OKRs (Objectives and Key Results) have been a mainstay in modern product development and organizational strategy for years. Introduced as a way to align teams around measurable goals, they quickly rose to prominence as the “go-to” framework. Yet, despite their popularity, many product leaders and contributors have encountered significant drawbacks—ranging from “gaming the system” and short-term thinking, to outdated targets that no longer serve the evolving needs of a product or organization.
At a recent workshop hosted by the Product Management Society and Productized, renowned product leader and author Radhika Dutt challenged us to rethink our reliance on OKRs. Through real-life examples and in-depth research, she introduced a new approach called Objectives, Hypotheses, and Learnings (OHLs), which reimagines the way we measure success. This article distills some of the key insights from that conversation and offers an invitation to join a free case study exploring OHLs in practice.
The Pitfalls of OKRs
1. Short-Term Thinking and “End-of-Year Exams”
From small startups to large-scale enterprises, setting annual or quarterly OKRs can feel like cramming for a final exam. As deadlines approach, teams are often driven to show results “by the numbers,” even if it means cutting corners or compromising on broader strategic or ethical considerations. Radhika calls this phenomenon “enshittification,” where too much emphasis on numeric goals ultimately deteriorates the quality of the product, service, or user experience.
2. “Gaming the System” to Hit Targets
Campbell’s Law and Goodhart’s Law—well-known in economics—point out that once a measure becomes a target, it tends to be gamed. For example, if your key result is 20,000 new signups, teams may resort to questionable tactics (e.g., spammy incentives, non-validated email registrations, or even bots) simply to demonstrate “progress.” The metric gets met, but at the expense of genuine learning and real value creation.
3. Lack of True Learning
Organizations often believe that setting ambitious goals helps teams “push harder.” But, as Radhika emphasizes, a stretch goal you never actually intend to meet can be more demoralizing than inspiring—particularly for passionate, high-performing individuals. While some OKR proponents suggest frequent resets (every quarter or even monthly), the overhead of constantly renegotiating goals can be huge, especially in larger organizations.
Enter OHLs: Objectives, Hypotheses, and Learnings
Instead of pinning teams to rigid, top-down targets, Radhika Dutt proposes a framework centered on collaborative learning:
- Objectives
Define an aspirational outcome (e.g., “Increase the speed of development” or “Enhance customer delight”). The objective sets a directional vision but stops short of dictating exact numeric targets. - Hypotheses
Propose testable ideas for why this objective might be achievable and how. For instance, “If we empower code reviewers to push back on insufficient test coverage, we’ll see faster, more resilient development” could be a hypothesis at a self-driving car startup. - Learnings
Gather both qualitative and quantitative feedback to see what worked and what didn’t. The point isn’t to prove success but to gain insights. Are more robust tests actually leading to better code quality? Are customers genuinely delighted—or just checking five-star boxes because they’re “supposed to”?
By removing rigid targets, OHLs unlock the freedom to pivot quickly when new data emerges. Rather than focusing on hitting a certain metric at all costs, you continuously refine your strategy based on what you discover.
Highlights from the Workshop with Radhika Dutt
During a recent virtual session (transcript excerpt available above), Radhika delved into:
- Vision Over Targets
When people have only goals to guide them, they often treat those goals as the de facto vision. OHLs encourage you to first articulate a long-term vision—one that might span multiple years—and then ask: “Which hypotheses should we test this quarter to get closer to that vision?” - Good vs. Bad Metrics
Any data point can be “good” if it’s used for learning. Problems arise only when metrics become a stick to measure performance. In an OHL system, “bad metrics” are often the most valuable—they reveal misalignments, product gaps, or flawed assumptions. - The Freedom to Change
Under an OKR system, updating goals midstream can be cumbersome or politically fraught. OHLs, however, make iteration and continuous improvement a built-in expectation. If a hypothesis isn’t working, you drop it—no shame, no blame—and propose a new one. - Empowering Teams
Participants in the workshop—including leaders from startups, established tech firms, and even those in non-tech industries—pointed out how OHLs could improve morale. With OHLs, you don’t just measure “did you hit your number?”; you ask “what did we learn, and how do we grow from here?”
Join the Free OHL Case Study
If the OHL framework resonates with you—whether you’re a leader seeking to transform your team’s approach, an individual contributor tired of gaming OKRs, or someone keen to adopt OHL thinking in your corner of the world—there’s a free opportunity to learn more.
Radhika Dutt is currently recruiting participants for a case study that will form part of her upcoming book. Selected participants will:
- Attend Monthly Mentoring & Feedback Sessions
You’ll receive guidance on applying OHLs and share your own insights with a curated group of product professionals. - Potentially Be Featured in the New Book
Participants who apply OHL principles and provide compelling stories or data may be included as case studies in Radhika’s upcoming publication. - Shape the Future of Product Management
Your experiences—successes, stumbling blocks, and creative solutions—will help refine OHLs and offer a real-world blueprint for others.
Sound interesting?
Click here to join the OHL Case Study!
Participation is free, and you’ll have the chance to gain hands-on mentorship, contribute to valuable research, and network with like-minded innovators.
Next Steps: Where to Learn More
- Explore Radhika Dutt’s Work
Learn more about her perspective on product leadership and innovation by checking out her first book, Radical Product Thinking, and keep an eye on her upcoming book about OHLs. - Dig Deeper into Product Management Insights
Curious to learn more about forward-thinking product approaches? Head over to my blog on Product Management Society for additional articles and case studies that offer fresh perspectives. - Stay Connected
Follow Radhika Dutt on LinkedIn for updates on the OHL framework, community events, and more details on how you can contribute to (or learn from) this evolving methodology.
Final Thoughts
OKRs have served many teams well, but it’s increasingly clear they can stifle genuine learning and long-term vision when used as rigid performance targets. Objectives, Hypotheses, and Learnings (OHLs) promise a shift—one rooted in continuous discovery and collaboration. With OHLs, you still measure and experiment, but you do so to uncover what truly works and to realign quickly when something doesn’t.
If you’ve ever felt that annual goal-setting didn’t quite capture the spirit of continuous product innovation, now is the perfect time to explore a new path. Join the free OHL case study, get expert guidance from Radhika Dutt, and help shape a framework that might just redefine how we think about success in product management—and beyond.
Workshop Recording
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