Key Takeaways
- • Bessemer Venture Partners outlines ten principles for building defensible vertical AI businesses, grouped into functional value, economic value, competitive position, and defensibility, in the fourth and final installment of its Vertical AI Roadmap.
- • Bessemer advises founders to let customer preferences govern which workflows get automated, noting that automatable tasks are not always ones customers want automated, with healthcare payment models cited as a bottleneck to adoption.
- • Defensibility comes from differentiated core capabilities and end-to-end, tightly integrated workflows rather than easily replicated features, which Bessemer warns are becoming table stakes.
- • Bessemer cites Abridge, EvenUp and Rilla as examples of vertical AI companies that generate hard ROI through cost containment, new revenue or work at scales humans cannot match.
- • Bessemer says the technical moat increasingly rests on combining data types and workflow integrations rather than proprietary models alone, and recommends modular systems that can incorporate the best available models.
Summary
Bessemer Venture Partners has published the final part of its Vertical AI Roadmap, presenting ten principles for building strong vertical AI businesses. The framework is organized around functional value, economic value, competitive position and defensibility, and is drawn from the firm's partnerships with and analysis of some of the most valuable and defensible vertical AI companies to date. Bessemer notes that while vertical AI is in a very early stage, emergent leaders such as EvenUp (founded 2019), Subtle Medical (2017), Abridge (2018) and Fieldguide (2020) show that the category has established blueprints for success.
On functional value, Bessemer argues that automation should be pursued only where it matches customer needs and context. Customers may want human review for higher-value decisions, as in a dental procurement example where small orders are automated and larger ones are reviewed. Bessemer points to healthcare, where clinicians have rapidly adopted administrative AI such as scribing, while diagnostic applications have seen slower uptake because payment models have lagged. The firm also advises avoiding easily commoditized features such as data extraction and verification, which it expects to become table stakes. It cites Sixfold, an insurance underwriting AI company, whose solution embeds via API or plug-in into existing policy administration systems, avoiding the need for insurers to overhaul legacy platforms.
On economic value, Bessemer says the most valuable solutions unlock revenue or demonstrate hard ROI, which can shorten sales cycles and improve retention. Abridge is cited for improving physician satisfaction and retention, mitigating recruiting and training costs that can run to millions annually, and for freeing one to two hours per physician per day, which can translate into additional patient visits and better coding and billing. EvenUp, which uses large language models alongside its proprietary PiAi models, generates demand packages for personal injury law firms in a fraction of the time a paralegal would need, allowing firms to take on more cases. Bessemer also notes that AI-enabled services models can open verticals that lacked sufficient total addressable market for traditional software, citing average gross margins of roughly 56% in 2024 for such businesses.
On competitive position and defensibility, Bessemer highlights the value of AI performing work humans cannot do at scale. Rilla, which serves the home services vertical, analyzes high volumes of in-person sales conversations to coach representatives, a level of data review that a sales manager accompanying reps on site could not match. Bessemer identifies sales and marketing, services and legal as categories well suited to AI because they involve large volumes of text and audio. The firm further argues that the technical moat increasingly depends on combining data types and workflow integrations rather than proprietary models alone, recommends modular infrastructure able to adopt the best models as the technology evolves, and stresses that high-quality, relevant data compounds in value as a business scales.
On functional value, Bessemer argues that automation should be pursued only where it matches customer needs and context. Customers may want human review for higher-value decisions, as in a dental procurement example where small orders are automated and larger ones are reviewed. Bessemer points to healthcare, where clinicians have rapidly adopted administrative AI such as scribing, while diagnostic applications have seen slower uptake because payment models have lagged. The firm also advises avoiding easily commoditized features such as data extraction and verification, which it expects to become table stakes. It cites Sixfold, an insurance underwriting AI company, whose solution embeds via API or plug-in into existing policy administration systems, avoiding the need for insurers to overhaul legacy platforms.
On economic value, Bessemer says the most valuable solutions unlock revenue or demonstrate hard ROI, which can shorten sales cycles and improve retention. Abridge is cited for improving physician satisfaction and retention, mitigating recruiting and training costs that can run to millions annually, and for freeing one to two hours per physician per day, which can translate into additional patient visits and better coding and billing. EvenUp, which uses large language models alongside its proprietary PiAi models, generates demand packages for personal injury law firms in a fraction of the time a paralegal would need, allowing firms to take on more cases. Bessemer also notes that AI-enabled services models can open verticals that lacked sufficient total addressable market for traditional software, citing average gross margins of roughly 56% in 2024 for such businesses.
On competitive position and defensibility, Bessemer highlights the value of AI performing work humans cannot do at scale. Rilla, which serves the home services vertical, analyzes high volumes of in-person sales conversations to coach representatives, a level of data review that a sales manager accompanying reps on site could not match. Bessemer identifies sales and marketing, services and legal as categories well suited to AI because they involve large volumes of text and audio. The firm further argues that the technical moat increasingly depends on combining data types and workflow integrations rather than proprietary models alone, recommends modular infrastructure able to adopt the best models as the technology evolves, and stresses that high-quality, relevant data compounds in value as a business scales.
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