Product teams often struggle to deliver meaningful outcomes. Why? Because they confuse activity with progress. Understanding that challenge is the focus of our chat with David Pereira, recorded in person at the 2026 ITX Product + Design Conference, in Rochester, NY.

David is a product management coach, speaker, and author of Untrapping Product Teams: Simplify the Complexity of Creating Digital Products. He has spent that past 17 years helping organizations create more value by eliminating busy work. In his conference workshop and keynote, David drew from his work with product teams around the world, explaining how eliminating unnecessary work, validating assumptions early, and staying close to customers enable teams to make better decisions and build products that solve real problems.

Eliminate Busy Work to Create More Value

Many organizations unintentionally create systems that reward activity instead of outcomes, David argues. Excessive meetings, backlog maintenance, and decision making based on conjecture rather than evidence consume valuable time without moving products forward.

Rather than accepting these practices as status quo, David believes that product leaders should ask whether these activities contribute to customer or business value. Set clear boundaries around focus time and make every meeting earn its place on the calendar, he adds.

“Bullshit management is an art,” Davids says. “It’s the art of doing things that create no value, but drain everyone’s energy.”

Build To Learn: Testing Assumptions Before Crafting Solutions

David’s central message in his day 1 conference workshop is that successful product teams learn before they build. Teams naturally become attached to their ideas, making it easy to seek evidence that confirms existing beliefs instead of challenging them. By identifying assumptions, prioritizing the most critical risks, and defining success criteria before running experiments, teams can quickly separate fact from opinion. AI can accelerate experiment design, but only when it supports disciplined learning – not when it replaces critical thinking.

“You will build to learn, because you’ll learn from reality,” David continues. “If you don’t do that, you’re gonna make something that only works in fantasy.”

Customer Reality Should Drive Every Product Decision

In a return to timeless product principles, David reinforces the mantra that decisions should be grounded in real customer behavior rather than internal assumptions. Using examples drawn from his own work, David reveals customer friction that internal discussions had completely overlooked. For product managers and UX professionals alike, direct observation remains among the most effective ways to uncover opportunities that analytics and meetings often miss.

“You need to go and check the users’ reality,” David says. “Because if you limit your discovery to the clinical environment of your cozy office, you’ll quickly learn that that is not how users work. You need to get out there to truly understand their reality.”

• [03:29] Bullshit management is the art of doing things that create no value, but drain your energy. The more bullshit you do, the less value you create.  
• [04:47] [As a product manager,] your job is to simplify what others complicate. So many times we have meanings because it’s the default way of collaborating. So we need to step back and say, what on earth am I trying to achieve?
• [05:51] We all need time for focus. So I block my morning; nobody can reach out to me in the morning because it’s the time I’m going to do deep work. As for meetings, they need to have a few things: what is the objective? What is the context? And what is agenda? If the meeting invitation can’t answer those questions, I immediately reject.
• [09:56] In order to test our assumptions, we need to understand the following: If my assumption is proven false, is my idea still valid? If yes, you can ignore this assumption because your idea will still survive. But if the assumption is proven false, my idea is worthless and I start looking at things differently.
• [17:05] When [AI-driven] execution becomes cheap, where do you put your energy? Your energy goes to thinking, decision making. But thinking and decision making should be a collaborative exercise, not a siloed one.
• [20:49] What is NIHITO? NIHITO is ‘nothing important happens in the office.’ It’s an important lesson about how teams and organizations make product decisions. We need to get out of the office and into our users’ environment if we are ever to understand their reality.

Podcast: Play in new window | Download

Sean Murray [00:00:10] Instead of talking about assumptions, I wanna talk about something that you’ve been writing about a little bit recently, which is bullshit management. And so enlighten our crowd as to what bullshit management is and what’s going on.

David Pereira [00:00:21] It’s the enemy. Alright. For those of you who don’t know, bullshit management is an art. The art of doing things that create no value, but drain your energy. And there’s an equation behind it. The more bullshit you do, the less value you create. And then people are wondering, am I doing bullshit or not? How do you know that? If you can’t say how what you do creates value, you have the answer.

Sean Murray [00:00:49] We got some advice from Rich Mironoff, who’s been on the podcast twice, just released his book Money Stories. One of the things that Rich said that he started early on in his career is that if a meeting invite was sent to him with no agenda or no stated outcomes, he just said no.

