Business model innovation in the AI era

The emergence of artificial intelligence is creating new business models that are transforming how AI capabilities are delivered to various industry sectors, particularly in areas like legal, healthcare, and professional services.

Market context and opportunity: The integration of AI into vertical industries represents a total addressable market approximately 10 times larger than the traditional software market.

• Large Language Models (LLMs) are enabling unprecedented capabilities in processing text, images, videos, voice, and code across various sectors
• These technologies are particularly impactful in language-heavy industries that previously saw limited benefits from software innovation

Copilot model overview: AI copilots function as digital assistants that work alongside users to enhance productivity while maintaining human control over workflows.

• Pricing typically follows a per-seat model similar to traditional cloud software
• Major tech companies like Microsoft have successfully implemented significant price increases by adding copilot features to existing products
• Different modalities include code completion (Github Copilot), text processing (Harvey for legal work), voice transcription (Abridge for healthcare), and image generation for design and construction

Agent-based solutions: Unlike copilots, AI agents fully automate workflows with minimal human intervention.

• Pricing models are still evolving but often relate to the cost savings from reduced headcount
• Key applications include software sales (Relevance AI’s Bosh), recruiting (LinkedIn’s Hiring Assistant), customer support (Slang), and back-office functions (Tennr)
• These solutions are being developed by both startups and established companies like Salesforce

AI-enabled services transformation: This model leverages automation to deliver traditional services more efficiently and cost-effectively.

• Services are typically priced below traditional provider rates while maintaining higher margins through AI automation
• Notable examples include EvenUp for legal services, SmarterDx for medical billing, and Reserv for insurance claims processing
• Companies can often undercut existing providers while delivering superior service quality

Emerging pricing strategies: Vertical AI companies are adopting hybrid pricing models that combine predictable revenue with usage-based upside.

• Many companies implement output-based pricing tied to specific deliverables
• Tiered pricing structures and base subscription fees ensure predictable baseline revenue
• Companies like DeepL, EvenUp, and Intercom demonstrate various approaches to value-based pricing models

Strategic implications: The success of vertical AI implementations will largely depend on how effectively companies can align their business models with industry-specific needs and value creation.

• The market is still evolving, with new pricing models and delivery mechanisms being tested
• Companies must balance automation capabilities with industry-specific requirements
• The ability to demonstrate clear ROI and value proposition remains crucial for adoption