From 'What to Build?' to 'Why to Build It now?': AI for Prioritization Clarity
Nov 16, 2024
From "What to Build?" to "Why Should We Build It Now?"
I remember vividly staring at a whiteboard, markers in hand, during a particularly intense product planning session. It was late, and the energy in the room was a mix of exhaustion and fervent passion. Everyone, myself included, was convinced their idea was the one that would revolutionize our product. The air was thick with the familiar question that always seemed to hang heavy: "What should we build next?" We had a dozen amazing ideas, countless user stories, and a finite amount of engineering time. It felt like we were trying to choose a winning lottery ticket based on vibes.
I've been in the product world long enough to see the struggle firsthand. It usually starts with a whiteboard, a flurry of ideas, and that familiar question that hangs in the air: "What should we build next?" Everyone's got their favorite features, their urgent fixes, and their big-dream projects.
For years, we've relied on gut feelings, stakeholder demands, and limited data. It’s a bit like navigating a dense fog with just a compass. You might get where you’re going, but it’s rarely efficient. Now, AI, especially these newer, agentic forms, is starting to change how we approach this.
The Old Way: Just a Shot in the Dark
Think back to your last product roadmap meeting. Was it full of clear, objective insights, or did it feel more like a negotiation? How much time did you spend debating the real value of each idea versus just whether it was possible to build? We've all been there, trying to quantify things that feel inherently unquantifiable.
I remember one project where we spent months building a feature because a single large customer asked for it. It was a huge effort. When we launched it, adoption was lukewarm, and that customer churned six months later for unrelated reasons. That was a painful lesson in prioritizing based on just one data point and a loud voice.
AI: Your Clarity Agent, Not a Fortune Teller
I'm not saying AI is a magic wand that will zap away all our prioritization headaches. But it's quickly becoming an incredibly powerful tool for bringing clarity and objectivity into the mix. Instead of just guessing "what" to build, we can use AI to really understand why we should build something, and even more importantly, why now.
Here’s how agentic AI helps us move beyond simple "what ifs" to concrete "why we should absolutely build this":
1. Making Sense of All the Noise:
Imagine feeding your AI agent every piece of customer feedback, every support ticket, sales call transcript, product usage stat, and market trend report. An AI "Clarity Agent" can spot patterns and connections that would take a human team weeks, maybe months, to uncover. It can highlight recurring pain points across different channels, identify features requested by customers at high risk of churning, or connect feature usage directly to expansion opportunities.
Example: Your AI agent might flag that 70% of churned users mentioned struggling with a specific onboarding step. At the same time, sales calls reveal prospects are constantly asking for better guidance in that exact same area. Suddenly, "improve onboarding flow" isn't just a good idea; it's a critical, urgent priority.
2. Predicting Impact, Not Just Estimating It:
Traditional prioritization often relies on guesstimating impact. AI can take us way beyond that. By digging into historical data, an agent can predict the likely impact of building a certain feature on key metrics like retention, activation, or conversion. It can even run scenarios, showing you the "cost of doing nothing" if you ignore a problem now, or the "opportunity cost" of choosing to build X instead of Y.
Example: Instead of just saying, "Feature Alpha might increase retention," an agent could analyze past data and confidently project, "Implementing Feature Alpha is predicted to reduce churn by 3% for a specific segment of users, which translates to $X revenue saved within six months, based on similar past interventions." This kind of data-backed projection is a game-changer.
3. Uncovering the Hidden Gems (and Silent Killers):
Sometimes, the most important problems aren't the ones shouting the loudest. They’re the "silent killers" – those small frustrations that build up over time, or untapped opportunities that aren't immediately obvious. An AI agent, constantly processing mountains of data, can shine a light on these. It can point to features that, while not explicitly requested, would dramatically improve the experience for your high-value customers, or flag a subtle shift in product usage that hints at a brand-new market opportunity.
Example: An agent might discover that users who frequently engage with a particular, underutilized feature have a significantly higher LTV (Lifetime Value). This tells you that investing in improving or promoting that feature could bring an outsized return, even if no one on your team is explicitly asking for it.
From "What" to "Why This, Why Now?"
The real power shift happens when we use AI not just to brainstorm, but to build a robust, data-driven argument for why a particular initiative deserves our attention right now. It transforms prioritization from a subjective debate into an objective, evidence-based conversation. Product managers can walk into a roadmap meeting armed with deep insights, predictive analytics, and a clear understanding of the potential ripples of their choices.
It’s about building a product strategy that’s not just informed, but intelligently prioritized.
The Takeaway for All You Builders Out There
If you're building products, you need to be thinking about how agentic AI can become your personal "Clarity Agent." It's pushing us beyond just "AI as a tool" or "AI as an assistant" to a place where AI is genuinely helping us make better decisions.
Start messing around with it. Feed your AI tools your existing data. Ask them to find correlations, predict outcomes, and highlight unusual patterns. The goal isn't to replace your human intuition – it’s to supercharge it with a level of insight and objectivity we simply couldn't get before.
This isn't just about building faster; it's about building smarter.
So, it's time to stop endlessly asking "What should we build?" and start asking, with real confidence and data to back it up, "Why is this the most important thing to build now?" Let AI help you answer that question with unprecedented clarity.