David Pereira [00:01:05] That’s a good thing to do also. So the very first thing, you need to have time for focus. So for example, I block my morning. Nobody can reach out to me in the morning because it’s a time where I’m gonna do deep work. And then meetings, they need to a few things. Like what is the objective of the meeting? What is the context? And then what is agenda? And the agenda for me is not saying like the bullet points is how much time I’m going to invest on each topic. And then I need to know what is expected from me in that meeting. If I can’t answer that, I made it a reject.

Dan Sharp [00:01:39] Assumption checks, especially with AI, right? It’s really easy to come up with, you know, 50 assumptions. How do we figure out what assumptions we really need to care about? Because if we go chase all of them, we’ll never get to the doing part.

David Pereira [00:01:53] We need to categorize them, so first we need to understand the following. Ask the first question. If this assumption is proven false, is my idea still valid? And if the answer is yes, well, then you can literally ignore this assumption because your idea will still survive. But then you say, if this is proven to be false, my idea is worthless. You start looking at this differently. Then you say… How confident am I that this assumption is true? That is your data.

Sean Murray [00:02:36] We are here with David Pereira. He is a coach, speaker, and author with 17 years of experience in helping companies achieve more by doing less. I’m excited to hear a little bit about that, right? But it sounds like you do that by helping them cut through the red tape and get to what matters the most, which is creating value. So thank you so much for being with us today.

David Pereira [00:02:56] Thanks for getting me here.

Sean Murray [00:02:58] So I got to be a part of your workshop yesterday, so I’m gonna apply my training, right? So I’m going to run an experiment, a real quick experiment, right, I’m testing the assumption that you don’t want to talk about assumptions, okay? And my expected result is that you say yes, right. So instead of talking about assumptions I want to talking about something that you’ve been writing about a little bit recently, which is bullshit management. And so enlighten our crowd as to what bullshit management is and what’s going on. It’s the enemy. Alright.

David Pereira [00:03:29] For those of you who don’t know, bullshit management is an art, the art of doing things that create no value, but drain your energy. And there’s an equation behind it. The more bullshit you do, the less value you create. And then people are wondering, am I doing bullshit or not? How do you know that? If you can’t say how what you do create value, you have the answer.

Sean Murray [00:03:53] Give us some examples of what you’ve found when working with companies where they didn’t realize that they were executing bullshit management.

David Pereira [00:04:01] But extensive backlog management of meeting marathons, opinion-driven decision-making, all these kind of things, you realize you’re doing them just because you’re going and don’t understand why. Sometimes I ask companies like, how many hours a week each person spend on average in meetings? Then I get crazy answers, like 30 plus hours, and I say, who is working? Because like what happened is that these companies, they start talking about the instead of doing the work. Cause they love dogs.

Dan Sharp [00:04:33] I think meetings is probably a sore subject for all of us. What have you found in your time traveling from company to company as techniques to make meetings more effective, reduce the ones that don’t need to be had?

David Pereira [00:04:47] So there’s one thing that I like accepting. Your real job is to simplify what others complicate. So many times we have meanings because it’s the default way of collaborating. So we need to step back and say, what on earth am I trying to achieve? Is it a decision? Is it something that I want alignment? And then I say like, what is the best way of doing that? Sometimes it’s just a message on Slack or it’s an email saying what you want to do. And if you want make a decision instead of scheduling a meeting for one hour. Just do the preparation and then you schedule a talk to make the decision for 15 minutes because there’s this law that the work will expand to the time you allow if you put one hour it’s gonna last one hour if you would 15 minutes people are forced to think about what makes sense, and then make the decision.

Sean Murray [00:05:34] Yeah, I’ve also we got some advice from rich Mironoff who’s been on the podcast twice just released his book money stories One of the things that rich said that he started early on his career Is that if a meeting invite was sent to him with no agenda or no stated outcomes? He just delete. He just said no

David Pereira [00:05:51] That’s also the very first thing you need to have time for focus. So for example, I block my morning. Nobody can reach out to me in the morning because it’s a time where I’m going to do deep work. And then meetings, they need to have a few things like what is the objective of the meeting? What is the context? And then what is the agenda? And the agenda for me is not saying like the bullet points is how much time I’m gonna invest on each topic. And then I need to know what is expected from me in that meeting. If I can’t answer that, I immediately reject. And when I was a CEO of a company at Hamburg, I was a little bit annoyed with that. I started poking around and seeing meetings. Meetings without this, I would just delete. I delete from the calendar and people say, I had some, I had an exchange there, a catch-up. I said, no, you had a time waster. First you restructure, then you get people to talk about it. But don’t build the conversation without the topic. There’s no need for that.

