Goal-setting has become a religion we no longer question - but Radhika Dutt argues that OKRs, KPIs and targets are duct tape over foundational cracks. Even the celebrated examples fray under scrutiny, and half-century-old laws from Goodhart and Campbell predicted why: the moment a measure becomes a target, people game it, quietly piling up "vision debt."
Her alternative is OHLs - objectives, hypotheses and learnings - metrics used for collaborative learning rather than end-of-year exams. She shows how a genuinely radical vision statement replaces fluffy mission-speak, and how one company traded OKRs for three questions - how well did it work, what did we learn, what will we try next - to give teams more ownership and sharper decisions.
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Radically rethinking metrics. So, what I have to say today is not just radical, it's downright blasphemous. Because goal setting has become a religion. Right? Whether it's goal setting OKRs, we don't question if goal setting actually works. We take it on faith. This is how we build businesses.
And so, here is my radical but more blasphemous thought, which is goal setting methodologies, including OKRs. And if you're lucky enough to not know what OKRs are, objectives and key results. These approaches don't work. They work about as well as duct tape on foundational cracks.
And we need a radical rethink. So what I'm saying is, regardless of which approach you're using for goal setting, whether it's SMART goals, targets, KPIs, OKRs, whatever you want to call it, it's in the same bucket. These approaches suck. And what I'm advocating for is a new approach, something I call objectives, hypotheses, learnings.
So instead of OKRs, OHLs. Now, at this point by the way, OHLs is the topic of my next book coming up in twenty twenty six. But you might say, why do we need to move to OHLs? In fact, there is a bestseller out there called Measure What Matters, written by famous billionaire VC, John Doerr, and he talks about how Google, Bono, and the Gates Foundation have rocked the world with OKRs.
So, why do we need something else? Well, I want to look at these examples of Google and the Gates Foundation, and let's actually see how these examples have played out over time. Let's start with Google. John Doerr, in a TED Talk, he talks about Google Chrome as an example.
And he talks about how Sundar Pichai, when he was leading Google Chrome, he set an OKR. It was a three year objective, build the best web browser. And the first year, he set a key result of get to twenty million users. And John Doerr says he got less than ten.
The second year, he said, get to fifty million users. And they got to thirty seven million. And the third year, Jaundra says, he said, get to a hundred million users and kaboom, he got to a hundred and eleven million users. Well, kaboom indeed because in twenty sixteen, the European Commission fined Google a whopping four point one two five billion euros, its biggest fine to date for anticompetitive behavior.
Google achieved that key result not because they built the best browser, but because they took a short term approach to achieve the key result that had been set. They used their market cloud to force mobile manufacturers to preinstall Google Chrome. Well, so that's how the Google Chrome example played out.
How about the Gates Foundation? John Doerr talks about their objective of global eradication of malaria by two thousand and forty as a fantastic objective, so aspirational. Right? And I won't talk about all of these key results. I'll just talk about this first one.
Prove that radical cure based approaches can lead to regional elimination. Radical cure based approaches. How did that play out? It's led to an aggressive pursuit of dubious solutions. In fact, Doctor. Arata Kochi, who was the former chief of malaria for the WHO, he said that it's led to a cartel of malaria scientists and it's been stifling debate.
Why? Because sometimes instead of radical cure based approaches, simple preventative approaches are the way to go. In The Lancet Journal, a renowned medical journal, the editors wrote a scathing article saying that the Gates Foundation was creating perverse incentives for politicians, policy makers, etcetera.
Why? Because so often resources were being diverted to fight malaria when in many of these developing countries it wasn't the leading cause of death. It was often gastrointestinal issues or sometimes even road accidents. Now, this is how, you know, Google and Gates Foundation's, their OKRs have played out, right?
And these examples are not talked about publicly. So now that you know these examples, I want you to do a checkup. Tell me if you've experienced some of these examples of goals and OKRs going wrong in your organization. So this first one. When I work with product teams, very often product teams say to me, can can you tell me how do we frame these metrics so that we can show our management that this is working?
