Incongruent

Breaking Free from Performance Traps - Radhika Dutt

• The Incongruables • Season 5 • Episode 2

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Radhika Dutt challenges traditional goal-setting frameworks and introduces OHLs (Objectives, Hypotheses, and Learnings) as a more effective alternative for today's knowledge workers who are solving complex puzzles rather than performing repetitive tasks.

• Goals and targets originated in the 1940s for unskilled, unionized workers doing repetitive tasks
• Today's workforce needs a puzzle-solving mindset rather than target-hitting approach
• AI accelerates "enshittification" by optimizing metrics while hiding negative consequences
• OHLs framework asks: How well is it working? What have we learned? What are we doing next?
• Even middle managers can start implementing OHLs by reframing their own work as puzzle-solving
• Successful organizations create cultures of psychological safety where honest discussions about successes and failures can occur
• Intel's success came from Andy Grove's "paranoid obsession with never getting complacent," not just OKRs
• The OHLs template is available at radicalproduct.com

Radhika invites listeners to reach out to her on LinkedIn and share their experiences implementing OHLs, as she's currently writing her second book and looking for case studies.


Aayushi
Hello and welcome to the new season of Inconquering. I'm Ayushi and I'm delighted to be here on this amazing episode where we welcome a real innovator. So today we have with us Radhika Dutt. She's the author of Radical Product Thinking, the new mindset for innovating smarter, which is now used in over 40 countries. She's an entrepreneur, speaker and product leader who has participated in five acquisitions.

She is currently advisor on product thinking to the Monetary Authority of Singapore. Radhika has built products in a wide range of industries including broadcast, media and entertainment, telecom, advertising technologies, government, consumer apps, robotics and even wine. She graduated from MIT with an S.B. and Masters in Electrical Engineering and speaks nine languages. Radhika is now working on her second book.

escaping the performance trap, why goals and targets backfire and what actually works. So before we get right into the questions about the book, I'm sure the listeners would like to know a lot more about you, Radhika. I read that you can speak nine languages and that you are the jolt that stirs you into action, offering bold insights, double shot impact and espresso level energy.

So can you share a little bit about your journey to date?

Radhika Dutt
The journey started with me being an engineer and, and you know, I had built startups and what I discovered over time was irrespective of what industry I was working in or what size of company, I was seeing the same set of patterns and I called them product diseases. And I'll give you a couple of examples of product diseases, things like obsessive sales disorder or pivotitis and strategic swelling, where we want to do everything for everyone, for example, and pivotitis is when we're always pursuing the next shiny object. You know, there were a bunch of these product diseases that I kept seeing over and over. And the big burning question for me at some point was, is it that we're all doomed to learning from failure? Or can we actually have a systematic step by step process for how do you build good products?

And so that burning question led me to writing Radical Product Thinking. And the second book that I'm working on was a result of writing Radical Product Thinking. So I wrote Radical Product Thinking and that offered people a step-by-step process for how do you build transformative products that really create change. But people would often ask me, well, you know, I want to apply these ideas that you have, of having clarity of vision, not just throwing things at the wall and seeing what sticks. But in my organization, we have goals and targets. So, you know, we have all these short term deliverables. How do I apply these in this in such a world? And so that's what led me to this idea that, you know, for a long time, I'd been seeing the issues with golden targets. And it led me to this question of what do we do differently? And how do I explain this properly?

And what's the alternative, right? And so that's what led me to the second book. So that's been my journey.

Aayushi
That's wonderful. That's really nice to hear. So, your first book has been adopted globally, has been like widely adopted globally. And you said that your second book, your first book kind of laid the foundations for your second book. Can you tell us more about that experience?

Radhika Dutt
Yeah, and maybe I'll touch on some of the elements of it too. you know, should we talk about AI too along the way, like since you wanted to cover some of that?

Stephen King
Go ahead more than happy to hear as much AI as you can

Radhika Dutt
Okay, well, so in radical product thinking, I talk about how we don't just disrupt for the sake of it. You know, like, don't just disrupt because, you want to change the status quo. Like, why do you need to change the status quo? This is what I often found out of Silicon Valley. We don't question what's wrong with the status quo. Without defining the problem, we go forth and just say, yeah, we'll disrupt, right? And then we create things that... that affects society and then we say later, whoops, you know, that was an unintended consequence. You know, it's just partly we don't think through the details. We don't have clarity on the end state we want to bring about. And so radical product thinking was about, you know, don't just tell me your vision is being the leader and blah, blah, or empowering people to change the world. Like, you know, bullshit visions like that. 

Instead, describe for me what is the problem statement, who has this problem, why does it need to be solved? Because frankly, maybe it doesn't. Then you describe what's the end state when you've solved this problem. And then finally, this is finally where you get to talk about your product, is how will you bring this about with your product, right? And so that was what radical product thinking was about, how to write such a detailed vision.