Sean Murray [00:06:48] No. All right, so everybody, if you get a meeting invite with no agenda or outcome stated, you get to immediately cancel it, and then if somebody asks why, you say, David told you that you had to. Okay. Or Sean, but not me. Not me or Dan.

David Pereira [00:07:04] But there’s one thing I learned, you need to set boundaries, because if you don’t set boundaries nobody will. You own that to yourself.

Dan Sharp [00:07:11] Yeah, yeah, I think that the boundary piece is interesting right like yo as product managers the design leaders as dev managers Right, there’s eight jillion inputs You know on you at any given time yet You’re also in a position of being tasked to solve complex problems, right? How do you? Find time to think

David Pereira [00:07:31] Right, first you need to decide what to think about. So once I read the book called The One Thing, so I started applying this, like every Monday morning I would say, what is the one thing I have to achieve by the end of this week? This is a must have, and then I would look at my agenda and say what is contributing to that? And sometimes there was nothing contributing to that and many things distracting me from particular that. So it’s a I need to adapt my agenda for what I need to achieve this with. And then once I achieve that, I allow myself to do other things.

Sean Murray [00:08:09] I had a really good time in your workshop yesterday. I am now gonna ask you about assumptions. I thought one of the most valuable exercises that I did in the two workshops yesterday was the exercise where you asked us to actually just list the assumptions about the eight ideas that we had created around the scenario that you had us run through. Why are assumptions so important and why is cataloging them before you actually start to build anything so important?

David Pereira [00:08:38] Because of our lab of confidence. It’s very fast for us to fall in love with ideas. And we start asking questions to confirm how right we are. And then the more we get positive signs, the more are willing to invest. So I want to help people to understand that they don’t have all the answers. And the way of getting people to understand that is by helping them see what they don’t know. So if you just slow down for 10 minutes. Asking like, what I assume it to have. And it’s very interesting, the results when I do that. Sometimes I do the exercise and I ask like, how confident you are in your idea? Zero to 10. A lot of people say something between seven and eight. And then they do the exercises. I ask, what about now? Two or three. The execution after that will be very different. You will build to learn. And then you will focus like, I need to learn, I need you to uncover this and so on and so. And in the end, you’re gonna create something that makes sense. Because you’ll learn from reality. If you don’t do that, you’re gonna make something that only works in fantasy.

Dan Sharp [00:09:41] Yeah, so assumption checks, especially with AI, right? It’s really easy to come up with, you know, 50 assumptions. How do we figure out what assumptions we really need to care about? Because if we go chase all of them, we’ll never get to the doing part.

David Pereira [00:09:56] We need to categorize them, so first we need to understand the following. Ask the first question. If this assumption is proven false, is my idea is still valid? And if the answer is yes, well, then you can literally ignore this assumption because your idea will still survive. But then you say, if this is proven, false, my idea is worthless, start looking at this differently. Then you say How confident am I that this assumption is true? That is your data. And if you talk to a few people, your confidence should be very weak. But if you have a payment commitment or something like this, then it’s strong. So for this strong evidence, you don’t have to test. But for the ones like weak evidence and business critical, yes. Then you need to figure out how you test. And then it goes to the thing. Most people will default. The same experiment methods. Most probably it will be A-B testing, prototype or something like this. That’s the moment AI can be your ally. You can ask AI to help you create experiments. You can run in four hours, eight hours, two days or a week. And then you look at that and say, oh, let me start with the ones I can do in four hours so I learn something fast instead of just building something.

Sean Murray [00:11:18] You had a real simple taxonomy for like what is in, like what makes up an experiment. So like what are like the key data elements of a great experiment?

