When building conversational AI, be aware that users might mistake it for a human. This requires carefully designing interactions to manage user expectations and clarify the AI's role, ensuring they understand they are not receiving direct instructions from a person.
Intuit's practice of observing customers use products in their actual environments (“Follow Me Homes”) reveals critical context, like interruptions and multitasking. This ethnographic research method provides deeper insights into real-world friction than traditional usability testing in controlled settings.
To drive AI adoption, leaders must balance two opposing actions. They must 'do more' by setting a high bar for creating 'magical' customer experiences. At the same time, they must 'do less' by empowering teams with autonomy, reducing review overhead, and giving them freedom to experiment.
When building AI for high-stakes domains like payroll, you must balance rapid innovation ('gas') with unwavering reliability ('brakes'). While teams can move fast on prototyping, the core promise of compliance and trust is non-negotiable, requiring safeguards, deep expertise, and risk-based rollouts.
Intuit's AI transformation didn't change the 'what' of a PM's job—identifying problems and shipping solutions. Instead, it changed the 'how.' Performance evaluations now emphasize skills like rapid prototyping, an experimentation mindset, and using AI tools to achieve core outcomes more effectively.
Accessible prototyping tools are changing product norms. The expectation is shifting from presenting detailed Product Requirements Documents to sharing interactive prototypes. This visual, hands-on approach accelerates discussions, improves decision quality, and makes ideas tangible for a wider audience.