And then not just write such a detailed vision, but very systematically translate this vision into reality. Not by throwing things at the wall, but thinking through strategy more comprehensively. You know, thinking about strategy that's grounded in pain points, how are you going to solve it, etc. The way AI fits into this, right, is I see the same pattern with AI. You know, open AI threw out chat GPT and it created this arms race in terms of AI.

And we've thrown something out there without thinking through what is the end state that we want. You know, I remember there was this New York Times interview of Sam Altman and he was asked, so what's, you know, you released ChatGPD a year ago, you know, what are your reflections? And his answer shocked me. He said, oh, you know, it's been so busy. I haven't had time for reflection. Right? I can see the shock.

Aayushi
Yeah.

Radhika Dutt
And then he was asked, so what's your vision in five years? And his answer was just this incongruent set of sentences about how everything is gonna be fine. Everyone will be happily employed. It's not a problem. Everything is gonna be great. You're like, I am not, your answer is not convincing me. This was a podcast on New York Times. And it was really shocking for me to see that. And this is the kind of thing that I think we need to stop doing where we just,

Aayushi
Yeah.

Radhika Dutt
throw stuff, disrupt for the sake of it. We have to be deliberate about what is the vision we have and how are we bringing about that change. Very deliberately, so we don't just say, oh, whoops.

Stephen King
I think that's very interesting. makes me think of the wolf, the boy who cried wolf.

Because there are certain technologies which people talk about they do technologists do this quite a bit, know, they find something it's shiny they think it's great and then they launch it to the world and Everyone is supposed to buy into the hype behind it and PR people are very much responsible for pushing the hype and making it so exciting because we benefit from the advertising sales and from all of the promotional marketing budgets, but then it doesn't materialize and then it makes people

Radhika Dutt
Mm-hmm.

Stephen King
suspicious about the next hype cycle and the next hype cycle until the point where we have something like generative AI which does have some impacts in particular industries but those industries or those parts of the world say hang on this is just another hype and therefore that they miss the fact that the wolf is at the door. Do you get what I'm saying? So I'm really...

Radhika Dutt
Haha, yeah, absolutely. But you know, in this one case, I agree with the hype cycle, but in this case, I don't think AI is necessarily hype in the sense that it's going to have a huge impact. I do think it's a huge impact. I think the hype you're referring to is that we think it is really capable. And what it is leading to is what Corey Doctorow calls entratification. I'll explain what I mean, right? So,

We think AI is super capable and therefore we use AI for customer service. And if you look at kind of just yesterday or the day before, I was trying to reach customer service. And I went through this AI loop cycle where, you know, they were trying to answer my question. It was a half-assed answer. I'm trying to find a human to talk to because that problem is not going to solve by AI.

I had a really bad customer service experience, the company saved money by reducing their customer service workforce. So let's talk about what I mean by this end shitification. Cory Doctor, he talks about end shitification as this three-step pattern that leads all platforms to shit. So the first step is you lock in value with users by giving them, or you lock in users by giving them value.

The second step is I take value from users and I give it to business users. And step three is I screw over both the consumers and the business users to give value to shareholders. And it works until it doesn't. So an example is Facebook, right? At first they were giving value to users. Then they sucked everyone's data and started, you know, relentless advertising, sharing data, et cetera, and giving value to business users.

Radhika Dutt
And step three has been sucking value out of both to give value to shareholders. And so this is not just Facebook, it's Unity for gaming. Like take any platform and it tends to go to shit as you optimize for numbers. And so where does AI come in? Yes, AI I think is good at optimizing for numbers. It makes numbers look good. Like it makes you think, I can fire my customer service people so that I reduce costs. Yes, you do reduce costs, but what has happened is in the process of creating less costs for yourself, you've created a shittier user experience. And it works until people finally decide, I cannot take your product anymore. That's it. know, finally there's a network effect and you can move on. And what AI is going to do, and this is the part that I don't think is hype, is it is going to... speed up that ancientification cycle. How quickly platforms go to shit.

Stephen King
It's very clear and very obvious and unfortunately with the new because of the customer service they actually improve their measurements that they are recording because it's preventing the number of people that get through to humans and reducing the amount of time with the humans right that's effective and so from the person who's counting the beans point of view this is actually a good result they don't see the pain and frustration that the customers have and moving forward I know you experienced that

Radhika Dutt
Exactly.

Stephen King
Let's move forward with one of these questions here. You've pretty much told us why goals and targets backfire I believe with the objectives and key results and you're quite restricted there but we've been told that

We've always been told and it's in many of our teaching is that if you can't measure it, you can't manage it. Right. So how do we measure to get progress and results?

