Quellenbasierte Analyse der OKR Experten® · Stand: Oktober 2026

By Erno Marius Obogeanu-Hempel · Last updated: October 10, 2026

In her book *Radical Product Thinking*, Radhika Dutt advises replacing OKR-based goal-setting with her own approach. We have examined her seven arguments against OKR: against the sources she herself cites, against independent research, and against our experience from over 300 OKR implementations.

Book Review: Dutt, R. (2021). Radical Product Thinking: The New Mindset for Innovating Smarter (1st ed.). Berrett-Koehler. ISBN 978-1-5230-9331-1

Introduction

Radhika Dutt’s book *Radical Product Thinking* (2021) describes an approach to vision-driven product development. In Chapter 6, Dutt examines Objectives and Key Results (OKR) in detail and reaches a clear conclusion:

“It’s time to move away from using product metrics to set performance goals. It’s time for a more radical approach.”

Translation: “It’s time to abandon the practice of using product metrics as performance goals. It’s time for a more radical approach.”

In her book, Dutt describes exciting and useful approaches. Her template for a vision statement, her RDCL strategy, and her prioritization matrix are valuable tools for product teams. This makes her blanket rejection of OKRs all the more regrettable. She explicitly positions her method as a replacement: “To align your team on metrics and replace goal setting with OKRs, you can take these three practical steps.” Yet, as Section 2.6 shows, OKRs could be easily combined with their approaches.

Here at OKR Experts, we have had the privilege of guiding over 300 companies through the implementation of OKRs—from small businesses to energy conglomerates and public administration agencies. That is why we take Dutt’s criticism seriously. We have examined each of her arguments—both against the sources she herself cites and in light of independent research.

The problems Dutt describes are real. They arise when OKR is not implemented or executed optimally. We suspect that Dutt’s insights stem from such implementations. Had she experienced OKR the way we establish it in companies, her assessment would likely have been different. We practice OKR differently: We have further developed the method and adapted it to the corporate culture and conditions in Germany and Europe. Five key adjustments are central to this:

1. Outcome rather than output or input. We consistently set outcome-based goals—that is, benefits and value for customers, employees, and the organization—rather than goals focused on completed tasks and results (output) or resources used (input).
2. Teams, not individuals. We use OKRs at the company, division, department, and team levels—not as goal-setting exercises with individual employees. OKRs are neither tied to compensation nor part of performance evaluations. This approach is also consistent with the co-determination rights of the works council and staff council.¹
3. Works council or staff council from the very beginning. We regularly involve the employee representative body from the start of the implementation process.
4. A dedicated role for the method. An OKR coach, master, or champion supports the teams, rather than having the manager take on the coaching role. Reports are submitted to the team, not to the manager.
5. Annual goals as KPIs and a target vision instead of annual OKRs (Section 2.4).

Our approach thus deliberately differs from models in which OKRs extend down to the individual level and are incorporated into individual performance evaluations. The following sections show how this relates to Dutt’s criticism.

To get straight to the point: Dutt’s criticism targets a form of goal-setting that OKR explicitly excludes—namely, sales quotas, annual targets linked to bonuses, and individual goal agreements. Where his criticism has a grain of truth, the answer lies in OKR itself—in some cases, dating back to Andy Grove, who developed the method at Intel in the 1970s.

1. What Dutt Criticizes About OKR

Dutt’s critique of OKRs is summarized in the section “The Danger of Setting Goals for Product Metrics” in Chapter 6 of her book. All quotes from Dutt are taken from this chapter, unless otherwise noted (Dutt 2021). Her critique can be summarized in seven points:

1. Product metrics become goals. Here’s an example of a typical key result: “Get over 20,000 new signups.”
2. Ambitious goals can be demotivating. “Ironically, the goals that were designed to be aspirational become demotivating.” (German: “Ironically, it is precisely the goals that were meant to be ambitious that end up demotivating.”) Out of fear of failing to meet them, even top performers advocated for less ambitious goals.
3. Specific goals can be counterproductive when it comes to complex tasks. Studies have shown that “do-your-best instructions led to better results than specific goals.” Specific goals hindered experimentation and adaptation.
4. Focusing on just a few metrics leads to a local optimum. “OKRs may be helping you reach the local maximum instead of the global maximum.”
5. Targets encourage unethical behavior. She cites the accounting scandal at Lucent Technologies and the fake accounts at Wells Fargo as evidence.
6. Designated managers take responsibility for mistakes personally. Even without a bonus, everyone knows “whose failure it was.”
7. OKRs are rigid and time-consuming. “OKRs, once set, are hard to adjust,” and: “Teams that use OKRs spend long hours negotiating these goals.” Spotify has therefore abandoned OKRs.