David Pereira [00:11:27] So an experiment will have the assumption you’re testing, with whom you’re testing, what do you expect as a result, defining success. There is one mistake many people do, which is very clear. Like they say, let’s run an experiment. And what is the experiment? We’re going to interview a lot of people and then interview a a lot people. And then they spend two hours discussing the results. And then if you ask like, was it a pass or fail? So you need to define success criteria before. A clear example like I gave yesterday is when I wanted to launch my book, I wanted get to interview a few people. But I said, I don’t wanna send any interview invitation. I want to say I’m writing a book and then based on the book, table of contents, I wanted find people who were interested to talk to me. So at the end of the LinkedIn post, I said if this lands well with you, I wanna talk to you. Success for me! Meant I will get seven people who want to talk to me in four hours. I needed to get seven people DMing me and say, I want to to talk you. And I got and I said, okay, this is a pass, so it works. But that was clear to me. If I get five, I’ll say, okay the copy was not good enough, so I need to do something.

Sean Murray [00:12:42] Yeah, when you when we did this exercise one of you said this earlier One of the things stuck out to the group of four that I was working with is The first assumption that we actually had to wrestle with is that like we all thought the idea was a great idea Right and that if we liked it then obviously everyone else without today that’s ever been had. Yeah, right Yeah So I just I love the way that that exercise helped us kind of uncover some of the bias that we have about Ideas that we create and how you can get to attach them

Dan Sharp [00:13:09] So throughout the talks today and it came up yesterday we can talk about AI and the need for human thought You know yesterday you mentioned a technique and how you prompt where maybe you’re asked for assumptions But one of the skills you’ve put in is hey ask me to put my stuff in first I would love to hear you expand upon how you came to that as a working model.

David Pereira [00:13:32] So it’s an interesting thing. There are some people, I respect them a lot. And one of these people, one person told me the following, David, I’m looking for a new job and I am failing in every single interview. And I said, what has happened? Tell me more about it. She said, I don’t know. I understand now everything about skills, evals, agents, orchestration. And I explain all of that in the interview and then they fail me. And then I started looking at that. And said, walk me through. And what I realized that this person who is super authentic, she was not her anymore. She was just a toolbox. I said, what I am missing is who are you? And once I heard that, I started observing what people are doing. They are figuring out how to automate things so agents can run things flowlessly, end to end. And then what happens is throughout the way, agents will make some assumptions and then you will not check. And you will get to some results in the end you don’t understand. So what I wanted is like decision points. I wanted you to, you know, you can’t get muscles without exercising. So I said you need to warm up your brain, do some exercise, so once you see the results, you are already warmed up so you can criticize. So for example, instead of somehow asking AI to come up with assumptions, you first need to, I force, you first come up with a few assumptions, and then AI will amplify and then you are going to decide based on what you see, which are the ones that make sense then you advance. So decision point. And this gets through another thing. It’s not always faster, but it can get better.

Sean Murray [00:15:12] Okay, how do we how do people when they’re looking at implementing an agentic system? How can they maybe understand? Where AI is gonna have license to make those assumptions like where do they find like actually this is a weak point and Like it’s gonna make a bunch of assumptions here if I don’t actually Articulate what I’m looking for at this step

David Pereira [00:15:35] AI is going to make assumptions all over the place because you give a task and AI will complete the task. So many people go from idea to prototype, but from idea to prototype there are some things in between. There is like, what are the assumptions? What are the ones we should test? What do we know? What don’t we know. So we need to slow down. So what I want is to break whatever you’re trying to achieve into smaller steps with outcomes. And then you have the checks. You can use agents for that, make it transparent. For example, you can integrate with Miro and say, first I want to see this, then I will give the thumbs up. Then you move to the next part and to the part. But you have a chance of making it transparent because one of the things that AI will do if you don’t intervene is will amplify misalignment because it will be silo work, not everyone will see what is going on. But if you make it transparent, then everyone has a chance of seeing what is going on step by step, and then you can just find why are you taking this path instead of another.

Dan Sharp [00:16:43] You hit upon something really interesting, right? And you had a dead year of silo-ization and AI amplifying, right. And if you’re prone to working by yourself, now you have AI and now why ever speak to anybody? I mean, I’m curious, you know, what, how you kind of break that pattern and kind of force yourself to go and collaborate when it’s like, Hey, I can just talk to Claude and hear I’m done. No.

