Below you’ll find practical OKR examples for various teams, themes or industries. Very often in my workshops and OKR coaching people and teams ask me for good and bad OKR examples so I crated the list below. Each example includes a “bad” and “better” version with a few thoughts. Let me know if you want other examples, have suggestions of your own.
1. OKR Examples For Product Teams
Let’s start with product teams. They’re often the ones who define and drive OKRs.
1. Travel Product
| Bad Version | Good/ Better Version | |
| Objective: | Launch New Travel Booking Flow | Help more users successfully complete their travel bookings |
| Key Results: |
KR1. Design new booking flow KR2. A/B test 3 designs |
KR1. Booking completion rate increases from 4.2% to 42% KR2. Booking-related customer support tickets drop by 42% |
Why: The bad objective focuses on delivery (launch), not behaviour. The good objective focuses on enabling a change: more completed bookings.
2. E-commerce Product
| 1 | Bad Version | Good/ Better Version |
| Objective: | Redesign the product listing page | Help more users find and buy relevant products faster |
| Key Results: |
KR1. Finish listing page redesign KR2. Apply new icons and components from the new design system |
KR1. Add-to-cart rate from listing pages increases from 2.42% to 4.2% KR2. Average time to first purchase drops from 8.4 minutes to 4.2 minutes |
Why: Redesigning is only a means to an end. The outcome is in how it affects buying behaviour.
| 2 | Bad Version | Good/ Better Version |
| Objective: | Add more product filters | Shoppers find what they are looking for faster |
| Key Results: |
KR1. Launch 5 filter categories KR2. Update category UX |
KR1. Percentage of product views from search filters increases from 24.2% to 42% KR2. Time to first product click after filter selection drop from 4.2 minutes to 42 seconds |
3. Internal Product OKR Examples
| 1 | Bad Version | Good/ Better Version |
| Objective: | Implement internal dashboard for managers | Managers make faster and more accurate decisions |
| Key Results: |
KR1. Launch Dashboard MVP KR2. Collect feedback from 5 managers |
KR1. 84% of managers report using dashboard weekly KR2. Average time to make decision decreases from 4.2 days to 1.42 days |
Why: “Implement a dashboard” is a task. The value lies in changing the decision making behaviour of the target users.
| 2 | Bad Version | Good/ Better Version |
| Objective: | Add more admin features | Internal users complete tasks with fewer workarounds |
| Key Results: |
KR1. Launch 3 new features KR2. Send announcement email |
KR1. Percentage of manual workarounds drops from 42% to 4.2% KR2. Admin task completion time decreases from 42 min to 4.2 min |
Why: Adding more features is never a good outcome. Users being able to do more of something or less of something is a stronger outcome that the team can drive and execute on.
4. Search / Discovery Product Team
| Bad Version | Good/ Better Version | |
| Objective: | Add advanced search filters | Help users discover relevant products with less effort |
| Key Results: |
KR1. Launch filter UI KR2. Add category tags |
KR1. Percentage of users finding relevant product in first search increases from 42% to 84% KR2. Average number of searches per user before conversion decreases from 8.4 to 4.2 |
Why: Filters are tools. The outcome is users getting what they want faster.
5. AI Product OKR Examples
| 1 | Bad Version | Good/ Better Version |
| Objective: | Integrate GPT into customer support | Support agents resolve tickets faster using AI suggestions |
| Key Results: |
KR1. Build GPT integration KR2. Add AI feedback button |
KR1. Average ticket handling time drops from 4.2 minutes to 2.42 minutes KR2. 84% of agents report AI suggestions are useful |
Why: GPT integration is a feature. The behaviour change is agents using it to resolve issues more efficiently.
| 2 | Bad Version | Good/ Better Version |
| Objective: | Improve algorithm precision | Users trust and rely more on AI driven results |
| Key Results: |
KR1. Tune model weights KR2. Reduce MAE score |
KR1. Percentage of users accepting an AI suggested result increases from 42% to 84% KR2. User surveyed trust in AI output increases from 4.2 to 8.4 out of 10 |
6. Mobile Apps
| 1 | Bad Version | Good/ Better Version |
| Objective: | Release new app version | Improve user satisfaction and engagement on mobile |
| Key Results: |
KR1. Submit app to store KR2. Fix 42 UI bugs |
KR1. App Store rating increases from 2.42 to 4.2 KR2. Daily active sessions increase by 42% |
Why: A new release is not an outcome. If users engage more and are happier with the mobile app, that’s an actual result.