Dutt’s Alternative: Radical Product Thinking

Dutt contrasts OKR with her own method. It consists of five components:

1. Radical Vision Statement (Chapter 3): A vision presented as a fill-in-the-blank: “Today, when [identified group] wants to [desirable outcome], they have to [current solution]. This is unacceptable because [shortcomings of current solutions]. We envision a world where [shortcomings resolved]. We are bringing this world about through [basic technology/approach].“ (German: “If [specific group] wants to achieve [desired outcome] today, they must use [current solution]. This is unacceptable because [shortcomings of current solutions]. We envision a world in which [shortcomings are resolved]. We are bringing this world about through [fundamental technology or approach].”)
2. RDCL Strategy (Chapter 4): four questions regarding real pain points (what pain drives the use of the product?), design (what feature solves it?), capabilities (what capabilities are needed?), and logistics (how does the solution reach the user, and through what business model?).
3. Prioritization (Chapter 5): a four-quadrant matrix based on “Vision Fit” and “Survival” (i.e., short-term survival), with the quadrants “Ideal,” “Investing in the Vision,” “Building Vision Debt,” and “Danger!”
4. Implementation and Measurement (Chapter 6): Hypotheses following the pattern “If [experiment], then [outcome], because [connection]” (i.e., “If [experiment], then [outcome], because [connection]”) and metrics without target values (“just refrain from setting targets”).
5. Culture (Chapter 7): a four-quadrant matrix that categorizes work into “Meaningful Work,” “Heroism,” “Organizational Cactus,” and “Soul-Sucking.”

OKR is to be replaced by three steps: “Align on what you’ll measure. Create a safe environment to discuss metrics. Manage progress through regular feedback.” (German: “Agree on what you’ll measure. Create a safe environment to discuss metrics. Manage progress through regular feedback.”)

Dutt summarizes the core of her criticism of OKRs in one of her seven “product diseases” (Chapter 2), “hypermetricemia”: “focusing excessively on measurable outcomes to determine success, irrespective of whether those are the right things to measure.” (German: “the excessive fixation on measurable results as a measure of success, regardless of whether those are the right metrics.”)

2. Where Dutt Is Wrong

2.1 OKR Is Not a Sales Quota

What Dutt writes

For Dutt, OKRs are “a framework used by many companies to define goals across the organization, assign responsibility, and track outcomes.” Her strongest examples against setting goals are Lucent and Wells Fargo. Regarding Wells Fargo, she writes:

“Branch managers were assigned aggressive quotas for the number and types of products sold. If the branch did not meet its targets, the shortfall was added to the next day’s goals.”

Translation: “Store managers were assigned aggressive quotas for the number and type of products sold. If a store failed to meet its targets, the shortfall was added to the next day’s targets.”

Classification

At Lucent, the CEO set an annual revenue growth target of 20 percent. At Wells Fargo, shortfalls were carried over to the next day. The U.S. Consumer Financial Protection Bureau (CFPB, 2016) explicitly cites “sales targets and compensation incentives” as the cause. Both are quota systems operating under high pressure; at Wells Fargo, they are explicitly linked to compensation incentives.

OKR, as we practice it, excludes precisely these characteristics:

• We don’t use OKRs to manage operational issues such as sales quotas anyway; instead, we use KPIs. OKRs cover only strategy and innovation (exploration) as well as targeted improvements (exploitation); see Section 2.5.
• OKRs are not tied to bonuses, salary increases, or promotions, and are not used for individual performance evaluations.
• A 70 percent achievement rate is considered a success. According to its own OKR guide (Google re:Work), Google expects “an average of 0.6 to 0.7 across all OKRs.”
• When goals are not met, the focus is on analyzing causes and lessons learned, not on assigning blame.

Dutt himself quotes Laszlo Bock, Google’s head of human resources from 2006 to 2016: “Google tied OKRs for product usage directly to people’s compensation. People started gaming the system to get their bonuses.” (German: “Google linked OKRs for product usage directly to employees’ compensation. People began to game the system to get their bonuses.”) The result was the decoupling of OKRs and compensation. Their case studies thus demonstrate what happens when precisely these OKR rules are missing. They do not argue against OKRs.

2.2 Spotify has eliminated only individual OKRs

What Dutt writes

Spotify announced on its HR blog that “it’s no longer using OKRs.” Dutt quotes from the post: “What went into the OKR process was often already outdated by the time we got that far. So the OKRs that came out were too.” (translation: “What went into the OKR process was often already outdated by the time we got that far. So the OKRs that came out of it were too.”)

Classification

Dutt’s own source (Spotify HR Blog 2016) is titled “Why Individual OKRs Don’t Work for Us.” In the post, Spotify writes that it eliminated OKRs for individual employees in 2013 because the process had become redundant at the individual level. At the company level, Spotify notes in the same post, OKRs worked well.

We use OKRs at the company, division, department, and team levels—not as goal-setting exercises with individual employees. Spotify’s experience confirms this approach rather than refuting it.

2.3 Target-oriented research suggests otherwise

What Dutt writes

“Studies have found that, when performing a complex task where the correct strategy was not obvious and performance depended more on strategy than on the effort expended on the task, ‘do your best’ instructions led to better results than specific goals.”

Translation: “Studies have shown that for complex tasks where the correct strategy was not obvious and performance depended more on strategy than on effort, the instruction ‘Do your best’ led to better results than specific goals.”

