The 10 Minute AI Feature Prioritisation Blueprint

追加: 2026-06-02

[00:00:00]Everyone is building AI features these days. But what we don't know is how we need to prioritize the features. If we have 20 features on our board, which one to choose first, which one to build first? Today in this video, I'm going to walk you through a step-by-step process on how you can build your AI feature which is going to bring user value, bring the business outcomes, and you're able to adapt AI.

[00:00:27]Let's start on our six steps journey for prioritizing AI features from defining what we want to build to commitment to what we have finalized. The first step in AI feature prioritization is to clearly define the decision you are trying to make to make that decision. I really like this template. We are deciding X so that we can achieve Y by Z date. For us breaking it down, X means which AI capability to build. Y means the measurable outcome we want to achieve. Z is the time frame in which we want to achieve it. This statement helps you anchor the discussion around the business outcome rather than jumping straight into a specific solution.

[00:01:24]For example, we are deciding which AI capability to build so that we can reduce customer support resolution time by 20% by the end of Q3. Here your X becomes which AI capability to build. Your Y becomes resolution time by 20% we want to reduce and the Z becomes end of Q3.

[00:01:51]At this point you haven't chosen the solution yet. There could be several possible AI features that could help you achieve this goal. As a product manager, your job is to evaluate these AI feature options you have and determine which one is most likely to deliver the desired outcome.

[00:02:12]This step helps you jump into building the most exciting AI idea with a clearly defined objective.

[00:02:23]You ensure that every feature you consider is tied to a specific business goal and timeline. This helps you create alignment across the product, engineering, leadership before you get into scoring or prioritizing anything at all. Once you're clear with this, you can move on to the next step. Step two, create your AI feature board. You must have all of the AI ideas that you have got maybe from engineering teams or sales leadership customers even your own product discovery ideas. All of these ideas you place them into a feature board because that is when you get an holistic view of what all AI potential solutions I have right now in my backlog.

[00:03:15]Why I love doing this? Well, this step matters because it helps you see the full landscape of possibilities. It helps you see which features can bring impact, no matter how big or small. It helps you pause and think so. So, you don't overlook any valuable features.

[00:03:36]Well, now that you have all of your AI feature ideas in a single board, the next step is to validate. validate whether these are really important to the clients that we have the customers are they going to actually use it that is very important and for those validations I have two approaches and one of my preferred one so let's talk about those validation approaches the two validation approaches are one is AI to AI simulation and the other one is persontoerson person. Now in AI to AI simulation, your interviewer and your user both are AI systems. They are having the conversation together and they get the answers, analyze the patterns and tell you what the validation outcome is for the feature that you wanted to validate. Obviously, it's faster. You don't have to run the interviews and it gives you quick insights. You might as well have each of the AI feature validated in less than 30 minutes.

[00:04:50]On the other hand, it's persontoperson which is a traditional way that we have been using for years and years now. In this traditional approach, you as a product manager or a designer is talking to the user itself, the clients, customers who are going to use your product in the first place. Those real people, you talk to them and you understand and you decide right now that is a bit slower because it takes you know let's say that you want to do 10 interviews. It's going to take 1 hour interview each. you end up in 10 hours just spending on validating the approaches.

[00:05:29]So I prefer the one where it's a mix of both. I would prefer that maybe in the initial validation process I do the AI to simulation I define my personas I get the insights then instead of the 10 users that I wanted to interview in the first place now I am going to interview just three or four critical users. This way I will rather have two or 3 hours spent to validate each of the approach.

[00:05:59]Maybe less if I talk to less users and I depend on AI to AAI simulation. At least this way I will have a better understanding of where we are with each of the AI features as well as you will get a real insight, a real input of a real user to validate what you found out from the AI2A simulation. So the mix of both helps me do a speedy validation instead of spending hours and hours just validating tens or you know maybe 20 features that I have on my feature board.

[00:06:39]Now that we have validated all of the AI approaches, we are going to move towards scoring them. Scoring is ranking them on a board from top to bottom. top being the highest priority and bottom being the lowest priority. There are various frameworks we can use to do this exercise but I prefer rice and ice at the as the best uh frameworks a product manager can use because it includes everything that we care about.

[00:07:12]If you want to get into the details on how to calculate the rice and ice scores, you can watch my next video about it.

[00:07:21]Well, your work doesn't ends where you're scoring each AI idea. Let's say for example, I had this list of AI features and now I have added these priority scores.

[00:07:31]That is just a starting point. These are telling you all of the initial stage estimates. They're not final because the most important step is adjustment.

[00:07:44]Adjustment of what? Well, before finalizing what you saw in the dashboard, you must analyze the strategic constraints and adjust each of the scoring. For example, your current business priority is to do X. On the other hand, your score said Y. So that's why you must understand that what the strategic considerations there are that I must be taking care of. Let's say available resources. One thing can be done in a few weeks. One might take a quarter up. So I need to understand what available resources I have to reduce or increase which feature I want to build.

[00:08:29]Let's talk about this example here that I am looking at. The rice initial score was saying that automatic ticket categorization has a 76 rice score. So it is landed in a third point. But now I have some strategic constraints here. I know this automatic ticket categorization is a smaller piece of work and it can give me a win in a much less time. It can be delivered in the quarter and it supports leadership goal as well. So from here at the third point now I'm moving it to the first point because even though the AI generated response suggestions was the highest r score but the timeline to implement was 6 months. That's why I moved it to the second point and I took the quick win. I created the automatic ticket categorization. I delivered it in the quarter. I was able to fulfill the leadership goals as well.

[00:09:34]Now when I have created the board, I validated my ideas, I know what I want to build, I have scored them, I've adjusted it for strategy. Now it's time to commit and communicate. So that is where we are at the final very step.

[00:09:55]Make the plan commitment and then communicate it to all of the leadership.

[00:10:03]So today in this video we learned the blueprint summary how we can prioritize AI features.

[00:10:11]Step one we talked about defining your decision. What is your goal? What do you want to achieve? The step two is creating the AI feature board. Adding all of the AI ideas that you have from all of the different uh people engineering consultants whatnot. You're adding it into a single feature board because that helps you look at it from a broader perspective to make sure that you're not missing out on any feature that could bring user value and business outcomes even if it's a small AI feature in the first place. Then you validate each feature. We talked about two approaches. One was AI to AI simulation.

[00:10:53]The other was persontoperson interviews.

[00:10:56]And I told you that my preferred approach is to have a mix of both.

[00:11:01]That's what the validation is about. Now that you have validated your AI features, you know which one you have to focus on when you're scoring them and which one you do not want to move forward with. Even if you don't want to move to the scoring stage, you must write down why you don't want to build a specific AI feature. The next one is scoring them. We talked about the best frameworks, rice and ice. If you want to have a detailed explanation on how rice and ice works, watch my video on the best framework prioritization for product managers. Then we adjust strategically. We talking about the strategical constraints. We are adding them onto all of the scores that we have created. we are adjusting it. We are creating a new board that becomes your road map and you communicate that road map to all of the product leadership in January everyone and then you commit to your multiquarter road map accordingly.

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[00:12:18]Thank you for watching.