Hebbia's Workflow-Driven Vertical AI
Danny Wheller, VP of Business & Strategy at Hebbia, on vertical vs horizontal enterprise AI
This shows Hebbia is selling a workflow transplant, not just AI seats. In finance and law, the hard part is not getting a model to answer a question, it is wiring that model into how analysts and lawyers already work, setting up templates, connecting data sources, and teaching teams when to trust, edit, and reuse the output. By keeping that services layer in house, Hebbia can shorten time to value and make renewal depend on actual workflow adoption.
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Hebbia describes an engagement team made up of ex bankers and lawyers that configures templates, supports onboarding, and helps customers adapt agents to each team’s workflow. That matters because even two groups inside the same bank may review the same document type differently.
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This is becoming the standard playbook for vertical AI. Harvey, the closest analog in law, pairs software with high touch deployment and customer success staffed by former lawyers, with about 10% of its team in those roles to push usage high enough for renewal.
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The Accenture comparison is literal, not rhetorical. Accenture said fiscal 2025 revenue from generative AI and agentic AI reached $2.7B, with $5.9B in bookings, showing how much enterprise spend is going to implementation and change management around AI, not only to model access.
The next step is a tighter bundle where software, templates, and domain implementation blur together. As enterprise AI buyers demand measurable output, vendors that can turn analyst and lawyer workflows into repeatable agent setups will capture more budget and become harder to displace than chat tools that stop at retrieval or drafting.