Radhika Dutt
Yeah. Yeah, so one of the things that I have realized while writing this book is we have to even look at why do we ask this question about measuring progress in this way? Like why goals and targets to measure progress, right? So what we see is the idea of goal setting is a way of measuring people measuring performance, et cetera, came all the way from the 1940s with Peter Drucker coming up with MBOs or management by objectives. And then the same idea of MBOs was repackaged and relabeled. Andy Grove called it OKRs. And John Doerr later promoted that. You know, the same ideas behind KPIs and setting targets for KPIs, et cetera. Same name, but nothing has changed in terms of core concepts.

And why did it work in the 1940s, right? It worked because it was a different workforce. It was people who are unskilled, unionized, working on repetitive tasks with no automation. And so I can tell that Andy's a better performer than Bob. He installed 40 tires and Bob did 35, right? There was one way of installing tires. There's the right way and anything else is the wrong way. So it's easy to measure in this way.

Whereas what we're doing today, the workforce is different. The problems are much harder. What you're facing is a workforce that is working on solving puzzles. And what we don't account for in Goals and Targets is how do you do puzzle setting and puzzle solving? And that's what I talk about in this book. How do you do puzzle setting and puzzle solving instead?

Aayushi
That's very intriguing. It's like opening up a new perspective. yeah, so now I would like to move on to a...

about AI. So the AI revolution means there's greater access to a greater quantity of data from wider variety of sources than ever before. And this is presented in like an accessible fashion on demand. Do you see this as an obstacle for overcoming OKR culture?

Radhika Dutt
think this access and the ease, right? It just feeds into our idea that, this is easy and we miss the information underneath. So what happens with OKRs naturally is we want to show numbers that look good. so when you set a target for me, I want to show you, look, ta-da, I met whatever numbers that you set for me. I'm a great performer because I've hit my numbers. So going back to that example of customer service, look, you wanted lower cost customer service, I achieved it, ta-da. What I'm not showing you is all the bad numbers, how customers are pissed off at me, how they're pissed off at the organization. But I don't want to tell you about that because that'll just make me look bad. So that's what happens, right? And with AI, what's going to happen is it looks easy. It hides all those bad numbers really well. So we talked about the example of customer service already, but it can be a lot of other

I want to give an example from user experience. know, AI can help you design user experience, right? And I've seen a lot of people use it. And it is just good enough that if you're not a designer, you look at that and say, yeah, this works, except it's going to create a slightly shittier experience than what you could have had, right? But it looks good enough that you would think that, I don't need a designer to do this. And so...

The more, so the way AI feeds into this whole OKR thinking is it helps you hit whatever numbers you're trying to, but what you lose with OKRs is this puzzle solving mindset. haven't, you're abdicating learning essentially to AI is what happens, right? What you need people to do is to ask three questions. And that's what I talk about in this.

in this new model of what we should do instead of OKRs. What I talk about is OHLs or Objectives, Hypotheses and Learnings. And it means asking three questions, which is how well is it working? What have we learned and what are we doing next? So that is what you need to ask. Like, how well is it working? What have we learned? That learning part. Instead, when everything looks so easy with AI, we abdicate that learning to AI. Just tell me what to optimize and I'll do that.

Stephen King
Well that's good. I mean you've described this alternative there, objectives, hypotheses and learnings or else, but the argument whenever you counter the norm is does it mean you're either being more expensive or is it going to be less rigorous? What are the criticisms that you overcome or the obstacles to taking on your point of view and how do you argue against them?

Radhika Dutt
Yeah. You know, when you tell leaders why OKRs or goals and targets don't work, that's exactly the concern. You know, how do I know that my team knows how to be rigorous about measurement? Because objectives, hypotheses and learnings, when I mention these three questions, how well is it working? What have we learned? What are you going to do next? It makes you think that you can be sort of just callous about measurement, right? But the reality is

it's a lot more rigorous in terms of getting you to think critically about what's working. So let me explain that a little bit more, which is that when you ask this question, how well is it working? It means defining a hypothesis. So if I run this experiment, then this is the outcome I expect because this is the connection. And then I list what are the leading and lagging indicators. So this is the left brain part of my thinking where I'm figuring out how well is it working and have clarity on what I even define as how well and what is working, right? The second question, what did I learn? Is where I tell teams, know, don't just throw out numbers at me. Don't just tell me you've reduced customer service costs by 30%. Don't just tell me I've reduced the number of calls to engineering by X%. Instead, tell me what have you learned?

What is actually going on? Tell me what the data, the entire story, what is telling you? Do the storytelling for me. And then the last question is based on how well it's working and what you've learned, tell me if I gave you a magic wand, what would you ask for? And these last two questions, Of how well is it, sorry, of what have you learned? And if I gave you a magic wand, what will you ask for? This is the creative problem solving, the right brain part. That helps you do this puzzle solving. And so the way this works for a leader is, you know, you don't have to abdicate OKR or set aside OKRs on day one, right? Because that feels scary. Just start by introducing OHLs, right? Because it's a way of thinking. It's not even a process burden. It's not additional work. It's just you tell teams, you know, instead of when you present information to me in monthly business reviews, don't spit out like a bunch of numbers at me.