Dutt draws primarily on the essay “Goals Gone Wild” (Ordóñez et al. 2009a) and a study by Earley, Connolly, and Ekegren (1989).

Classification

The truth is: In the early stages of learning, when students still lack the knowledge needed for the task, specific performance goals can be counterproductive. However, Dutt omits the crucial part of that same research.

• Both approaches achieve learning objectives. Seijts and Latham (2005) summarize the findings as follows: In their business simulation, there was no significant difference between participants with a performance goal and those instructed to “do your best.” In contrast, participants with a specific learning goal achieved a market share that was nearly twice as high. The authors therefore recommend setting a specific, challenging learning goal for new, complex tasks. The conclusion, then, is not “no goals,” but rather “the right goal for the situation.”
• There is no critique of “Goals Gone Wild.” Edwin Locke and Gary Latham, who founded goal-setting theory, refuted “Goals Gone Wild” that very same year (Locke and Latham 2009). Dutt cites only their 1990 book and calls the solutions proposed there “superficial.”
• Even the critics do not want to do away with goals. Ordóñez and colleagues (2009b) compare goal setting to an effective medication that should be “carefully prescribed and closely monitored.” In Dutt’s own book, Lisa Ordóñez says of their approach: “It retains the best part of goal setting (directing and aligning actions) without the negative aspects.” (She, too, wants to retain the aspect of directing and aligning actions through goals.)
• Group goals appear to be robust. The meta-analysis by Kleingeld, van Mierlo, and Arends (2011), based on 49 effect sizes, shows that group goals significantly increase group performance (d = 0.56). Specific, challenging group goals have a large effect (d = 0.80).

In Section 3.4, we describe how OKR handles learning objectives during periods of uncertainty.

2.4 OKRs are not set for a year

What Dutt writes

“Getting buy-in and setting OKRs every year takes an incredible amount of effort. If you discover during the year that some of the goals were set incorrectly, how much of the coordination and realignment are you willing to revisit?”

Translation: “Obtaining approval and setting OKRs every year requires a tremendous amount of effort. If, over the course of the year, you realize that some goals were set incorrectly, how much coordination and realignment are you willing to go through all over again?”

She quotes an executive from a large company: “We’d die if we had to do this many times a year.” (German: “We’d be ruined if we had to do this several times a year.”)

Classification

Dutt advocates for annual OKRs. In fact, according to its own guide (Google re:Work), Google also uses “annual and quarterly OKRs.” In practice, we haven’t had good experiences with annual OKRs: The method is used differently than in the short cycles, and that usually leads to confusion. For the year, we therefore work with two other tools:

• KPIs as annual goals that are to be fully achieved.
• A target vision for the end of the year, the“MOAL Picture” (MOAL stands for Mid-term Goal). It is created using Amazon’s “Working Backwards” method: The target scenario describes, in the form of a fictional press release, what will have been achieved by the end of the year, supplemented by the KPI targets. Starting from this state, we plan the necessary steps backward. Former Amazon managers Colin Bryar and Bill Carr describe the principle of the method as follows: “Start by defining the customer experience, then iteratively work backward” (Bryar and Carr 2021).

We set OKRs only for short cycles—so far, mostly for three months (a quarter) and, in some cases, for four months (a tertial). Due to the acceleration of technological development driven by AI and the resulting acceleration in nearly all areas, we expect a two-month cycle to become the standard in the future. Progress is discussed weekly during check-ins, and actions are adjusted every one to two weeks based on their actual impact. The goal remains constant; the path to achieving it is flexible. In the event of unforeseeable major events, such as a pandemic or the outbreak of war, we start the planning process from scratch.

The executive’s quote clearly describes a strict cascade in which each Key Result is passed down from the top. As we gain maturity, we work with loose coupling: The teams independently derive their OKRs from the higher-level OKRs. This significantly reduces the effort required for coordination. For highly mature product teams, a bottom-up approach to goal-setting is also possible instead of a top-down cascade. In that case, however, product portfolio management must be ensured, and the goals must be aligned with the corporate strategy. Alternatively, the teams develop the strategy together with top management and review it regularly.

We describe when an OKR set should be adjusted during the cycle in Section 3.3.

2.5 Focus on a Few Key Metrics? Not Without KPIs to Go Along With Them

What Dutt writes

“Employees may optimize for those narrow measures of success, but this may come at the expense of other KPIs that you’re not measuring. OKRs may be helping you reach the local maximum instead of the global maximum.”

Translation: “Employees may optimize for these narrow measures of success, possibly at the expense of other KPIs that aren’t being measured. OKRs may help you reach the local maximum instead of the global maximum.”

Context

This applies to organizations that use OKRs exclusively for management. We use a three-dimensional management model:

• Exploration: OKRs for strategy implementation, including the development of new business models.
• Exploitation: OKRs for the targeted improvement of existing processes, where KPIs identify areas for improvement.
• Execution: KPIs continuously monitor the health of day-to-day operations.

During the check-in, the most important KPIs are discussed along with the status of the OKRs. OKRs are the navigation system; KPIs are the instruments on the dashboard. Exploration, in particular, aims for the “global maximum” that Dutt finds lacking.

