As businesses invest more heavily in artificial intelligence, simply adopting the technology isn’t enough to prove it’s delivering meaningful value. Faster output or wider AI use may look like productivity gains on paper, but leaders also need to consider whether the technology is actually making work more efficient and sustainable for employees.

Understanding AI’s true impact requires looking beyond surface-level measures and examining how the technology is affecting day-to-day work across the organization. Below, members of Forbes Business Council share ways leaders can determine whether AI is genuinely improving productivity or simply contributing to a more exhausting workweek.

1. Check Whether The Workflow Is Already Established

The only time AI should ever be used is when we’re automating a workflow that we already have an SOP for. We should also already have a human doing it, too. If we’re trying to do something for the first time with AI, we’re doing it wrong. - Moe Alamawi, Momentum Academy

2. Assess How AI Changes Problem-Solving

Watch whether AI tools are actually changing how employees approach problems or are just speeding up the same old process. If teams are using it to test ideas they’d normally skip, explore edge cases or challenge their first instinct, it’s clearly a productivity gain. - Vaidotas Juknys, Decodo

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3. Track Error Reduction

Leaders should measure whether AI is reducing human errors, rework and repeated corrections, not just increasing output. If employees complete tasks more accurately, handle fewer escalations and spend less time fixing avoidable mistakes, AI is improving productivity. If output rises but errors and burnout remain, it is only accelerating the workload. - Nadia AlShomali, PersonalHour

4. Measure Total Working Hours

Look at total working hours, not per-task time. AI usually saves minutes inside a task—through things like writing a draft or summarizing a document—but adds hours around the task. This is because the same person now runs three parallel workflows they couldn’t before. If people are more productive but are working longer, AI is expanding the job, not shrinking the workload. Track the delta in end-of-day time, not per-task time. - Egor Karpovich, Travel Code Inc.

5. See Whether AI Gives Time Back

Leaders should look beyond how much work is getting done and ask whether AI is actually giving people time back. If output increases but hours, meetings and after-hours work stay the same or grow, AI may just be accelerating the workload. Real productivity means better results with less friction, not simply doing more in the same amount of time. - Nikolaus Kimla, Coevera

6. Measure Total Effort

Measure whether AI is reducing total effort, not just speeding up individual tasks. If employees finish work faster but face more reviews, corrections, context switching or after-hours cleanup, productivity has not truly improved. The clearest signal is sustainable output, along with better quality, shorter cycle times, fewer rework loops and no increase in employee fatigue or unnecessary workload. - Rawad Baroud, ZeroGPT

7. Determine Whether AI Removes Work

Look at whether AI is actually removing work, not just increasing output. If people are producing more but also spending more time reviewing, correcting and supervising AI-generated work, you may have created a new layer of operational overhead. The real test is whether AI gives people back time, focus and decision-making capacity. - Kobi Nissan, Mine

8. Compare Quality With Output

Focus on quality over quantity. If more work is completed but that work is riddled with errors, productivity isn’t improving. If AI allows employees to focus more on higher-value tasks instead of administrative work, productivity improves. However, if it creates more busy work for employees, it simply fosters an environment for increased burnout. - Jigar Thakkar, Longitude Rx

9. Track Returned Tasks And Extra Reviews

Assess how often tasks are returned, sent back or go through more review stages than before. AI improves task performance, but it can also create bottlenecks during review. Unverified or inaccurate AI output requires additional manual work by more senior employees, which drags down any productivity gains and can even increase costs. If the frequency of additional reviews and discussions has increased by a fair margin, it’s time to review AI usage practices. - Mindaugas Čaplinskas, IPRoyal

10. Measure Senior Review Burden

Measure review burden rather than generation volume. If AI tools simply produce more drafts, data and decks that require senior leaders to spend hours verifying, editing and policing accuracy, it is not saving time but shifting the cognitive load. Real productivity improves decision velocity; synthetic volume just creates operational fatigue. - Susana Cabrera, SC Growth Counsel