| 2 | Bad Version | Good/ Better Version |
| Objective: | Launch new app version | Users complete key flows on mobile app without friction |
| Key Results: |
KR1. Push update to app store KR2. Redesign 4.2 screens |
KR1. Drop rate in key flow (e.g. add to cart -> checkout) falls from 42% to 4.2% KR2. Average app store rating increases from 2.42 to 4.2 |
Why: Launching a new app is not what we want to achieve. A good outcome describes what users are able to do more of or less of, in this case related to the completion of a key flow.
7. Onboarding
| Bad Version | Good/ Better Version | |
| Objective: | Build onboarding checklist | New users perform their first meaningful first action faster |
| Key Results: |
KR1. Create 4.2 walkthroughs KR2. Add welcome email |
KR1. Percentage of users completing onboarding journey increases from 42% to 84% KR2. Time to firs meaningful action drops from 12 minutes to 4.2 minutes |
Why: Building the checklist or creating walkthroughs is not what we want to achieve. Having more new users complete the onboarding and start performing meaningful actions with our product is what we are after.
8. Hardware
| Bad Version | Good/ Better Version | |
| Objective: | Improve the circuit board | End users experience more consistent product performance |
| Key Results: |
KR1. Test 4.2 new versions KR2. Reduce board size by 4.2% |
KR1. Hardware fail rate drops from 4.2% to 0.42% KR2. Support tickets related to device restarts drop by 42% |
Why: We don’t want to improve just for improvement sake. We want to focus on improvement that drives the right change in behaviour and impact for customers and business.
2. Examples For Other Teams
We continue with OKR examples from all the other important teams present within a company that decide to work with and define Objectives and Key Results.
1. Marketing OKR Examples
| 1 | Bad Version | Good/ Better Version |
| Objective: | Integrate GPT into customer support | Help support agents resolve tickets faster using AI suggestions |
| Key Results: |
KR1. Build GPT integration KR2. Add AI feedback button |
KR1. Average ticket handling time drops from 4.2 minutes to 2.42 minutes KR2. 84% of agents report AI suggestions are useful |
Why: GPT integration is a feature. The behaviour change is agents using it to resolve issues more efficiently.
| 2 | Bad Version | Good/ Better Version |
| Objective: | Run Q2 ad campaigns | More visitors take the first step towards conversion |
| Key Results: |
KR1. Launch 42 ad creatives KR2. Increase impressions by 42% |
KR1. Percentage of visitors clicking on CTAs increases from 4.2% to 42% KR2. Cost per qualified lead drops by 42% |
Why: Running a campaign is an activity, not an outcome. The change in visitors’ behaviour is what we hope to accomplish.
2. Sales OKR Examples
| 1 | Bad Version | Good/ Better Version |
| Objective: | Do 42 sales demos | Increase win rate from demos through stronger sales storytelling |
| Key Results: |
KR1. Book 42 demos KR2. Use new slaes deck |
KR1. Win rate from demos rises from 42% to 84% KR2. Sales cycle time for demo leads shortens from 42 to 24.2 days |
Why: Demos don’t close deals on their own and demos for demos’ sake is not what we want to actually achieve. The win rate shift shows the change in how customers respond to them.
| 2 | Bad Version | Good/ Better Version |
| Objective: | Increase upsell numbers | Existing customers adopt more relevant products to solve their next-level problems |
| Key Results: |
KR1. Create 4.2 upsell packages KR2. Pitch every account once |
KR1. Percentage of existing customers purchasing 2+ products increases from 4.2% to 42% KR2. Churn rate of upsold customers drops from 8.4% to 4.2% |
Why: Sales OKRs can often drift into “do more” territory: more calls, more pitches, more decks. But outcomes in this case are all about changing how leads and customers behave so OKRs should focus on that.
| 3 | Bad Version | Good/ Better Version |
| Objective: | Send more outbound emails | Get more qualified leads to erquest product demos |
| Key Results: |
KR1. Send 4200 emails KR2. Buy new contact list |
KR1. Demo request rate increases from 4.2% to 42% KR2. Meetings booked per sales rep increases from 4.2 to 8,4 per week |
Why: Sending emails and buying new contact lists are just actions and one of many ways to potentially progress towards achieving outcomes. The actual impact comes from making progress on relevant leading indicators like more demo requests and more meetings per sales rep.