Even Dutt’s example, “Get over 20,000 new signups,” wouldn’t be a good key result for us. Registrations are considered vanity metrics: they look impressive but say little about actual success. As key results, they’re useful at best as a supplement. Andy Grove already knew how to prevent one metric from being optimized at the expense of others (Section 3.1).

2.6 Dutts’ alternative reintroduces the goals through the back door

What Dutt writes

“You’ll need to develop a shared understanding of the baseline metrics today and discuss what improvements you want to see, how quickly you want to see those improvements, and how that affects priorities.”

Translation: “You must develop a shared understanding of today’s baseline metrics and discuss what improvements you want to see, how quickly you want to see those improvements, and what that means for priorities.”

Context

Baseline, desired improvement, pace: These are precisely the components of a Key Result—namely, baseline, target, and timeframe. Dutt simply chooses not to document them. As a result, they become less transparent and less binding, but they are no less goals.

The rest of her counterproposal is also included in OKR:

• Your regular metrics meetings correspond to the OKR check-in and the OKR review.
• Your detailed vision and the strategy derived from it are the prerequisites for OKR. We derive OKRs from the mission statement, strategy, annual goals, and annual roadmap.

Radical Product Thinking and OKR are therefore not mutually exclusive; they complement each other. Radical Product Thinking sharpens the vision and product strategy. OKR translates these into a focus for each short cycle with measurable progress, across all areas of an organization. Two connections are obvious:

• The Vision as a Source for Objectives. The Radical Vision Statement describes the change a product is intended to bring about. From this, objectives can be derived that address this change step by step.
• The Matrix as a Tool for OKR Planning. Balancing “Vision Fit” and “Survival” helps determine which topics belong in the next cycle and which should be deliberately put on hold.

Especially for areas without product metrics—such as human resources, finance, or administration—“Metrics Without Target Values” does not provide a mechanism for setting priorities.

3. Where Dutt Hits the Nail on the Head and How OKR Solves It

3.1 Whitewashing: When the Metric Becomes the Goal

What Dutt writes

“Researchers found that people who were given specific goals were more likely to engage in unethical behavior than people who were told to do their best. Even more importantly, they found that the relationship between goal setting and unethical behavior was particularly strong when people fell just short of reaching their goals.”

Translation: “Researchers found that people with specific goals were more likely to act unethically than people who were simply told to do their best. More importantly, the correlation was particularly strong when people fell just short of their goal.”

Added to this is her argument that decoupling from compensation is not enough.

The True Essence

Goals create pressure, even without money. The study on which Dutt bases his argument (Schweitzer, Ordóñez, and Douma 2004) found this effect both with and without financial incentives. No one should downplay this point. What matters, however, is how numbers can be embellished. There are four such ways, and OKR has a solution for each one.

Option 1: Report incorrect figures

In the experiment conducted by Schweitzer and colleagues, 154 students formed words consisting of seven letters. They checked their own results and then reported whether they had achieved the target for nine words. The researchers compared these reports with the words that were actually written. In other words, they measured the perceived quality of the participants’ own reports.

When it comes to Key Results, we rely on impact metrics that can be objectively measured. If these metrics are tracked through systems—such as a CRM, ticketing system, or phone system—this approach is largely off the table.

However, “objectively measurable” does not necessarily mean “objectively measured.” In 2003, the Commission for Health Improvement (Commission for Health Improvement 2003) found that one-third of the emergency medical services audited had retroactively adjusted their response times to meet the eight-minute target for life-threatening emergency calls. This resulted in a 2- to 6-percentage-point improvement in measured performance. Among other factors, the agency cited “fear of failure” and cultures in which employees felt pressured to “give the right answer.”

It follows that, for each key result, it must be clear where the number comes from and who measures it. Whenever possible, the number should be provided by a system rather than by the person reporting on progress.

Option 2: The number is correct, but the method is wrong

At Wells Fargo, the metric was perfectly objective, because accounts can be counted. According to the CFPB (2016), employees opened approximately 1.5 million deposit accounts and applied for approximately 565,000 credit cards that customers may never have authorized. To do this, they transferred money between accounts, created fake email addresses, and assigned PINs without the customers’ knowledge. The number was real. Reality was manipulated.

Social scientist Donald T. Campbell has described this mechanism (Campbell 1979): The more a quantitative metric is used to inform decisions, the greater the pressure for it to be distorted, and the more likely it is to distort the processes it is actually intended to reflect.

OKR deliberately leaves the path to the goal open, because that is where the teams’ creativity lies. However, this freedom also includes undesirable paths. Typical examples:

A memo from the U.S. Occupational Safety and Health Administration (OSHA) dated 2012 (OSHA 2012) illustrates just how realistic this example of accidents is. It warns against incentive programs in which an entire team loses out as soon as an injury is reported. Such programs lead to injuries going unreported, in part because those affected do not want to put their colleagues at a disadvantage. The memo refers to bonuses. However, the same mechanism can also operate through social pressure, for example, when a team score is visible company-wide.

