Customers are hesitant to trust a black-box AI with critical operations. The winning business model is to sell a complete outcome or service, using AI internally for a massive efficiency advantage while keeping humans in the loop for quality and trust.

For enterprise AI adoption, focus on pragmatism over novelty. Customers' primary concerns are trust and privacy (ensuring no IP leakage) and contextual relevance (the AI must understand their specific business and products), all delivered within their existing workflow.

Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.

Legora wins 85% of competitive deals by focusing on three things: product quality, team dedication, and their long-term roadmap. In a fast-moving field like AI, enterprise clients are betting on a partner who can navigate the future, not just a tool for today.

While model performance is key, the real defensibility for enterprise AI applications lies in the surrounding software stack. This includes tooling for compliance, testing, integrations, and business logic management, which are necessary to make powerful AI safely deployable within large organizations.

To mitigate risks like AI hallucinations and high operational costs, enterprises should first deploy new AI tools internally to support human agents. This "agent-assist" model allows for monitoring, testing, and refinement in a controlled environment before exposing the technology directly to customers.

In the AI era, large enterprises still prefer vendors who act as partners, offering on-site training and change management support. This "old-school" approach builds trust and ensures successful adoption, often trumping a purely tech-driven or product-led growth (PLG) motion.

For enterprises, the raw capability of foundation models is a security risk, not a selling point. The real product value lies in building "boundaries"—robust permissions, approvals, and audit logs that make powerful models safe to deploy company-wide.

Despite strong interest in AI security, Netskope's CEO notes a lag in sales cycles because enterprises lack an established playbook. Customers are in a learning phase, trying to understand how to implement and budget for AI security, which pushes actual purchasing decisions further out.