--- title: "How We Decide What to Build Next: AI Product Prioritization" description: "Our prioritization framework: user value x AI advantage x technical feasibility x competitive urgency. If AI does not make it better, it is not a priority." canonical: https://nowah.xyz/blog/ai-product-prioritization lastModified: "2026-08-07T07:54:07.390Z" --- # How We Decide What to Build Next: AI Product Prioritization Our prioritization framework: user value x AI advantage x technical feasibility x competitive urgency. If AI does not make it better, it is not a priority. Every product team has more ideas than capacity. That is normal. What is not normal is building an AI product where the platform's capabilities expand faster than your team can ship features. New model releases improve the agent's intelligence overnight. A tool that was impossible last quarter becomes straightforward this quarter. User requests come in through conversation data in real time, not through quarterly surveys. Traditional product prioritization frameworks do not account for any of this. RICE (Reach, Impact, Confidence, Effort) was built for a world where your product's core capabilities are stable and you are adding features on top. That is not how AI products work. The foundation shifts under your feet every few months. We built our own prioritization framework because we had to. Here is how it works, why it exists, and what we have learned from using it. ## The unique challenge of AI product prioritization ![Illustration for this section](https://pics.nowah.xyz/website-media/product-066-img-1.webp) In a traditional software product, the stack is stable. Your database, your backend, your frontend framework. These do not change meaningfully from quarter to quarter. You plan features on top of a predictable foundation. AI products have a moving foundation. The underlying model gets smarter on a timeline you do not control. Last year, our agent struggled with complex multi-[city itineraries](/blog/launching-multi-city-itineraries-complex-planning). A model update fixed that without us writing a single line of code. Conversely, sometimes a model update introduces regressions in areas that were working fine. This creates a planning problem. How do you prioritize building a feature that improves X when the model might improve X on its own next month? How do you justify investing engineering time in a capability that could become obsolete or trivially simple with the next model release? The other unique challenge is data abundance. In a traditional product, you survey users, run analytics, and infer what they want. In an AI travel product, users literally tell the agent what they want, every single day, in natural language. "Can you help me find travel insurance?" is not an NPS comment. It is a direct feature request embedded in a conversation. We have thousands of these daily. The combination of unstable capabilities and abundant signal makes traditional frameworks feel outdated. You need something that accounts for both. ## Our framework: four factors We score every potential feature or improvement along four dimensions: **User value.** How much does this improve the user's experience? We measure this by frequency (how often users would use it), intensity (how much it helps when they use it), and breadth (what percentage of users care about it). A feature that 80% of users benefit from weekly scores higher than a feature that 5% of users need once a year, even if that rare feature is impressive. **AI advantage.** This is the filter that makes our framework different. We ask: does this feature meaningfully benefit from AI? If the answer is no, it drops in priority. A settings page does not benefit from AI. A flight booking flow benefits enormously. A currency converter benefits moderately (AI can add context like "the dollar is weak against the euro right now, consider waiting"). We are an AI-native product, and our limited engineering capacity should go toward features where AI creates a differentiated experience. **Technical feasibility.** How hard is it to build, given our current architecture and team? This includes estimated development time, infrastructure requirements, data dependencies, and risk of unexpected complexity. We are honest about this factor because AI features have a habit of being 80% done in a week and then taking two months for the last 20%. **Competitive urgency.** How quickly are competitors closing this gap? If [Google Flights](/blog/best-flight-booking-2026-ai-vs-google) launches a feature that directly [competes with](/blog/small-team-competes-with-expedia) something on our roadmap, that feature gets an urgency boost. If we have a clear differentiation advantage and no one is close, we can afford to take our time and get it right. Each factor gets a score from 1 to 5. The composite score is the product (multiplication, not addition) of all four. This matters because multiplication means a zero in any dimension kills the feature. A feature with massive user value but zero AI advantage gets a zero. A feature with great AI advantage but zero user value also gets a zero. ## The AI advantage filter I want to spend more time on the AI advantage factor because it is the most counterintuitive part of our framework, and the one that generates the most internal debate. The question is simple: if we removed the AI component from this feature, would it still be meaningfully different from what competitors offer? If the answer is yes, then AI is not providing an advantage and the feature is not playing to our strengths. Example: we considered building a detailed flight comparison page where users could see seat maps, baggage policies, and layover details side by side. Useful? Absolutely. AI advantage? Minimal. Google Flights and Kayak already do this well with traditional UI. Building it would not leverage what makes Nowah different. Instead, we invested in the agent's ability to explain flight tradeoffs conversationally: "Flight A is $80 more but saves you a 3-hour layover in Newark. Given that you have told me you hate long layovers, I'd pick Flight A." That is something only an AI agent with memory can do. It plays to our unique strength. Another example: we debated building a travel insurance marketplace. Insurance is a real user need. But presenting insurance options in a grid is not an AI problem. What is an AI problem is the agent proactively recommending insurance based on your destination, trip length, and past travel patterns: "You are going to Japan for two weeks. Last time you traveled internationally, you did not have insurance and mentioned you wish you had. Want me to find a policy that covers medical and trip cancellation?" The AI advantage filter forces us to ask not "is this useful?" but "is this differentially useful because of AI?" The answer determines whether we build it now, build it later, or let competitors handle it. ## What we deliberately did not build The features we said no to are as important as the ones we said yes to. Here are a few. **Explore/discover page.