Approach 3: Broaden the Definition

Measurements can only be objective once it is clear what is being counted. What is a “new customer”—does it include a reactivated account or a trial subscription? What is an “accident,” and what is an “error”?

An example from the United Kingdom illustrates just how far-reaching such leeway can be: In January 2014, the UK Statistics Authority revoked the “National Statistics” quality designation from police-reported crime figures (Full Fact 2014). This decision followed, among other things, testimony before Parliament by active and former police officers that crimes had been downgraded to less serious categories.

It follows that: The measurement definition for a key result is established during the planning phase and may only be changed in a documented manner.

Method 4: Press the target value

Objective measurement says nothing about how ambitious a target is. Bevan and Hood (2006) use the example of the English healthcare system to show how target-based systems lead to such tactical behavior. A well-known example is the “ratchet effect”: Those who exceed their targets today will be given a higher target tomorrow, so they hold back.

Two basic OKR rules directly counteract this: A 70 percent goal achievement rate is considered a success, and OKRs are not tied to compensation. Those who have nothing to lose have no incentive to inflate the target figure. Additionally, the baseline should be based on a defined time period rather than the most favorable month.

The answer from OKR itself: paired metrics, now called “guardrails”

The most effective response to Dutt’s objection comes from the inventor of OKR himself. In *High Output Management* (1983), Andy Grove writes that metrics guide action. That is why it is important to prevent overreactions:

“You can do this by pairing indicators so that both the effect and the counter-effect are measured together.”

Translation: “This is achieved by pairing indicators so that both the effect and the countereffect are measured together.”

His example: If you want to reduce inventory levels, you also have to measure stock shortages.

Today, such counter-indicators are usually called “guardrails.” The term is best known from the experimental practices of large digital companies, where guardrail metrics are monitored alongside the actual target metric (Kohavi, Tang, and Xu 2020). A guardrail specifies the maximum extent to which another metric is allowed to deteriorate while the team works toward the actual goal.

For us, each key result includes four pieces of information:

1. an impact metric that is objectively measurable,
2. a defined measurement definition,
3. a specified data source, ideally a system,
4. a baseline value from a specified time period. If one does not yet exist—for example, in the case of a new offering—it is determined at the beginning of the cycle and added retroactively.

We specifically implement a guardrail wherever there is a risk that a Key Result might be achieved in the wrong way. It is then formulated as a separate Key Result within the same OKR set. Here are two examples:

• Sales: The key result “We increased revenue from Product X from 2 to 3 million euros” is accompanied by the guardrail “We maintained the margin on Product X at at least 30 percent.” This ensures that the sales team cannot achieve the revenue target by selling the product below cost. It must come up with other strategies.
• Product: For the key result “We increased the number of weekly active users of our app from 20,000 to 30,000,” the guardrail is “We kept the opt-out rate for push notifications below 2 percent.” The quickest way to get more active users would be to send more push notifications. Anyone who finds them annoying will turn them off or delete the app. The guardrail forces the team to make the app itself more appealing.

The OKR review also includes a question that goes beyond the numbers: “How did we achieve this?”

Interim Conclusion

Dutt is right that targets create pressure to inflate numbers, even without a bonus. Objective impact metrics derived from system data prevent figures from being misreported. To counter the other three pitfalls, we can rely on measurement definitions, a baseline period, the 70-percent rule, and—where there’s a risk of misalignment—Grove’s paired indicators, now known as guardrails. The answer to Dutt is not to abolish targets, but to structure them as the inventor of OKR recommended as early as 1983.

3.2 “Everyone knows whose failure it was”: Responsibility without blame

What Dutt writes

“Unfortunately, separating OKRs from performance reviews isn’t enough […] the process of setting OKRs requires identifying who is responsible for achieving a particular goal. This means that if a goal isn’t achieved, everyone knows whose failure it was […]”

Translation: “Unfortunately, it’s not enough to separate OKRs from performance evaluations. […] The OKR process requires specifying who is responsible for achieving a particular objective. So if an objective isn’t met, everyone knows whose failure it was.”

In essence: Even if OKRs aren’t directly tied to compensation, individual performance is still indirectly linked to the achievement of goals.

Why the argument doesn’t hold up

First, the premise misrepresents OKR. In OKR, no single person is responsible for achieving a goal. The person in charge of a Key Result or OKR set—the owner—has a different role:

• to coordinate the work,
• prepare check-ins and reviews,
• report on progress,
• and, ideally, contribute to the implementation themselves.

The entire team—that is, the workgroup working on the Key Result or OKR set—is responsible for the outcome. Google describes this similarly in its official OKR guide (Google re:Work): The owner—”usually the leader of the relevant team”—explains the evaluation. And further: “OKRs are not synonymous with employee evaluations.”

Spotify had already concluded back in 2013 that OKRs do not work for individuals (see Section 2.2). Dutt cites this very article, but interprets it as a move away from OKRs altogether.

Second, the claim remains unsubstantiated. Elsewhere in her chapter, Dutt draws on studies. She cites no source for the assertion that a designated person in charge inevitably assigns blame.

