“People don’t buy what you do; they buy why you do it.”
— Simon Sinek
It’s very common nowadays, to see teams chase every new coding assistant, chatbot, and AI agent—only to end up spinning in circles. So let’s drill down here. When you let shiny tools dictate your roadmap, you muddle your product vision, lose sight of long‑term goals, and burn through compute and engineering resources on whims.
The Allure and the Danger of AI Sprawl
- Building prototypes that wow execs in slide decks… only to gather dust when they don’t solve a real problem.
- Sprinkling AI “features” everywhere… until no one can see how they connect to your core mission.
- Pouring budget into endless model / agent experiments… with no clear metric for success beyond “it’s cool.”
Why Your “Why” Matters More Than Ever
- Unites engineers and PMs
When every feature ties back to a clear purpose, your data scientists, software engineers, frontend engineers, and product managers all pull in one direction. - Sharpens prioritization
A strong why acts like a filter—if a proposed AI component / or even an entire product doesn’t advance your core outcome, it stays in the backlog. - Creates real impact
Customers won’t care that you used a fancy agent; they’ll care that you saved them hours, cut costs, or removed a bottleneck.
Anchoring Your AI Roadmap in Purpose
- Surface the real pain
Talk to users. Observe where they get stuck. Identify the repetitive task or decision they dread. - Pinpoint the motivator
Are you freeing up their time, reducing errors, or unlocking a new capability? - Boil it down
Craft a one‑sentence why:
“We automate invoice matching so finance teams close the books two days faster.”
4. Validate early
Share that sentence in customer interviews, landing‑page copy, and internal demos. Tweak until it resonates.
Embedding Your “Why” in Every Decision
- Roadmap gating: No AI feature ships without a “why this matters” section in the PRD.
- Demo storytelling: Every prototype begins with “Here’s the problem…” and ends with “Here’s the outcome.”
- Outcome metrics: Track business or user impact (time saved, errors avoided), not just model accuracy or API usage.
In an era where anyone can spin up a new AI agent in minutes, discipline is your greatest advantage. By answering your why before writing a single line of code, you ensure that your AI product isn’t just a collection of slick demos, but a cohesive story that delivers real value—powered by thoughtful engineering and guided by purposeful leadership.
Here’s to building revenue and business value generating AI Products.
Infinite Couch 