Right? How do we spin these metrics? How many of us have wanted to do that? Thank you. Thank you for sharing. Right? Another thing I've observed is that OKRs often limit innovation. There was a product manager I was talking to, and he told me very proudly, OKRs are great.
They give me so much clarity. I just told my team, if it doesn't move an OKR, don't do it. How many of us have experienced this? And it's not helpful. To this, by the way, a lot of people say, Oh, that just means you're setting the wrong OKRs.
Right? You just need to set OKRs more often. And in fact, I heard an OKR expert say this to an executive in a large organization, and this executive just laughed out loud. He said, okay, I know we're stuck with the goals we set in January, but we would just die if we had to set these goals many times over in a year because it takes so long.
How many of us have experienced this? Thank you. Thank you for sharing. And this last one. This is probably my favorite because I've seen this in every single company that I've worked at. How many of us have seen that at the end of the quarter, you have salespeople scrambling about saying, how do we pull in revenue from the next quarter?
Right? This works until it doesn't. But I bet until now, when you saw these symptoms of goals and OKRs not working, you thought to yourself, It's probably just us. You know, we're just not doing OKRs right or goal setting right. And this is what you're usually told. Right?
But I'm here to tell you, it's not you. In fact, we could have predicted this. We've known this for half a century. In economics, there's Goodhart's Law that says when a measure becomes a target, it ceases to be a good measure. Nineteen seventy five.
We have known this in anthropology. Nineteen seventy six, Campbell's Law that says, the more a given metric is used to evaluate performance, the more likely it is to be gamed, and the less reliable it becomes as a measure of success. We've known this for half a century.
We could have predicted it, but we haven't absorbed this in business. We still think goal setting works. So now that you know these laws of metrics, I want to show you what they actually do. What targets and OKRs do when you have this yin and yang balance of long term versus short term.
Right? Vision versus survival. In the first book I wrote, Radical Product Thinking, I help you visualize this yin and yang as an x versus y axis of vision versus survival. And, the quadrants that emerge help you just visualize how you're balancing priorities across the yin and yang.
So, things that are good for vision and survival, those are the easy decisions, right? But if we're always just focusing on those easy decisions, then we're being myopic sometimes. Sometimes, we have to invest in the vision. That's when you're doing things for the long term, but it's not helpful in the short term, like doing user research, paying off technical debt.
And the opposite of that is when you take on vision debt. That's when you're doing things good for the short term, but it's bad in the long term. Right? Now, when you're balancing these quadrants, you want to do more things in the easy quadrant.
You want to do as much as you can in investing in the vision. You want to do You want to avoid vision debt as much as you can, and you really, really, really want to avoid the danger quadrant. Right? So what do OKRs do to this balance?
Want to give you the example of a self driving car company. They had the objective, increase the speed of our development. Sounds wonderfully long term. Right? And they said, to be able to increase the speed of our development, we need to get better test coverage.
So they set the key goal as key result is achieve ninety percent test coverage for COVID. By the end of the year, the team achieved it. But how did they do it? They wrote a bunch of bogus test cases so that they could get to the ninety percent mark.
So in fact, they actually achieved this key result by taking on a bunch of vision debt. I'll give you another example. This is a company that I was working at where the objective was increase customer delight. And it sounds wonderfully long term. And the CEO had been getting a bunch of calls from angry customers who had made a big investment in our product and they were running into stability issues.
And so the CEO set this key result, address urgent customers' shoes on the same day. And you think, yeah, you know, we're really investing in customers. Right? It's longer term. It instantly turned everyone in the company into a firefighter. Everyone wanted to be the savior for the customer and wanted to be seen as the savior, and nobody was working on the long term stuff that was under the radar that actually made the product better in the long run and that made it more stable.
And I'll give you one last example. This one was at a B2B company that I was at, and the objective was grow the business. Sounds like an easy decision. Good for long term and the short term. Right? And our key result was grow ARR to x million by the end of the year.
