DocuSign is hiring its way to 7,000 employees as enterprises adopt its Intelligent Agreement Management platform, CEO Allan Thygesen told The Verge this week.
The company launched IAM in April 2024 to escape what it calls the "agreement trap": contracts locked in email threads and file systems that cost organizations an estimated $2 trillion in lost value globally. The platform bundles agreement creation, AI-assisted redlining, e-signature, and analytics into a single system built on DocuSign's no-code Maestro workflow engine.
The pitch is end-to-end automation. DocuSign claims 50% faster contract completion, 80% reduction in drafting time, and five-minute agreement packages that previously took 75 minutes. One enterprise metric: 70% fewer drop-offs during signing.
The AI piece centers on DocuSign Iris, the company's underlying intelligence layer. Thygesen addressed AI hallucinations directly, framing them as an industry-wide risk that IAM mitigates through human oversight loops. The Navigator feature surfaces renewal dates and contract insights from historical agreements, though partners note it still lacks granular extensibility like custom party attributes or MSA-to-SOW linkage that mature CLM systems offer.
Pricing for new IAM customers starts at $100 per user for teams of 1-50, including 100 AI-processed agreements monthly. Enterprise tiers add unlimited processing and deeper integrations across DocuSign's 1,000-plus partner ecosystem.
The growth bet makes sense: DocuSign built its brand on e-signature, but IAM is a broader play into legal operations and procurement workflows where Ironclad and other CLM specialists already compete. The question is whether enterprises will rip out existing systems for DocuSign's vision, or whether IAM becomes another module in a fragmented stack.
History suggests the answer depends on integration quality and whether Navigator's AI actually delivers on contract intelligence. DocuSign includes 5,000 historical agreements in processing allotments, so early adopters can test the platform against their backlog. We'll see if the efficiency claims hold at scale.