11. Track Capacity Gains

I’ve learned that AI productivity isn’t about how much more we can squeeze into a workday. My agency uses AI as a thought partner, which allows us to scale our tasks with a lean team. The real test, though, is capacity. Is AI removing repetitive work and creating more room for strategy, creativity and human connection? AI should create leverage, not a longer to-do list. - Karina Jimenez, Social-Kraft

12. See Where Saved Time Goes

Ask what team members are doing with the time AI saves. Is it creating space for higher-value thinking, better decisions, work that previously went undone, or even personal aspirations? That’s productivity. If people are simply producing more while working just as long and feeling more depleted, AI has increased output, not productivity. - Deirdre White, Pyxera Global

13. Measure Value, Not Volume

Measure productivity by value created, not volume produced. AI should create capacity by reducing low-value effort while improving the speed and quality of outcomes. If output rises but hours, complexity and workload rise with it, we haven’t gained productivity; we’ve simply accelerated the workweek. - Nachiketa Popat, EY

14. Tie AI Gains To Business Results

Productivity that never reaches the P&L isn’t productivity but redistribution. The honest test is naming one line item AI made cheaper, one cycle time it shortened and one hiring plan it changed. If the gains exist only in self-reported time savings and usage dashboards, the hours saved were silently reinvested as higher expectations, which is exactly why your people are exhausted. If individual speed rose and the workweek absorbed it, the company kept nothing. - Michael Riedl, Team Internet Group PLC

15. Check Whether AI Creates Higher-Value Capacity

Leaders should look beyond increased output. If AI saves time only for that time to be filled with more tasks or if teams become dependent on AI to think and create, the result may be greater exhaustion rather than true productivity. AI should create space for higher-value work while protecting the human creativity and original thinking essential to producing authentic, original work. - Joelle Machia, Wanderers Compass LLC

16. Benchmark AI Against Defined Goals

One way leaders can tell is by benchmarking AI’s impact against data-driven goals and clearly defined OKRs. Productivity should be measured through outcomes, not simply activity or hours worked. OKRs have been especially proven to help organizations connect AI adoption to measurable results, making it easier to track both outcomes and effort. If AI is delivering better results with the same or less effort, it’s creating value. - Syed Ahmad, DPL

17. Ask Employees Whether Work Feels Lighter

Ask your team one thing: “Do you feel less buried or more?” AI should be taking the boring, repetitive stuff off their plates so they can do the work they’re good at. If they’re just as slammed but now expected to produce more, you didn’t free them up. You just added more to their plate. - Barbara Schreihans, Your Tax Coach LLC

18. Compare Capacity And Cycle Time

Leaders should look at two measures: capacity and cycle time. Is the same team able to accomplish more without adding people or hours? Are employees able to complete work faster? At DHL, we’re seeing AI help teams increase their capacity while reducing the time it takes to deliver projects. That’s a better measure of productivity than simply asking whether people are using AI. - Greg Hewitt, DHL Express U.S.

19. Track Time Returned To Human Connection

Leaders can tell AI is truly improving productivity when it shifts a team’s focus from screens back to human connection. In healthcare and veterinary medicine, success means technology automates routine friction, allowing clinicians to build trust with people and pets. With tools like AI scribes absorbing administrative tasks, teams can bring their full energy and care to every patient and client interaction. - Nefertiti Greene, Mars Veterinary Health

20. Watch Whether AI Drives Action

Action, or the lack of it, is your answer. AI should clear the path and give you simple steps to follow, organize, systemize and automate. AI should save time, reduce tension and turn decisions into momentum rather than turning opinions into paragraphs. If it’s not automating action, it’s just creating clutter and roadblocks. Good AI use feels like ease and productivity. Bad AI use feels like confusion dressed up as overthinking. Watch what moves, not what stalls your team. - Laura Jayne Waters, Solar Buddies Ltd