And this is where the salesperson came to me and said, you know what? We can win this big deal if we just add this custom feature. And we said, you know what? We're all in the same boat. We wanna achieve this key result.
So we took on a bunch of vision debt. What happens with OKRs is that they lead to a stinking pile of vision debt year after year. And so what Goodhart and Campbell were really saying was, Don't set targets because otherwise you're giving high marks for crap.
And people, that's just soul sucking. But at this point, you might say, okay, now you've told me what not to do, but what should I be doing instead? And this is where I want to introduce you to law. People, there's a special place in hell for those who name laws after themselves.
But let's make this a thing. Let's put Dutz law on social media. If Goodhart and Campbell can do it, this is That's Law. Metrics are only effective if they're used towards collaborative learning. Metrics are only useful if we're using them to learn together.
And this is such an important point, and it took me a long time to realize this. But now the skeptics amongst us, right, we're going to say, yeah, that law sounds great, but don't we need goals and OKRs to define the impact that I want to create, to align teams on which metrics are important, and to create accountability, because I want to know, are you or aren't you achieving your goal?
And to this I'm going to tell you, yes, we do want to define the impact, we do want to create this alignment on metrics, and we do want to create accountability, but goals and OKRs are not the way to do it. Why? Because if you're feeling like you're lacking that clarity around the impact you want to create, it's because most vision statements are absolutely useless.
Let me give you examples. So, here's an example of a vision statement. Aspire to be the best in aerospace and an enduring global industrial champion. This is Boeing's vision. Right? What does this even mean? Right? Is the is being the best about revenues, market share?
Here's another one. Contributing to human progress by empowering people to express themselves. This is Snapchat's vision. And we learn from such vision statements, and here's an example of a vision statement at a company that I worked at, Reinvent Warehousing. And by the way, I have made this mistake myself.
In fact, the first company that I founded, our vision was revolutionizing wireless. Twenty five years later, I still don't know what that meant. And when we have these foundational cracks, this is where we feel tempted to take the easy solution and we want to put a duct tape of OKRs and goals on it.
Right? Does this does this feel like it resonates? Are you experiencing this? And so, the problem with this patch is that it doesn't work. This is not just me saying it. In fact, James Clear, who wrote Atomic Habit says, every Olympian wants to win the gold medal.
What separates the winners from the losers is not goals. It's about having a system, and it's about committing to a process that matters. And so, this is where I want to introduce you to the first book that I wrote, and that's radical product thinking.
And, it's a methodology for building world changing products, and it gives you a systematic and a step by step process. Now, won't get into all of the elements of radical product thinking. I just want to give you a quick preview of what it means to write a good vision.
So that you never ever have to write a vision statement like that again. In the radical product thinking way, a good vision statement is a fill in the blank statement where you don't have to focus on the words. You really can just focus on the profound questions that you need to answer in a good vision.
So, let me give you an example of this vision statement filled out, and this was for a start up that I had founded in two thousand and eleven, and I sold it in two thousand and fourteen. Today, when amateur wine drinkers want to find wines that they're likely to like and learn about wine along the way, they have to find attractive looking wine labels or find wines that are on sale.
We this is unacceptable. This is unacceptable because it leads to so many disappointments, and it's hard to learn about wine in this way. We envision a world where finding wines you like is as easy as finding movies you like on Netflix. We're bringing about this world through a recommendations algorithm that matches wines to your taste, and an operational setup that delivers these wines to your door.
Now, this is why this is a radical vision statement. I hadn't told you anything about my startup. Right? But hopefully, after I shared this vision, you knew exactly what we were doing and why we were doing it. And so this is how we define the impact that we want to create.
By answering these really profound questions of who, what, why, when and how. Okay. So at this point, let's get back to the skepticism. Right? Because you can say, great, we've done step number one. We've defined the impact we want to create. But how about these?