Third, your own alternative proposal needs the same visibility. Dutt recommends: “To achieve accountability without the adverse side effects of a goals-based approach, you can have product teams present product KPIs at regular update meetings.” (Translation: “To achieve accountability without the negative side effects of a goals-based approach, product teams can present their product KPIs at regular update meetings.”) And: “These discussions achieve increased alignment as well as accountability.” (Translation: “These discussions ensure greater alignment and accountability at the same time.”) When a team regularly presents its metrics to the organization, everyone can see whose product is falling behind. Dutt, too, wants accountability and visibility. She simply removes the agreed-upon target value against which progress is measured.

What the Research Shows

1. Group goals are highly effective, while individual goals can be harmful. The meta-analysis by Kleingeld, van Mierlo, and Arends (2011) evaluates 49 effect sizes and reaches the following conclusions:

• Group goals significantly increase group performance (d = 0.56).
• Specific, challenging group goals have an even stronger effect (d = 0.80). By conventional standards, this is a large effect.
• Individual goals, which someone uses to maximize their own performance, significantly harmed group performance (d = -1.75, six studies).
• Individual goals focused on one’s own contribution to the group goal, on the other hand, had a positive effect (d = 1.20, four studies).

The findings regarding individual goals are based on only a few studies, but the trend is clear. This is precisely the structure that OKR reflects: The team pursues a shared key result, and each individual’s contributions contribute to this shared goal.

2. When contributions are visible, free-riding decreases. An obvious objection to team accountability is that, in that case, no one feels responsible. In psychology, this is called social loafing. The meta-analysis by Karau and Williams (1993) shows what reduces this effect:

• Individual contributions that can be assigned,
• regular feedback,
• a task that is perceived as meaningful.

OKR delivers all three without holding individuals accountable for the outcome. Contributions become visible at the task level—for example, when each team member states during the Daily Huddle or Check-In which Key Result their work contributes to. Feedback is provided weekly. The Key Result itself provides the purpose when it states its objective (“… to …”).

3. Accountability is most effective when it applies to both the outcome and the process. Chang, Atanasov, Patil, Mellers, and Tetlock (2017) conducted a long-term study examining how accountability influences the accuracy of forecasts:

• Those who were held accountable performed better than those who were not.
• Accountability for results yielded better outcomes than accountability for process alone, and the gap widened over time.
• The combination of both was just as effective and also improved the exchange of knowledge.

Applied to OKRs: The team takes ownership of the outcome, as measured by the Key Result. The owner ensures that the process remains transparent: through regular reports, by issuing an early warning if the team’s confidence level drops, and by documenting insights during the review.

4. The real enemy is fear, not responsibility. The same study identifies the danger Dutt refers to more precisely than she does: the fear of being judged. This fear can impair decision-making and cause people to cling to resources they have already invested. The answer to this is psychological safety. Amy Edmondson (1999) defines it as a shared belief within a team that it is safe to take interpersonal risks. Teams with high psychological safety seek feedback more often, discuss mistakes more openly, experiment more, and, as a result, achieve better results.

Google implements this in two places:

• Within the operations teams, post-incident reviews are explicitly designed to be “blameless” (“blameless postmortems,” see the Google SRE Book). They identify causes “without indicting any individual or team” and assume that everyone involved “had good intentions and did the right thing with the information they had.”
• The same approach applies to OKRs (Google re:Work): “Low grades should be viewed as data to help refine the next OKRs.”

Here’s what the role model looks like in practice

This includes a simple rule for meetings: Reports are given to the team, not to the manager.

Interim Conclusion

Dutt’s criticism targets a distorted view: individual OKRs where a single person is held accountable for failure. OKRs, as intended, distribute roles differently. The team is responsible for the outcome, the owner coordinates and reports, and a missed target serves as feedback for the next cycle—not a judgment on individuals. Research supports precisely this structure: shared group goals, visible individual contributions, accountability for results and the process, and an environment where failures can be discussed openly.

3.3 Mid-Cycle Adjustments: When an OKR Set May Be Changed

What Dutt writes

“Once OKRs are set, they are hard to adjust, and teams may end up working toward a goal even when it’s clear that it’s not the right measure of success.”

Translation: “Once OKRs are set, they are difficult to adjust, and teams may continue to work toward a goal even though it is clear that it is not the right measure of success.”

Dutt herself mentions that Bruce McCarthy, author of *Product Roadmaps Relaunched*, recommends in his OKR workshops that OKRs be recalibrated if they prove to be the wrong metrics or unachievable. She considers this impractical in large organizations because OKRs there are coordinated across many departments.

The True Essence

When dealing with ongoing goals, two mistakes are possible, and both have been well documented:

• Sticking to the Wrong Course. Barry Staw (1976) had 240 participants make investment decisions in a business simulation. Those who were personally responsible for a wrong decision subsequently invested the most money in the very same course they had previously chosen. A rule that prohibits changing a clearly wrong Key Result would permanently embed this effect.
• Change your goals when you encounter your first obstacle. Locke and Latham (2002) summarize 35 years of research as follows: Ambitious goals lead to higher performance, and performance only declines when the limits of one’s abilities are reached or “when commitment to a highly difficult goal lapsed.” Those who adjust their goals as soon as things get uncomfortable undermine precisely this commitment. Dutt herself warns against this mistake in her book (Dutt 2021, Chapter 2) and calls it “Pivotitis”: “changing direction whenever things get tough,” resulting in exhausted, confused, and demotivated teams.