3. Customer Support
| Bad Version | Good/ Better Version | |
| Objective: | Hire 2 more support reps | Increase percentage of customers resolving their issue on first contact |
| Key Results: |
KR1. Hire 2 reps KR2. Train tam on new scripts |
KR1. First contact resolution increases from 42% to 84% KR2. Customer satisfaction score increases from 2.42 to 4.2 |
Why: Staffing up is a means to an end. The outcome is users getting what they need in as few calls/interactions as possible.
4. User Research
| Bad Version | Good/ Better Version | |
| Objective: | Conduct 42 user interviews | Product teams make better decisions based on strong user evidence |
| Key Results: |
KR1. Run 42 interviews KR2. Share research summary |
KR1. 84% of product teams say research helped change or confirm direction KR2. Average satisfaction with insights (internal survey) imprvoes from 2.42 to 4.2 |
Why: Doing interviews is activity. A good outcome is a change in behaviour for the people that benefit from and use the research.
5. DevOps
| Bad Version | Good/ Better Version | |
| Objective: | Improve CI/CD pipeline | Developers can deploy faster and with more confidence |
| Key Results: |
KR1. Reduce build time to under 4.2 minutes KR2. Upgrade Jenkins version |
KR1. Percentage of successful deployments without rollback increases from 84% to 94.2% KR2. Average deploy time drops from 42 minutes to 8.4 minutes |
Why: Improving a pipeline is likely not the end goal. What we actually want to achieve is developers being able to ship faster and trusting the system they are using.
6. Infrastructure OKR Examples
| 1 | Bad Version | Good/ Better Version |
| Objective: | Migrate all services to Kubernetes | Help product teams scale reliably without performance issues |
| Key Results: |
KR1. Complete migration KR2. Decomission old servers |
KR1. 100% of product teams report no performance degradation during traffic spikes KR2. Page load time under high load remains under 1.42 seconds for 94.2% of cases |
Why: Migration is a project. The outcome is about how well product teams and users experience performanc afterwards.
| 2 | Bad Version | Good/ Better Version |
| Objective: | Upgrade all servers | Users now experience faster and more reliable platform performance |
| Key Results: |
KR1. Migrate 42 servers KR2. Reduce CPU usage |
KR1. Average load time across core flows drops from 4.2 seconds to 1 second KR2. We have increased uptime from 94.2% to 99.9% |
Why: Upgrading servers is just a task. how users experience our platform and its performance is a more relevant and actual outcome.
7. Data Analysis / Business Intelligence
| Bad Version | Good/ Better Version | |
| Objective: | Build new dashboards for leadership | Leaders make faster and more confident decisions using provided data and insights |
| Key Results: |
KR1. Create 42 dashboards KR2. Conduct data workshop |
KR1. 84% of execs report using dashboard weekly KR2. Decision confidence score when using provided data and insights improves from 4.2 to 8.4 out of 10 |
Why: We don’t want dashboards for dashboards sake. What we are actually aiming for is to drive better decisions.
8. Legal OKR Examples
| 1 | Bad Version | Good/ Better Version |
| Objective: | Review all vendor contracts | Teams onboard vendors faster while staying compliant |
| Key Results: |
KR1. Create contract checklist KR2. Go through all contracts |
KR1. Average contract approval time drops from 42 days to 8.4 days KR2. Percentage of contracts approved without long revisions increases from 42% to 84% |
Why: Legal work often seems like compliance, but an actual outcome is when teams can make progress and gain new customers faster without additional risk.
| 2 | Bad Version | Good/ Better Version |
| Objective: | Review all contracts faster | Teams have fewer legal roadblocks when closing deals |
| Key Results: |
KR1. Review all docs within 48h KR2. Track all changes |
KR1. Percentage of deals delayed due to legal drops from 42% to 4.2% KR2. Internal team satisfaction with legal review process improves from 4.2 to 8.4 out of 10 |
Why: Although you can consider the objective on the left as a potential intermediate result, the stronger and better outcome for the legal work is what the teams they work with are able to do more of or less of. in this case, closing deals with fewer legal roadblocks.