Radhika Dutt
Give me this narrative because that requires so much analysis. And then, you know, this just sort of becomes a way of thinking for the team. And it just sort of automatically de-emphasizes goals and targets.

Aayushi
Okay, yeah, that's really... going into the right brain side of things, having more like creative problem solving, that's beautiful. And while we were talking, you really focused on having a clear vision, right? So I want to know what your ultimate vision for how organizations can break free from the performance illusion and truly optimize their performance by building cultures that perform and not just for show.

Radhika Dutt
I love the question. So how do you organize, create this culture? So one thing just to take away as a lesson from history is when you look at the book, Measure What Matters and what John Doar says about Intel's success, he attributes it to OKRs. If you actually look at what Walter Isaacson wrote about Andy Grove, what led to Intel's success was his paranoid obsession with never getting complacent. So, you know, in terms of culture and what do we take away, it's not that as a leader you have to be setting goals and targets. It's not that that makes you successful. What makes you successful is when you create a culture of psychological safety where teams can openly present what is going well, what is not going well, you know, what to try next. When you create a culture that encourages the sort of experimentation, the open and honest discussions so that you know not just the good metrics and what people want to present to you as ta-da, but also the bad metrics and kind of what you're going to do next. And so that's what I think as leaders we can aspire to, creating that sort of a culture. And Andy Grove called it a culture where it was...

How do you describe it? Constructive confrontation.

Stephen King
Now...Radica we're coming towards the end of our conversation which has been really delightful and enlightening and there's a there's a number of takeaways that Business owners can take from this and they can really start to reflect upon their operations but assuming that you are a younger person and you're in the middle middle management and you are seeing the You are being forced to create numbers for the sake of creating numbers and you know that the people who are seeing the numbers are not really acting on it, but you're forced in this cycle of number crunching and number crunching presenting and then nothing ever changing because people appreciate the fact that the numbers are really just there for show. What can we do if we're not in a position of authority to help move the company away?

Radhika Dutt
Hmm. You know, on this, I think even as you were describing it, I was thinking about just how soul sucking that is, right? When you're working that scenario. So what I would say for people is, you know, even as a starting point for yourself, goals and targets are soul starving, but you can even frame it as puzzle setting and puzzle solving for yourself. And so

OHLs and the free template that I put up on my site. OHLs give you the starting point to start to frame things as puzzle setting and puzzle solving. And so if you're someone who cares about your work, this is like catnip for you. It makes it so much more rewarding, even for yourself, right? So the first thing is even just for yourself, you can start to apply this approach and start to think about your day-to-day work. you know, I was on... on a call with a client and they're like, you know, my employees have a lot of trouble setting goals for themselves. How can I help them? And, you know, it's like, well, actually, if you ask them a different question, what are the puzzles you'd like to solve this year? my, you'll see a very different answer. So I think this is the sort of question you can even say to yourself, even if you can't change the organization, what are the puzzles that you want to work on this year? And when you start to think about it that way,

you can then start to introduce this approach, even within the safety of just the people that you work with, that you feel like you have the sort of safety with, and you spread this slowly. You don't have to challenge OKRs. You don't have to challenge targets and goals, et cetera, at top level management. You can just start to introduce this as a way of making everyday work less soul starving.

Stephen King
That's super. Aishi, would you have anything else or are we happy to conclude this wonderful conversation?

Aayushi
I don't have any more questions.

Stephen King
Wonderful stuff. Well Radhika with Ayushi's approval there. Is there any last message you would like to leave to our listeners on how they can access your book or where they can find out more information from your OHL methodology?

Aayushi
You

Radhika Dutt
Okay, great. So I'm in the process of writing the book. And so this is a sneak preview for our listeners so that, you know, as you apply OHLs, I would love to hear from people. Like I already have case studies in the book I'm writing about. But you know, if you want to reach out to me and share your experience, I would love to hear from you. And who knows, that might also make it as a case study in the book. So you're welcome to reach out to me. I'm on LinkedIn.

Radhika Dutt.com also get the OHL's template from radicalproduct.com. And lastly, you can also get the Radical Product Thinking Book. It's wherever books are sold.

Stephen King
That's absolutely superb. Thank you very much Radhika. Thank you Ayushi. And for everyone listening at home, if you want to keep tuned in to more radical thinking on business topics and artificial intelligence and general marketing and advertising topics, please don't forget to subscribe, like and provide any comments wherever the box allows you to do. We're always welcome to receive them. Okay. Thank you all very much.

Aayushi
Thank you. Thank you.

Radhika Dutt
Thank you for having me.

Stephen King
Wonderful stuff. Thank you.