** Every travel app has a page where you browse destinations with pretty photos and deal cards. It is standard because it drives engagement metrics. We did not build it because it is fundamentally a browse-and-scroll pattern that does not benefit from AI. If you want to explore destinations, you can ask the agent: "Where should I go in September for under $1,500? I like warm weather and historical sites." That is a better discovery experience, and it is already built into the core product. **Manual trip editing.** We considered letting users manually drag and drop itinerary items, add custom events, and restructure their trip plans. Power users would love it. But it is the opposite of our product thesis. We want the AI to manage your itinerary. If something needs changing, you tell the agent. Manual editing tools would undermine the conversational model and require significant UI investment with no AI advantage. **Social features.** Trip sharing, follower profiles, community forums. These are engagement drivers in traditional travel apps. We said no because social features do not benefit from AI, they require a critical mass of users to be valuable (which we do not yet have), and they would distract us from the core booking experience. Each of these was a reasonable feature idea with genuine user value. But none passed the AI advantage filter, and all would have consumed engineering time that we invested in making the agent smarter instead. ## Community input through conversation data Here is something that traditional product teams would kill for: real-time, natural language feedback from every user interaction. When a user asks the agent "Can you find me a car rental?" that is a feature request. When a user says "I wish you could check my [visa requirements](/blog/ai-agents-visa-requirements-documents)," that is a feature request. When a user tries to book a multi-city trip and the experience is clunky, the conversation data shows exactly where it breaks down. We analyze [conversation patterns](/blog/designing-for-conversation-ux-patterns) to identify the most requested capabilities that the agent cannot currently handle. This is not a manual process. We aggregate conversation topics, identify clusters of unmet needs, and quantify how many users are asking for something we do not offer. The result is a continuously updated picture of user demand, ranked by frequency and specificity\. "Find me travel insurance" appears 340 times per month\. "Can you book a car rental?" appears 280 times\. "Help me plan activities in \[city\]" appears 520 times\. These numbers directly feed into the user value score of our prioritization framework\. The qualitative data is equally valuable. Conversations show not just what users want but how they think about it. Users do not say "I want a visa requirements tool." They say "Do I need a visa for Thailand?" The framing tells us how to build the feature: not as a separate tool, but as knowledge the agent should have natively. ## Competitive urgency We track competitors across three categories: other AI travel startups, OTAs adding AI features, and general AI assistants expanding into travel. Competitive urgency is not about copying features. It is about protecting our differentiation while it matters. If a competitor launches a capability that directly overlaps with one on our roadmap, the urgency of building our version increases. Not because we need feature parity, but because differentiation erodes quickly in AI and we need to stay ahead in the areas that matter. Conversely, if a competitor builds something that we deliberately chose not to build (like a social feed or an explore page), we do not react. Their strategy validates their thesis, not ours. We stay focused on the AI-native experience. The urgency factor also applies to emerging AI capabilities. When a new model release makes something possible that was not before (like reliable multi-step planning or accurate fare prediction), the urgency of building features that leverage that capability spikes. First-mover advantage in AI features is real because it takes time for the product integration to catch up with the raw capability. ## Revisiting decisions quarterly The worst thing a product team can do with a framework is set it and forget it. We re-score the entire roadmap quarterly because the inputs change constantly. AI capabilities improve, which changes technical feasibility scores. User demand shifts, which changes user value scores. Competitors make moves, which changes urgency scores. A feature that scored poorly six months ago might score well today because the model improved, or because conversation data revealed unexpected demand, or because a competitor launched a related feature. We also retrospectively evaluate the features we shipped. Did they deliver the user value we expected? Did the AI advantage materialize in practice? Were there unexpected complexities? This [feedback loop](/blog/ai-feedback-loop) tightens the framework over time. The quarterly review is also where we kill features that have been on the roadmap too long without being built. If something has sat in the backlog for two quarters, either the urgency is genuinely low (in which case, drop it) or we have been wrongly deferring it (in which case, bump it up). Stale backlog items are decision debt, and we pay it down regularly. Product prioritization in AI is not a process you can run on autopilot. The landscape moves too fast. The framework gives us a consistent language for making decisions, but the decisions themselves require judgment, context, and a willingness to change course when the inputs change. That is the job. And in a space that moves as fast as AI travel, it is a job that never gets boring. --- Nowah is an AI travel agent that searches and books real flights and hotels through conversation — no filters, no thirty open tabs. [Plan your next trip](https://app.nowah.xyz).