Remember when product roadmaps were these beautifully crafted, multi-year documents that everyone treated like gospel? Yeah, those days are gone. And frankly, good riddance.
I was talking to a product lead at a major tech company last week – let’s call her Sarah – who showed me their “AI-era planning process.” It looked nothing like the Gantt charts and waterfall timelines I grew up with. Instead, it was a living document that changed almost daily, with more conditional statements than a legal contract. When I asked her how she managed stakeholder expectations with such fluid planning, she smiled and said, “We don’t plan outcomes anymore – we plan for learning.”
This shift isn’t just about adopting agile methodologies or moving faster. It’s fundamentally about recognizing that in the age of AI, the ground beneath our feet is constantly shifting. The traditional planning paradigm assumes a level of predictability that simply doesn’t exist when your competitors include algorithms that can pivot in milliseconds.
Look at what’s happening with large language models. When GPT-3 launched, nobody predicted the explosion of AI-powered writing tools that would follow. The companies that succeeded weren’t the ones with the most detailed five-year plans, but those who could quickly experiment and adapt. As one startup founder told me, “Our quarterly planning sessions now focus more on defining what we need to learn than what we need to deliver.”
This aligns perfectly with what I call the 「product development golden rules」 from The Qgenius Golden Rules of Product Development. The principle of 「starting from user pain points in niche markets」 becomes even more critical when AI can help you identify those pain points with unprecedented precision. But here’s the catch: the pain points themselves are evolving at AI-speed.
Take the concept of 「mental models」 from the golden rules. Users’ mental models are being reshaped daily by their interactions with AI tools. The way people think about search has completely transformed since ChatGPT made conversational interfaces mainstream. If you’re still planning based on last year’s user research, you’re already behind.
And what about the rule that 「products are compromises between technology and cognition」? Well, AI is blowing up both sides of that equation. The technology is advancing at breakneck speed, while user cognition is struggling to keep up. The most successful products I’ve seen recently aren’t the ones with the most advanced AI – they’re the ones that make complex AI feel simple and intuitive.
Here’s what I’ve observed about planning in successful AI-era product teams:
First, they’ve shifted from deterministic planning to probabilistic thinking. Instead of asking “What will we build?” they ask “What might we discover?” and “How will we respond?”
Second, they’re building planning systems that embrace emergence. The most interesting opportunities in AI aren’t the ones we can predict – they’re the ones that emerge from the interaction between users, data, and algorithms.
Third, they’re measuring progress differently. Traditional planning focuses on output metrics – features shipped, deadlines met. AI-era planning focuses on learning velocity – how quickly are we understanding user needs and adapting our approach?
Don’t get me wrong – this doesn’t mean throwing planning out the window. It means planning has evolved from being a blueprint to being a compass. You still need direction, but you also need the flexibility to navigate around unexpected obstacles and opportunities.
The companies that will thrive in this new era aren’t the ones with the perfect plans, but the ones with the most adaptable planning processes. They understand that in the AI era, the plan isn’t the destination – it’s the vehicle for discovery.
So, what’s your planning process looking like these days? Still treating your roadmap like scripture, or have you embraced the beautiful chaos of AI-era product development?