A common trap is starting with the assumption that AI must be used, leading to a search for a place to tack it on. This results in superfluous features like a generic "AI assistant," rather than solving a real user need. The correct approach begins with the user's pain.

Before launch, product leaders must ask if their AI offering is a true product or just a feature. Slapping an AI label on a tool that automates a minor part of a larger workflow is a gimmick. It will fail unless it solves a core, high-friction problem for the customer in its entirety.

Instead of relying solely on top-down, consultant-led workflow automation, enterprises should empower individual employees with AI tools. This builds user fluency and intuition, allowing them to pull AI into their own workflows, resulting in greater overall impact and less disempowerment.

Most users don't want abstract tools like 'agents' or 'connectors.' Successful AI products for the mainstream must solve specific, acute pain points and provide a 'golden path' to a solution. Selling a general platform to non-technical users often fails because it requires them to imagine the use case.

Unlike traditional software, AI adoption is not about RFPs and licenses but a fundamental mindset shift. It requires leaders to champion curiosity and experimentation. Treating AI like a standard IT project ignores the necessary changes in workflow and thinking, guaranteeing failure.

Selling to engineers requires winning bottoms-up adoption, as leaders won't dictate tools. However, you also need a top-down motion to articulate business outcomes (like R&D cost reduction) to executives. Neither approach works in isolation for developer-centric products.

A common AI implementation failure is assuming users think like technologists. Trivial technical details can be huge adoption blockers. To succeed, focus on building user trust and actively partner with customers to operationalize the technology, rather than simply delivering it and expecting them to figure it out.

Companies fail with AI when executives force it on employees without fostering grassroots adoption. Success requires creating an internal "tiger team" of excited employees who discover practical workflows, build best practices, and evangelize the technology from the bottom up.

In the rush to adopt AI, teams are tempted to start with the technology and search for a problem. However, the most successful AI products still adhere to the fundamental principle of starting with user pain points, not the capabilities of the technology.