What OKR has to say about this

According to its own sources, OKR is not a rigid framework. John Doerr writes in “Measure What Matters” (Doerr 2018): “Remember that an OKR can be modified or even scrapped at any point in its cycle.” Google’s OKR guide (Google re:Work) explains that some teams review their OKRs “a few times a quarter” to drop goals “that are clearly not going to happen” (German: “that clearly won’t be achieved”). However, changes are not arbitrary. The platform What Matters, whose training programs are based on Doerr’s book, deliberately sets the bar high (What Matters): “The bar for changing OKRs should be high,” and: “Never change your OKRs because you’re afraid you’ll fall short of a goal.” (German: “Never change your OKRs because you’re afraid you’ll miss a goal.”) Dutt’s criticism, then, targets a practice that the method itself does not endorse.

Our rule: three cases

To ensure that neither of these things happens, we distinguish between three cases:

Regardless, the teams are constantly adjusting their measures anyway: every one to two weeks, based on their actual impact.

Why this works even in large organizations

Dutt’s objection—that making adjustments is too time-consuming in large corporations—does not hold up for three reasons:

• Outcome goals rarely become outdated. An objective describes the desired impact. This impact usually remains valid even if individual paths toward it fail. The paths are adjusted weekly without the need for a new round of coordination.
• Changes remain localized. Case 2 involves a single key result. Thanks to the transparency of OKRs, affected teams see the change immediately. With loose coupling, it does not trigger a cascade of new negotiations.
• The time spent on errors is short. In Dutt’s scenario, a team might work toward the wrong metric for a year. With short cycles and weekly check-ins, it’s at most a week until the next check-in.

Interim Conclusion

Dutt is right that teams shouldn’t cling to a metric for success that is clearly flawed. OKR doesn’t call for that either. The trick lies in making the distinction: a challenging goal remains, a flawed metric is corrected, and planning starts over in the event of a major change.

3.4 Learning Objectives for Phases of Uncertainty

What Dutt writes

“Specific goals may discourage experimentation and adaptive behavior and ultimately limit innovation.”

Translation: “Specific goals can hinder experimentation and adaptive behavior and ultimately limit innovation.”

The True Essence

Anyone entering a new field lacks the knowledge of how to achieve the goal. In this situation, specific performance goals can be counterproductive because they lead to frantic activity rather than learning. Locke and Latham (2002) also confirm this: For tasks that are complex for those involved, “learning goals can be superior to performance goals.” However, as shown in Section 2.3, this does not mean that one should do without goals. Seijts and Latham (2005) explicitly recommend a specific, challenging learning goal.

The Answer from OKR: Learning Focus in the Objective, Impact in the Key Results

During times of uncertainty, we clearly distinguish between three levels:

• The objective sets the direction for learning, as recommended by Seijts and Latham.
• Key Results measure the impact on the customer, not the team’s work.
• The actions are the experiments the team uses to figure out what works.

This corresponds to the cycle of building, measuring, and learning that Eric Ries describes in *The Lean Startup* (Ries 2011). Ries calls the result “validated learning”: the empirical evidence that a team “has discovered valuable truths” about current and future business prospects.

An example from pricing illustrates just how much depends on the wording. The objective is: “We have found a pricing model that appeals to small- and medium-sized business customers.”

Output that we avoid

“We conducted a robust analysis with at least 50 target customers to determine whether small-to-medium-sized businesses would pay 49 euros per month for our product, as measured by binding pre-orders, and based on that, we decided on the pricing model.”

This key result appears measurable and ambitious. However, it merely measures the team’s work: a test was conducted, and a decision was made. It would still be considered achieved even if not a single customer found the pricing model convincing. It does not indicate whether the model resonates with customers.

Outcome We Are Aiming For

“We received a 5-star rating from our target audience for the appeal of our pricing model.”

This key result measures the impact on the customer: how convincing the target audience finds the pricing model. We’ll determine the specific price point in the next step, once the model itself has proven convincing.

Three Rules for Outcome Goals During Times of Uncertainty

1. Customer impact, not team performance. A key result measures what happens with the customer, not what the team has done or decided.
2. A low score is an insight. If the target audience rates the pricing model 2 out of 5 stars, it isn’t convincing, and the team will test a different one in the next cycle. Google puts it this way (Google re:Work): “Low grades should be viewed as data to help refine the next OKRs.”
3. First the model, then the price point, then growth. If the model is convincing, the next set of OKRs determines the price point. This is followed by a key result for growth—such as the number of new customers. In this way, OKRs set the right goal for each phase: first learn, then scale.

Interim Conclusion

The research Dutt cites does not argue against goals, but rather in favor of the right kind of goals. OKR combines both: The Objective provides a direction for learning during periods of uncertainty, the Key Results measure the impact on the customer, and the actions remain open to experimentation.

