ventures and New York Life Ventures.

According to a TechCrunch report, the new funding may enable Scribe to speed up the launch of Scribe Optimize. The platform is designed to automatically map workflows across entire organizations and help them identify processes best suited for automation or AI interventions.

In an interview with TechCrunch, Scribe CEO Jennifer Smith said that in many organizations measuring the efficiency of day-to-day workflows is being measured by manual processes.

“Very simply, [Scribe Optimize] mines across workflows for what people are doing when they’re at work, and then it abstracts those up into being able to show you in a single pane of glass, here are the actual workflows that are being done. Here’s how often, how long it takes, etc.,” she said.

Scribe Optimize collects and aggregates data on task frequency, duration, and variations in workflows to provide actionable insights to business leaders. From there, strategic decisions can be made to reduce costly and ineffective automation efforts by focusing on areas with clear potential for efficiency gains.

Scribe’s original product, Scribe Capture, creates step-by-step guides of workflows by capturing user actions within software applications. According to the report, the platform is used by more than 5 million users and Scribe has documented over 10 million workflows across 40,000 software programs.

That detailed, AI-powered workflow data could potentially be applied to cost savings and financial outcomes analysis.

Speaking of operational visibility, a recent PYMNTS report identified three broad areas where AI is generating ROI withing finance functions — operational, strategic and relational.

Specific examples include automating manual tasks like invoice matching and payment dispute resolution, leveraging AI-driven predictive models for working capital management, and using dynamic payment terms and rapid dispute resolution to build stronger trust and loyalty among suppliers and customers.

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