I still need to align teams on which metrics are important. I need to create accountability. How do we do that? Let me illustrate that with the example of SIGNAL. So SIGNAL is a company in the maritime industry that I've been working with for about two years now.
And the best way to describe what SIGNAL does is they're a data platform that helps all the parties in the maritime industry make the best decisions in terms of how do I match shipping vessels to cargoes. So, when I started working with SIGNAL, the CEO had introduced OKRs so that teams could measure success and be more rigorous about measurement.
And the teams were using, you know, OKRs in a very typical way. You know, key results included what you would expect. Things like increasing ARR to x million, increasing weekly active users by x percentage, you know, targets on various user engagement stats. And you know, I had talked to the CEO about a year ago to say, you know, here are the problems with OKRs.
Let's not use OKRs. But honestly, I wasn't getting anywhere. Right? And what I was observing though was that there really were a lot of negative side effects with using these OKRs. And I was seeing that, you know, in terms of what was happening, it was triggering this left brain focus, or in more scientific terms, it was triggering the attention network.
And so, the way it was manifesting itself was when I would ask the team sometimes, how's the product doing? Or, tell me how this particular feature is doing? You know, very often they would talk to me in stats, in numbers. They would tell me weekly active users is blah, or weekly growth, sorry, the growth of weekly active users is blah, you know, I've talked to x number of clients.
Right? It was all numbers that were coming back to me. And it reminded me of The Little Prince, one of my favorite books, where the author says, Grown ups really like numbers. Right? But it wasn't telling me the story. And so I realized that we needed to shift the mindset.
So two months ago, just two months ago, I introduced this OHL's approach to the team, to be able to introduce this puzzle solving mentality. And I want to give you just one example of how we use this approach. So out of the entire data platform, I'll focus on just one example where we use this, and that was for their mobile app.
What we did was we defined a problem statement. So we said, you know, it's interesting that many of our customers, they're using only the desktop app, but not our mobile app. And then we wrote some guiding questions. We said, why might that be?
Do they know about our mobile app? Maybe are we not giving them the right functionality? How might we increase adoption? How might we increase stickiness? And then we defined the objective, which was that we really want to make Signal the platform that our users think about even when they're on the go.
And what I found was just framing this as a puzzle that we want to figure out together was triggering the imagination network, or what we think about as the right brain. So, now that we have set the puzzle, I needed to build the muscle memory in the team. Right?
Building the muscle memory for actually solving these puzzles. And to do that, we introduced this OHL approach of structuring hypotheses and learnings. And the way we did that was through three questions. So the first question is, how well does it work? How well does it work? What did we learn?
And the third one is, what will we try next? So let's start with this question of how well did it work? This is where we define a hypothesis. And we define it in the form of, if we do this experiment, then here's what we expect as the outcome because here's the connection.
And then we can test this hypothesis with leading and lagging indicators. Once you've defined the hypothesis, that's when we answer this next question by trying out the experiment. What did we learn? And this is where I say to the team, don't just give me stats.
Don't give me just the numbers. Tell me the story. Weave for me the narrative around those metrics. What are those metrics teaching you? And once you've reflected on that, then this is the most powerful question. The question I ask the team is, now that you've reflected on the learnings, imagine I give you a magic wand.
What I want from you is the level of clarity where if I were to give you a magic wand, you know exactly what to ask for. And what I found was this was giving people the task focus by asking how well did it work because we were focusing on the numbers and hypotheses.
And then the other two questions were creating this creative network or triggering this creative network. Right? And so let's talk about what did they come back with. And I want to focus on the mobile landing page. So the product manager came back with a hypothesis.
She said, if we give users the ability to open their desktop vessel list on the go, then they're more likely to use the mobile app to access their lists. Because this way it's much easier to carry around just the mobile app instead of having to lug around your laptop.
And then, she defined leading indicators, which was what's the percentage of users who are adopting the app, and what's the percentage of users who are using or opening a list? Now, this is where she said, well, how well is it working? Well, so it turns out that some personas are indeed using the vessel list.
