Customers have a double standard for mistakes. They accept that humans err, but expect AI-driven systems to be 100% accurate from the start. This creates a significant challenge for product managers in setting realistic expectations for new AI features.
The market is shifting to platforms, but best-in-class point solutions (like Plaid for bank verification) remain critical. The winning strategy isn't to build everything, but to package these specialized services into a cohesive platform, leveraging their focused excellence for distribution and governance.
Focusing on individual enterprise client needs creates conflicting workflows that hinder scalability. A successful transition involves moving to a user research-driven approach, using data to justify a standardized product direction that serves the broader market, not just a few powerful clients.
To overcome customer trust issues with new AI features, avoid a 'big bang' rollout. Instead, launch with a pilot group. This approach allows the AI model to be trained on real-world data in a controlled environment, improving its accuracy and demonstrating value before a wider release.
In regulated industries like finance, the primary barrier to full AI automation is often regulation, not just user trust. It is the technology provider's responsibility to prove AI's reliability and safety to regulators, much like the industry did to legitimize e-signatures over a decade ago.
Many AI applications focus on content generation (e.g., chatbot answers). The deeper value lies in enabling content consumption: creating actionable insights that help users make better and faster decisions. Product managers should prioritize building features that provide decision support, not just information.