David Pereira [00:17:05] In each of first step. So if you want to do this, for example, I understand that the teams are gonna change the size because I imagine in the future, every person in the team will have a series of agencies executing things. So if execution becomes cheap, where do you put your energy? Your energy goes to thinking, decision making. But thinking and decision making should be a collaborative exercise, not a silo one. So for example with the team, you agree where you are going. And even say, we have decision points. So let’s align on this. And how you align can be different, can be asynchronous, or can be meeting, as I mentioned, 15 minutes to decide, say we have this output, we have three different paths to take, which is the one, and why, or maybe we are gonna come up with a farfaw. So you force these decision parts collaboratively.

Dan Sharp [00:17:56] All the times I feel like I’ve had a brilliant idea, I’ll go talk to somebody else and they’re like, yeah, no, that was obvious. I mean, it’s like, ah, great.

Sean Murray [00:18:03] You told a great story that I am hoping that you’ll recount for everybody, but we talked about kind of like that bias of like, our idea is good. We’ve made the decision and we don’t want to hear anything else. And you took the opportunity when you were working for a famous car manufacturer to actually challenge that assumption. Can you regale the crowd with that story?

David Pereira [00:18:26] Not sure if the crowd know this car manufacturing. Let’s check. Do you know Bayardish Motorwagen? BMW. So what happened is like, I was working with them and they asked, how do we build the best navigation system in the world? And they had a lot of money to do that. And I was trying to understand like what they tried to optimize and why they had to build that. And I asked him, like, how do I talk to customers? I want to talk to a customer to understand what is going on in their mind so we can optimize for their needs. And they told me I cannot talk to the customers. No, compliance reasons, you cannot talk them. Oh, but I live in Munich, Germany, so I said, every second person here has a BMW. I’m gonna walk on the street and the first person that parks the car, I approach. And then I would ask, like if you’re gonna go from A to B, how did you choose to go there? 9 out of 10 answers were like, either Apple Car or Android Auto. And then I got one person saying, I use the BMW navigation. And I said, OK, how do you like it? I hate everything about it. And the only reason I use that is because BMW is charging a subscription for Apple Car. I refuse to pay. And then, I look at this. I talked to a few more people, but the pattern was clear. I went back to Bim Davos at It didn’t allow me to talk to your customers. I talked to BMW car owners who live in Munich, and here’s what I learned. And BMW was looking at me and said, what are you saying? I said, I am saying that people use their phones for navigation and they like that. What they don’t like is plugging the cable because plugging a cable adds a lot of friction. Sometimes they break the cable or something like this. He said, if there would be a different way remove this friction, it would be better than building the best navigation system in the road. And BMW said, alright, let’s explore this. That’s when they started building a frictionless way that you just enter in the car and it’s connected automatically, you don’t have to do anything. They still invested in the navigation at all, but they prioritized up on first.

Sean Murray [00:20:39] I love that story, I love just taking it upon yourself to just say, well, I have to go talk to customers, right? I feel like you got through a little bit of bullshit management there.

David Pereira [00:20:49] All right, a lot, a load’s off. So there’s one thing I learned earlier in my career is about one of the best friends of a product people I mentioned yesterday, Nihito. Some of you probably know Nihito by now. If you don’t know Ninhito is nothing important happens in the office. So I learned about it and I was like, this, I always look at home companies, how are they making decisions about what they are doing? And many times decisions are inside out. So inside an office room, and so on, and when I lived in Brazil, I remember once I was developing an app for food delivery, and I was checking, I said, if you really want to understand how these things happen, you need to be on the move. So Brazilians always do things on the moves, thinking, when I arrive home, I want my food to arrive with me. So they are going to order that when they are in the subway or something like this. So I started testing the app when I was inside the subway. Like if the metro would fit like 100 people, there were 300. And I was trying to do this and so on. And it wouldn’t work. And I will tell you, this app needs to work very well with one hand. And I need to see what is important when I have a screen like this. You need to go and check reality. Because if you do in the office, you have a lot of space. You are sitting there, very cozy, very calm and so on, but that’s not how people order. You need to understand reality. And that’s what I thought about BMW, it’s the same thing. How do people choose what is reality? What is the real fridge and not what is happening inside the office?

Sean Murray [00:22:28] David, you are an amazing storyteller. Thank you for the opportunity to use a little casual profanity on the podcast. They normally don’t let me do that. This one’s getting to me. Yeah, right. Thank you so much for being with us today.

Dan Sharp [00:22:40] Thanks for coming and hanging out with us.

Sean Murray [00:22:41] All right,.

David Pereira [00:22:42] My pleasure. Thank you.