AI is giving us hours back each week — but unless companies rethink workflows, much of that time just slips through the cracks.
In the age of AI-powered productivity tools — from digital assistants and copilots to dialogue-based systems such as ChatGPT — it stands to reason that we are on the cusp of a revolution in the world of work. These tools are designed to help us create content faster, communicate more efficiently, and solve problems more intelligently.
But as many companies are now realizing, “working smarter” does not automatically mean “working less” — nor does it necessarily lead to measurable economic success.
The reality of “time saved”
In our private lives, productivity gains can be converted into “quality time” — for sports, recreation, or hobbies, for example. In the business world, however, the time saved by AI does not necessarily lead to more work being done. Instead, it often results in longer coffee breaks, more idle time, or simply a lower workload.
This phenomenon is called productivity leakage: efficiency gains at the individual level do not translate into clear business value. Part of the challenge is that most companies do not track individual productivity gains — either for data protection reasons or because it is too difficult to monitor tool usage without destroying trust or violating regulatory boundaries.
According to a study by BCG, 82 percent of consultants who regularly use generative artificial intelligence (GenAI) feel more confident in their role and believe that their colleagues also value the technology. Over 80 percent agreed that GenAI improves their problem-solving skills and leads to faster results. But the key question remains: Does this lead to real organizational efficiency — or just individual relief?
What the numbers really say
According to Gartner’s 2025 CEO and Senior Business Executive Survey, growth remains the top strategic priority for 56 percent of CEOs. AI is seen as a key lever in this regard — but perhaps in a different way than is often assumed.
Gartner data shows that while the use of AI saves an average of 5.7 hours per employee per week, only 1.7 hours of that time is spent on high-value work that improves results. Another 0.8 hours are spent correcting AI errors. And the rest? That is often simply not traceable.
The results are consistent with a recent Microsoft survey, which found that only 34 percent of CEOs expect GenAI to increase productivity, while 43 percent are placing greater emphasis on better decision-making. This points to a shift in management mindset: instead of focusing on every minute of work saved, the impact is increasingly being prioritized over the activity.
When productivity gains bring real business value
Despite all the skepticism, teams that achieved high productivity through AI report clear benefits, according to Gartner:
- 81 percent achieved significant cost savings at the enterprise level — 27 percent more than less productive peer groups.
- 71 percent report better innovation performance, for example, through novel products and offerings.
However, not all areas of the company are making full use of AI. According to Gartner, around 60 percent of finance employees continue to rely on manual processes — either because they distrust AI or because they are used to established methods.
To close the gap between individual productivity and business value, managers should consider the following:
- Measure the right metrics. Don’t just track the time saved, but also analyze how productivity tools are being used — and link that usage to KPIs at the team and individual levels.
- Evaluate business results. Instead of monitoring every AI interaction, you should rather check whether quality, speed, or business results have improved. For example, has GenAI helped sales achieve more deals? Have development cycles been shortened?
- Redesign processes to be AI-friendly. Writing emails, creating reports, or evaluating operational data should be specifically tailored to the use of AI. Without process redesign, automation often remains superficial. The goal is to control AI workflows, minimize risks, and ensure alignment with business objectives.
- Provide training and develop skills. Simply using AI is not enough. The BCG study shows that even for tasks that do not require programming, people with some coding experience performed better than beginners. Contextual knowledge and experience, therefore, increase the effectiveness of AI.
- Redefining productivity. Resist the temptation to fill every minute gained with additional work or to reduce staff. If AI frees up five hours per week, these could be used for creativity, reflection, or innovation. If productivity gains exceed expectations, KPIs, workflows, and team structures should be adjusted — and the process repeated.
Jan Burian is the head of industry insights at Trask, where he is responsible for overseeing and executing the go-to-market strategy from a marketing perspective, with a primary focus on the DACH region, particularly Germany. Previously, Jan was associate VP and head of manufacturing insights EMEA for IDC, and a management consultant in manufacturing environment for EY.
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