The IT workplace: foundation for productivity

Most organizations in 2026 depend on a digital workplace. From Microsoft 365 and Google Workspace to sector-specific tools: communication and collaboration take place digitally. A structural pain point has already become visible in the decline of global engagement from 23% in 2023 to 21% in 2024 – a trend break that explains the current urgency for better insight. What stands out is that the engagement among managers has significantly decreased (from 30% to 27%). This decrease directly impacts overall productivity and the extent to which work performance can be structurally predicted.

There are plenty of tools available, but adoption often remains the bottleneck. If employees find it difficult to locate information or perceive applications as cumbersome, their effectiveness declines. Technology alone is therefore not enough. What matters is: insight into how people work – and why.

From assumptions to insight: teams make work agreements measurable with data from the digital workplace.

From feeling to actual use

Many organizations still rely on 'gut feeling' to assess collaboration and productivity. Is location X as digital as location Y? Does team A adhere to the agreed tools, or does it unconsciously revert to outdated working methods?

In 2026, this can be improved. Through data analysis of application usage (such as Microsoft Teams, Slack, or OneDrive), you gain insight into behavior and bottlenecks. Is Teams used for collaboration – or just as a video calling platform? Is information stored centrally, or are Word files still sent via email?

And in 2026, this is not only smarter but also easier to justify: on August 2, 2026, the majority of the EU AI Act rules will apply and enforcement will begin. Then the transparency rules (Article 50) will also come into play, and high-risk applications (Annex III) will become more relevant – including AI that monitors or evaluates employee performance or behavior. By clearly defining which workplace data you measure, why you do it, and who has access to it, you prevent discussions later on and make your analyses more defensible. This way, you can also predict work performance more accurately based on actual signals, without it feeling like micromanagement.

AI as co-pilot: predicting work performance in real-time

Thanks to AI tools in modern workplaces, a new layer emerges: proactive support. Think of:
 

• Automatic content recommendations based on your project context
• Smart notifications that signal overload
• Body doubling features in virtual environments that increase focus
• AI-generated dashboards that monitor team productivity and even predict work performance in real-time

Modern platforms like Microsoft 365 Copilot and Gemini for Google Workspace assist users by summarizing tasks, preparing emails, and even generating meeting points. This lowers barriers, increases speed, and helps employees stay focused – and demonstrates the growing impact of AI in the workplace.

From insight to action: what works?

Data only becomes valuable when you translate it into policy, training, and automation – with clear agreements about ownership and use.

Managing productivity starts with insight into the workflow, but succeeds only when those insights are translated into targeted action. This can be done on three levels:
 

1. Policy – adjust your guidelines based on actual user data
2. Training – offer customization where adoption lags (not generic)
3. Automation – bring relevant information to the user, rather than the other way around. This way, you can not only adjust but also recognize patterns earlier and predict work performance more accurately at the team or process level.

For example: if analysis shows that 80% of the questions to the HR department are about the same topic, you can automate that in Teams or Slack with an AI bot.

Hybrid working and predictability

Hybrid working is now standard. But that does not automatically mean higher productivity. What does work:
 

• Hybrid working does not have to harm productivity: in a large randomized study, hybrid working had no negative effect on performance, while the quit rate decreased by about a third – especially among non-managers (and among employees with long commutes). That is exactly the kind of predictability organizations are looking for in 2026.
• Data-driven insight prevents mutual frustration and misunderstandings between teams
• Clear digital rules (e.g., where information is stored) improve work speed

A predictable workplace is not a control model – it is an environment where employees know what to expect, supported by tools that sense what they need. It also helps to make hybrid workplaces predictable through tools that provide insight into work behavior.

Conclusion

In 2026, productivity is no longer just a matter of working harder. The combination of AI, behavioral data, and smart automation makes it possible to manage work happiness, effectiveness, and predictability, by better substantiating and – where appropriate – predicting work performance at scale.

Organizations that actively monitor and optimize their digital workplace turn insight into an advantage – and elevate their productivity to the next level. Those who have their data, tools, and governance in order can also predict work performance more reliably and proactively manage it. A people-first approach remains crucial, especially because AI in 2026 "can do more" than many teams have already arranged in terms of adoption and agreements.