9. Finance
| Bad Version | Good/ Better Version | |
| Objective: | Finalize next year’s budget | Department heads can plan better with more predictable budgets |
| Key Results: |
KR1. Send budget templates KR2. Hold 5 planning sessions |
KR1. 100% of departments submit correctly formatted budgets on time KR2. Average variance between forecasted and actual spend drops rom 42% to 14.2% |
Why: Sending templates and holding sessions is not what the finance team actually wants to achieve. The intended change is the improvement of planning quality and reduction of surprises or errors in the submitted budgets.
Looking to spark change in your product journey? Here’s how I can support you:
- Product Management & Leadership Coaching – I help leaders grow their confidence, lead despite uncertainty, focus on delivering value and create real impact for their teams, users and company.
- Outcome Thinking & OKRs – Backed by hands-on experience in both using and rolling out OKRs across multiple teams and companies, I help you roll out, improve or fix your OKRs or OKR system and make changes and improvements that stick beyond the start of the cycle.
- Facilitation With a Twist – I create, facilitate and moderate workshops that energize the room and foster stronger engagement and learning – all through my crazy, fun and playful approach.
- GenAI for Product Teams – Practical, hands-on workshops that give teams and leaders the skills to harness AI and GenAI for their everyday goals and way of working, but also the time to reflect as a group on the impact that this AI wave brings.
⚡️Let’s jump on a free discovery call!
10. Audit & Controlling
| Bad Version | Good/ Better Version | |
| Objective: | Conduct quarterly audits | Departments and other teams have identified and fixed compliance risks earlier |
| Key Results: |
KR1. Finish 42 audits KR2. Document findings |
KR1. Percentage of identified risks resolved within 30 days increases from 42% to 84% KR2. Repeat audit findings drop from 3 per audit to 1 per audit. |
Why: An audit is a task, a check-up. The change the team is aiming for is in how the audited teams and departments respond and adapt to the findings.
11. IT Support
| Bad Version | Good/ Better Version | |
| Objective: | Reduce ticket backlog | Employees resolve issues faster and with fewer follow-ups |
| Key Results: |
KR1. Close 420 tickets KR2. Hire 1 new agent |
KR1. First response time improves from 12.6h to 4.2h KR2. Percentage of tickets resolve in first contact rises from 42% to 84% |
Why: the number of tickets closed is not the point and sometimes this can even become a chea metric. Faster and complete help is what will change how employees work.
12. Packaging
| Bad Version | Good/ Better Version | |
| Objective: | Redesign packaging layout | Unboxing is now faster and easier for customers |
| Key Results: |
KR1. Test 42 new layouts KR2. Use less material |
KR1. Average unboxing time drops from 2.42 minutes to 42 seconds KR2. Packaging complaint rate drops from 4.2% to 1.42% |
Why: We don’t care about design for design’s sake. What we look at and want to focus on is how we want design and other actions to change the behaviour of our customers – in this case, how people unbox, perceive or struggle with packaging.
13. Learning & Development
| Bad Version | Good/ Better Version | |
| Objective: | Deliver 42 training sessions | Employees have built skills they apply in their roles. |
| Key Results: |
KR1. Conduct 42 sessions KR2. Collect feedback scores |
KR1. 84% of participants report applying new skills in daily work KR2. Average manager rating of team capability improves from 4.2 to 8.4 out of 10 |
Why: Training is not the end goal. Using and applying what has been learned is.
14. HR / People & Culture OKR Examples
| 1 | Bad Version | Good/ Better Version |
| Objective: | Improve onboarding process | New hires feel confident and productive faster |
| Key Results: |
KR1. Reduce onboarding doc length KR2. Conduct 3 onboarding sessions |
KR1. 84% of new hires rate onboarding clarity 8.4+ out of 10 KR2. Average time to first project contribution drops from 10 working days to 4.2 |
| 2 | Bad Version | Good/ Better Version |
| Objective: | Launch new feedback framework | Employees give and receive useful feedback on a regular basis |
| Key Results: |
KR1. Share new feedback templates KR2. Conduct feedback training |
KR1. Percentage of employees who report receiving monthly feedback increases from 42% to 84% KR2. Peer feedback usefulness scores increase from 4.2 to 8.4 out of 10 |
Why: HR work is often process-oriented, but the real win and impact is when employee behaviour shifts, like providing better feedback or more frequent or feeling ready to contribute.