3.5 Ambition and Effort

What Dutt writes

“Ironically, the goals that were designed to be aspirational become demotivating. Even high-performing individuals who are passionate about their product will advocate for less ambitious goal setting because of the fear of failing to achieve those goals.”

Translation: “Ironically, it is precisely the goals that are supposed to be ambitious that end up demotivating people. Even top performers who are passionate about their product advocate for less ambitious goals out of fear of failing to achieve them.”

And regarding the effort involved: “Teams that use OKRs spend long hours negotiating these goals.”

Ambition: the true core

Dutt does not cite any source for the claim that high performers settle for lower goals out of fear. Nevertheless, there is some truth to it: Sitkin, See, Miller, Lawless, and Carton (2011) argue that “stretch goals”—in the sense of seemingly unattainable goals—are risky. They are “paradoxically, most seductive for organizations that can least afford the risks.” Unlimited ambition, then, is not an end in itself.

What Research Says About Ambitious Goals

Locke and Latham (2002) clearly summarize the findings:

• “The highest or most difficult goals produced the highest levels of effort and performance”
• “Specific, difficult goals consistently led to higher performance than urging people to do their best”

So ambition works as long as the commitment to the goal endures. The question is not whether goals should be ambitious, but how to foster ambition without fear.

Our Practice

• 70 percent is considered a success. We apply this rule to all our outcome goals. Anyone who sets an ambitious goal and achieves 70 percent has met their goal. This eliminates the basis for fear of failure. Since OKRs are not tied to compensation, there is no reason to set goals too low.
• Ambitious does not mean unattainable. Our goals are set so that 70 percent of them can be achieved through hard work: not by taking the easy way out, but through dedication and creativity. They are therefore not “seemingly unattainable” goals in the sense described by Sitkin and colleagues. They are meant to be an incentive to get started right away and keep at it, but not so demanding that they lead to burnout or resignation. This is consistent with Locke and Latham (2002): Performance declines as soon as the connection to a very difficult goal is lost.
• Where 100 percent compliance is mandatory, the 70 percent rule does not apply. When it comes to security and compliance, partial compliance is not considered a success. Such goals are not treated as ambitious OKRs.
• Output goals do not belong in an OKR set. In any case, the 70-percent rule would hardly be applicable to them. We therefore formulate Key Results as impacts, not as activities or deliverables.
• Early warning instead of late disappointment. During the weekly check-in, the team assesses how confident it is in achieving the key result (confidence level). If confidence drops, this becomes apparent early on, and the team can request support or respond according to the three-case rule from Section 3.3.

Cost: What OKR Actually Costs

Dutt criticizes the hours teams spend negotiating OKRs. Her own proposal, however, doesn’t save that time—it redistributes it: “Instead, you can allocate those same hours to regularly scheduled cross-functional discussions.” So it’s not about reducing the effort, but about redirecting it.

In our practice, OKR planning takes four hours per team per cycle, divided into two hours for drafting and two hours for refinement. For company-wide OKRs, we allocate up to one day. For quarterly cycles, that amounts to 16 hours per team per year—about two workdays—and for future two-month cycles, 24 hours—about three workdays. Two factors contribute to this:

• Few objectives. Google’s guide (Google re:Work) recommends “just three to five objectives,” each with “about three key results.” We recommend two to four objectives, each with two to four key results, thereby deliberately deviating from common rules of thumb such as “three objectives with three key results each” and from Google’s recommendation. A single key result rarely fully captures an objective. More than four dilute the focus and prolong the check-ins.
• Loose coupling instead of a strict cascade. Teams derive their OKRs independently from the higher-level OKRs, rather than negotiating each Key Result individually (Section 2.4).

Interim Conclusion

Ambitious goals are demotivating when failure to meet them is punished and the goals seem unattainable. OKR addresses both issues: 70 percent is considered a success, compensation is decoupled, and where 100 percent is mandatory, the rule does not apply. The effort involved is about two to three workdays per team per year—not the “long hours” that Dutt describes.

4. Conclusion

Radhika Dutt describes the real-world side effects of goal-based management. However, her evidence comes from quota systems, annual targets linked to bonuses, and individual goal agreements—in other words, from precisely the practices that OKR is intended to replace. While there is some truth to her criticism, the solution lies in OKR itself and in the research she cites.

Your own alternative approach includes baseline metrics, desired improvements, and timeframes—just without specifying them. Radical Product Thinking and OKR are therefore not opposites. One sharpens the vision and product strategy, while the other makes their implementation measurable—using rules that specifically prevent the side effects Dutt warns against.

Notes

¹ The works council’s rights of co-determination apply, among other things, to technical devices intended to monitor the behavior or performance of employees (Section 87(1)(6) BetrVG), in the case of performance-based bonuses as a matter of wage structure (Section 87(1)(10) BetrVG; Redmann, Haufe), and in the case of general performance evaluation principles (Section 94(2) BetrVG). In the public sector, the staff council has comparable rights under the laws governing employee representation. ↩

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