To keep non-technical stakeholders engaged, don't show code or API responses. Instead, have team members role-play a customer scenario (e.g., a customer service call) to demonstrate the 'before' and 'after' impact of a new platform service. This makes abstract technical progress tangible and exciting.

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Instead of guarding prototypes, build a library of high-fidelity, interactive demos and give sales and customer success teams free reign to show them to customers. This democratizes the feedback process, accelerates validation, and eliminates the engineering burden of creating one-off sales demos.

A platform's immediate user is the developer. However, to demonstrate true value, you must also understand and solve for the developer's end customer. This "two-hop" thinking is essential for connecting platform work to tangible business outcomes, not just internal technical improvements.

Generic use cases fail to persuade leadership. To get genuine AI investment, build a custom tool that solves a specific, tangible pain point for an executive. An example is an 'AI board member' trained on past feedback to critique board decks before a meeting, making the value undeniable.

Bupa's Head of Product Teresa Wang requires her team to explain their work and its value to non-technical people within three minutes. This forces clarity, brevity, and a focus on the 'why' and 'so what' rather than the technical 'how,' ensuring stakeholders immediately grasp the concept and its importance.

Stakeholders will ask "so what?" if you only talk about developer efficiency. This is a weak argument that can get your funding cut. Instead, connect your platform's work directly to downstream business metrics like customer retention or product uptake that your developer-users are targeting.

To get product management buy-in for technical initiatives like refactoring or scaling, engineering leadership is responsible for translating the work into clear business or customer value. Instead of just stating the technical need, explain how it enables faster feature development or access to a larger customer base.

To replace a technical expert in a sales process, an AI's value isn't just its data. It should be prompted to explain concepts through storytelling, visualizations, and 'future scaping.' This shifts the AI from a mere information-dispenser to a persuasive communicator that resonates with a buyer's emotions.

Shift your team's language from tracking output (e.g., 'deployed XYZ API') to tracking outcomes. Reframe milestones to focus on the business capability you have 'unlocked' for other teams. This small linguistic change reorients the team toward business impact and clarifies your contribution to metrics like NPS.

To win over skeptical team members, high-level mandates are ineffective. Instead, demonstrate AI's value by building a tool that solves a personal, tedious part of their job, such as automating a weekly report they despise. This tangible, personal benefit is the fastest path to adoption.

The V0 team dogfoods their own AI prototyping tool to define and communicate new features internally. Instead of writing specification documents, PMs build and share working prototypes. This provides immediate clarity and sparks more effective, tangible feedback from the entire team.