Why You Can't Automate a Process That Doesn't Exist Yet
AI can make a defined process smarter, but it cannot replace the need for a clear trigger, inputs, decisions, outputs, ownership, and human judgment.
Purple Brains Blog
Insights on product strategy, management, and digital innovation from our team of experts.
Product Culture Starts With Shared Context
Product culture grows when the whole team understands the customer, the problem, the objective, and why each story exists.
Building Is Cheap Now. Deciding What to Build Isn't.
AI tools removed the long build as an excuse. A working prototype now takes days, so a bad call about who you are building for shows up immediately instead of hiding behind the roadmap. The two jobs that decide whether a product survives, product management and go to market, were always the harder work.
The Only Thing You Actually Manage Is Uncertainty. Here Is Where Most Product Managers Freeze.
Most PMs stop when they hit 80% confidence. They want certainty before deciding. The real skill is making the call with imperfect information, moving forward, and course-correcting when you learn more.
The Frontend Is the Spec: How I Kill Ambiguity With AI Prototypes
When product cycles compress by 10x, the traditional PRD-to-handoff workflow breaks. Here is what I do instead: build a functional frontend prototype with AI tools and hand that living spec to developers.
The Runtime Problem: Why 40% of Agentic AI Projects Will Fail
Gartner predicts over 40% of agentic AI projects will be canceled by 2027. The reason isn't weak models. It's a missing runtime.
Why Most AI Pilots Never Become Products
Most AI pilots and MVPs do not die because the technology is weak. They die because teams build before they understand the problem, the market, the business model, and the path to adoption.
The Product Strategy Behind the SpaceX IPO
SpaceX is not just a rocket company. It is a product system where each layer makes the next one stronger. That is the real lesson for founders.
The Most Expensive AI Mistake: Using the Best Model for Every Task
The smartest AI teams are not the ones using the strongest model everywhere. They are the ones matching each task to the right model, cost, and level of reliability.