| 3 | Bad Version | Good/ Better Version |
| Objective: | Improve internal mobility | Employees have pursued career growth within the company |
| Key Results: |
KR1. Post all internal openings KR2. Launch career pathing page |
KR1. Internal applicants per open role increases from 1.2 to 4.2 KR2. Percentage of employees moving roles internally rises from 4.2% to 42% |
Why: Bad OKR focuses on vague end goal and task based key results. The right side focuses on what Employees have done more of in relation to internal roles and their career growth and the key results reflect that.
15. Logistics
| 1 | Bad Version | Good/ Better Version |
| Objective: | Implement new fleet management software | Drivers spend less time on admin and more on driving |
| Key Results: |
KR1. Finish software rollout KR2. Train all drivers |
KR1. Driver-reported admin time drops from 2 hours per day to 30 minutes per day KR2. Driver satisfaction with tooling improves from 5 to 8+ out of 10 |
Why: Just improving processes isn’t enough unless it translates to an improvement or less frictions for users or drivers.
| 2 | Bad Version | Good/ Better Version |
| Objective: | Track all shipments | More customers receive packages on time and with fewer issues |
| Key Results: |
KR1. Add tracking tool KR2. Update shipment dashboard |
KR1. Percentage of deliveries arriving on time increases from 42% to 84% KR2. Customer complaints about shipping drop from 20 per month to 4.2 per month |
Why: tracking all shipment is likely not our end goal. more customers receiving packages on time is a good outcome and likely a strong leading indicator for a higher level outcome or impact (a belief proxy here would be that customer satisfaction increases if they get their packages on time and with fewer issues).
16. Warehousing
| 1 | Bad Version | Good/ Better Version |
| Objective: | Reduce energy usage in warehouse | Make warehouse operations more cost-effective and efficient |
| Key Results: |
KR1.Install LED lighting KR2. Set heating schedule |
KR1. Energy cost per unit stored drops by 40% KR2. Equipment idle time reduces from 25% to 10% |
Why: Warehousing isn’t just about space or equipment; the outcomes come when workers are able to move faster and operate more efficiently while making fewer mistakes.
| 2 | Bad Version | Good/ Better Version |
| Objective: | Organise shelves better | Pickers find items faster and with fewer errors |
| Key Results: |
KR1. Redesign floor plan KR2. Add 100 new shelf organisers |
KR1. Average picking time per item drops from 4.2 min to 1 min KR2. Order picking error rate drops from 4.2% to 1% |
Why: Organising for organising’s sake is not the outcome we seek. Geting pickers to find items faster and with fewer errors is.
17. AI Ethics & Alignment Team (withing AI companies)
| 1 | Bad Version | Good/ Better Version |
| Objective: | Publish AI ethics guidelines | AI users feel confident that their data is respected and protected |
| Key Results: |
KR1. Research existing guidelines and best practices KR2. Write |
KR1. Percentage of users opting into advanced AI features increases from 42% to 84% KR2. Users’ trust scores for “responsible AI” increases from 4.2 to 8.4 out of 10 |
18. Digital Twin Operations (smart cities / infrastructure)
| 1 | Bad Version | Good/ Better Version |
| Objective: | Model all city buildings | Urban planners make faster and more confident decisions using simulations |
| Key Results: |
KR1. Map all buildings KR2. Run 10 simulations |
KR1. Average planning decision time drops from 4.2 months to 4.2 weeks KR2. Percentage of planners that run a simulation per week increases from 4.2% to 42% |
19. Space Logistics Coordination
| 1 | Bad Version | Good/ Better Version |
| Objective: | Plan satellite delivery routes | Satellite operators deploy payloads with fewer conflicts or delays |
| Key Results: |
KR1. Get ok on flight plans KR2. Host space traffic workshop |
KR1. Percentage of successful launches on first attempt increases from 42% to 84% KR2. Collision warnings per month drops from 8.4 to 4.2 |
20. Emotional AI Interaction
| 1 | Bad Version | Good/ Better Version |
| Objective: | Improve sentiment detection accuracy | Users feel better understood by their AI companion |
| Key Results: |
KR1. Add new sentiment model KR2. Test new model |
KR1. Percentage of users reporting “felt understood” increases from 42% to 84% KR2. Re-engagement rate after emotional support sessions rises from 42% to 84% |
21. Remote Agriculture
| 1 | Bad Version | Good/ Better Version |