But it turns out that a lot of the personas who are coming to this mobile app are coming to it with very different questions in mind. And so that brings us to this next question. What did we learn? So instead of spitting out a bunch of stats at me, she looked at all of the data and she came up with a narrative.
She came up with a list of questions that the users seemed to have in mind when they were coming to the app. And I want to reveal all of the questions, but I'll give you number three, which was, when will my vessel arrive at a particular port?
So, the data was telling her kind of what was it that people had in mind. And that brings us to the next question, which was, If I give you a magic wand, what would you ask for? And she was This is where I could really see this moment in her.
Right? Because she came back with this icon of, If you gave me magic, this is what I would ask for. This was an entire wireframe that she thought of, that this is what I would try next. This is the foundation for the conversation with your designer.
This triggers that next set of questions about, okay, what have we learned? Then jointly, you can start to collaborate on what are we going to do next. This was building up muscle memory for how we review past features. And it wasn't just the mobile app.
We were doing this across the whole product team. And by talking about features, both the good and the bad metrics, we were creating a culture of collaborative learning. And once we had built this muscle memory in the product team, we started using this elsewhere in the organization.
We started using it in monthly business reviews. We were even using it in the design sprint where, when we had a five day design sprint, we started to use this on at the end of every day, so that we could capture learnings from the day and reflect on what we were going to do next.
And we started seeing that things were changing. Right? The customer interviews sounded different. Two years ago, when I'd started, customers were polite. They said, You guys are asking interesting questions. A year ago, it started changing. We like talking to you because you guys seem to understand what we want and you're making changes.
My favorite was the last design sprint where it started with, I love you, Mike. Our standing in the market started changing. Two years ago, it started with the product isn't quite there yet. About a year ago, we were hearing, your product allows me to do in two just in two clicks what would have taken me fifteen minutes.
But in the most recent meetings, we were hearing, your product is becoming the standard in the industry. But probably what was the most satisfying for me is when I asked the CEO, So what do you think of OHL's versus OKR? And by the way, I told him that I was going be talking about just this concept at this conference, and I said, you know, I don't have to quote you on it.
But he said, Oh, please, feel free to quote me. He said, what I found is OHLs give you ears on the ground, that they help you anticipate what's coming and they make measurement more actionable. And what was amazing for me is he didn't just say this as a positive for OHL's, he talked about OKRs in the past tense.
He said, in contrast, I see now that OKRs were the view from the rearview mirror. The moment for me though was what he observed in the team in just the span of two months. That's how long ago I'd introduced this. And what he said was he saw that teams were being were taking on more ownership.
They had reflected on learnings. They had more conviction in their decisions, and they were making better decisions. And so, you had seen Goodhart's Law and Campbell's Law. Now I've really solidified my place in hell by putting up my picture with Dutz Law. But let's make this a thing in social media.
So Dutz Law is if you can't avoid targets, at least you can make your life less soul sucking by introducing this puzzle solving mentality at a grassroots level. So today, you're using OKRs to compensate for vague and fluffy vision statements. Instead, can write a radical vision statement.
Today you are aligning teams on metrics to prove progress, and instead you can align teams on how to test your strategy and improve it. Until now, we've created accountability through end of the year exams, and instead you really want to create accountability through collaborative learning.
And one final slide, just so that you have your final takeaways all in one slide, you can bring this mindset at a grassroots level by defining the puzzle, by identifying hypotheses, and then by structuring learnings by asking three questions, saying, how well is it working?
What have we learned? And what will we do next? And this is how you can put laws in Dutz's law into action. So, in terms of next steps, you can keep a lookout for the Radically Rethinking Metrics book, which comes out next year in twenty twenty six.
And until then, you can check out the first book, which was Radical Product Thinking. You can get the book, you can ask me about workshops, and you can get the free toolkit on radicalproduct dot com. And also, I always love to hear how you're creating change in the world.
So please feel free to message me if you want to vent about OKRs and goals. You're welcome to do that too. Thank you.