| Objective: | Analyze drone crop footage | Framers take timely actions based on crop health insights |
| Key Results: |
KR1. Process all footage KR2. Create reports |
KR1. Percentage of farmers using insights within 42h increases from 4.2% to 42% KR2. Average footage processing time decreases from 42 minutes to 4.2 minutes |
22. Virtual Education
| 1 | Bad Version | Good/ Better Version |
| Objective: | Launch immersive VR lessons | Students retain more knowledge from the new virtual lessons |
| Key Results: |
KR1. Prepare lesson plans KR2. Train avatars for all teachers |
KR1. Average quiz scores after VR lessons increase from 42% to 84% KR2. Student-reported comprehension rises from 4.2 to 8.4 out of 10 |
22. Decentralized Finance (DeFi)
| 1 | Bad Version | Good/ Better Version |
| Objective: | Improve wallet interface | New users are able to confidently manage their crypto assets within their first session |
| Key Results: |
KR1. Redesign UI KR2. Improve transaction explainability |
KR1. Percentage of users completing their first transaction or transfer without support rises from 4.2% to 42% KR2. Drop-off during onboarding drops from 42% to 4.2% |
23. Quantum Software
| 1 | Bad Version | Good/ Better Version |
| Objective: | Optimize quantum algorithm | Researchers solve previously intractable problems faster using quantum solutions |
| Key Results: |
KR1. Reduce gate count KR2. Publish benchmark paper |
KR1. Percentage of test problems solved under 1h rises from 4.2% to 42% KR2. Research teams report time-to-insight reduced by 42% |
24. Neural Interfacing UX
| 1 | Bad Version | Good/ Better Version |
| Objective: | Improve BCI (brain-computer interface) control | Users interact with devices more “fluently” using only neural signals |
| Key Results: |
KR1. Test UI modes KR2. Reduce signal error rate |
KR1. Average input success rate increases from 42% to 84% KR2. User Fluency increases from 4.2 to 8.4 out of 10 |
25. Autonomous Vehicle Fleet Management
| 1 | Bad Version | Good/ Better Version |
| Objective: | Improve route optimization | Passengers arrive at their destination faster and more reliably |
| Key Results: |
KR1. Reduce idle time KR2. Update features in map algorithm |
KR1. Avg. trip ETA variance drops from 4.2 minutes to under 42 seconds KR2. Reduce avearage waiting time by 42 seconds |
26. AR Commerce Experience
| 1 | Bad Version | Good/ Better Version |
| Objective: | Launch AR try-on feature | More shoppers purchase products via AR without returns |
| Key Results: |
KR1. Build AR experience KR2. Launch Instagram campaign |
KR1. Conversion rate for AR-enabled products increases from 4.2% to 42% KR2. Return rate drops from 42% to 4.2% |
27. Emotionally Aware Smart Home
| 1 | Bad Version | Good/ Better Version |
| Objective: | Add mood-sensing to home assistant | Users feel more emotionally supported by their environment at home |
| Key Results: |
KR1. Integrate sensors KR2. Add emotion or mood presets |
KR1. Percentage of users reporting mood improvement post-interaction increases from 42% to 84% KR2. Percentage of repeat use for mood or emotional features increases from 4.2% to 42% |
28. Digital Afterlife Management
| 1 | Bad Version | Good/ Better Version |
| Objective: | Create memorial chatbot | Loved ones feel emotionally connected and supported through and by digital memories |
| Key Results: |
KR1. launch chatbot KR2. Add voice playback |
KR1. Average emotional connection score (user feedback) increases from 4.2 to 8.4 out of 10 KR2. Percentage of users with 30 day repeat usage increases from 4.2% to 42% |
29. AI Rights Policy Team
| 1 | Bad Version | Good/ Better Version |
| Objective: | Draft policy on sentient AI | Institutions now act responsibly when managing emerging machine intelligence |
| Key Results: |
KR1. Write policy KR2. Hold debate in parliament |
KR1. Percentage of institutions complying with AI rights charter increases from 0.42% to 42% KR2. Public support for ethical AI management rises from 42% to 84% |
30. Space Mining Operations Team
| 1 | Bad Version | Good/ Better Version |
| Objective: | Extract minerals from asteroid belt | Earth-based industries access rare space materials at lower cost and more reliably |
| Key Results: |
KR1. Land all autonomous drills KR2. Collect quality ore |
KR1. Cost per unit of rare metal on Earth drops by 42% KR2. Number of industries switching from Earth-based to space-sourced supply increases from 4.2 to 42 |
Why: Mining alone isn’t an outcome; the change and outcome is when Earth-based industries rely on the new space supply chane as a result of better mining.
31. Full Body VR Experience
| 1 | Bad Version | Good/ Better Version |
| Objective: | Deliver new haptic suit and immersion SDK | Users feel physically present and emotionally connected during virtual experiences |
| Key Results: |
KR1. Deliver the suit and SDK KR2. Sign partnerships with game studios |
KR1. Percentage of users reporting interruption of virtual immersion decreases from 42% to 4.2% KR2. Average session length in VR with haptic suite increases from 42 minutes to 84 minutes |
Why: Users engaging longer and feeling more immersed reflects behavioral change.
32. Personal Cybernetic Augmentation Team
| 1 | Bad Version | Good/ Better Version |
| Objective: | Launch new neural-linked prosthetic | Users regain or enhance physical functionality in everyday life without friction |
| Key Results: |
KR1. Finalize firmware KR2. Units are present in stores |
KR1. Percentage of users independently performing complex daily tasks with a prosthetic increases from 4.2% to 42% KR2. Average daily usage of prosthetic for complex tasks increases from 4.2 hours to 8.4 hours |
Why: the better and stronger outcome isn’t the delivery per se but the quality of life improvements and independence
33. Extraplanetary Terraforming Operations Team
| 1 | Bad Version | Good/ Better Version |
| Objective: | Begin atmospheric engineering on Mars | Mars Settlers perform more tasks and projects with less artificial ecosystem support |
| Key Results: |
KR1. Launch CO2 in jectors KR2. Test soil hydration module |
KR1. Percentage of settlement projects proceeding without habitat domes increases from 4.2% to 42% KR2. Reduce the need of artificial life support infrastructure due to atmosphere terraforming from by 42 from 420. |
Why: tech effort is important, but the actual outcome lies in what people are able to do without artificial support on Mars.
34. Intergalactic Law & Treaty Team
| 1 | Bad Version | Good/ Better Version |
| Objective: | Draft unified space law protocols | All civilizations signing the treaty can resolve disputes through a common legal framework |
| Key Results: |
KR1. Finalize the main clauses KR2. Translate protocol to all intergalactic languages |
KR1. Number of conflict resolutions citing the new legal framework increases from 0 to 42.000 KR2. Percentage of intergalactic lawyers reporting high confidence in fair dispute mechanisms increases from 42% to 84%. |
Why: Treaties drive outcomes when people use them to change their behaviour and the way they resolve disagreements through them.
As you can see, regardless of the team, theme or industry, there are plenty of OKR examples of how to define strong targets and outcomes that drive meaningful progress, but also ways in which we sabotage ourselves or don’t get close enough to the actual change and impact we hope to drive.
For what other teams or industries should I provide OKR examples? Leave a comment below!
Also, do check out my workshops page if you want a private in-house OKR training for your teams and also my FREE introductory course on OKRs.
Looking to spark change in your product journey? Here’s how I can support you:
- Product Management & Leadership Coaching – I help leaders grow their confidence, lead despite uncertainty, focus on delivering value and create real impact for their teams, users and company.
- Outcome Thinking & OKRs – Backed by hands-on experience in both using and rolling out OKRs across multiple teams and companies, I help you roll out, improve or fix your OKRs or OKR system and make changes and improvements that stick beyond the start of the cycle.
- Facilitation With a Twist – I create, facilitate and moderate workshops that energize the room and foster stronger engagement and learning – all through my crazy, fun and playful approach.
- GenAI for Product Teams – Practical, hands-on workshops that give teams and leaders the skills to harness AI and GenAI for their everyday goals and way of working, but also the time to reflect as a group on the impact that this AI wave brings.
Recent Comments