Your next boss may not be just a person with a corner office. In some companies, decision-making is beginning to rely on something less visible that is automated systems built to think through problems and offer answers without human hesitation.
Recent industry analysis from Gartner suggests that artificial intelligence is becoming more than just a tool in the background. If current trends continue, by the middle of this decade, around half of the important choices made inside companies could be influenced or directly completed by AI. This shift is not simply about speed, but about how information is processed, evaluated, and turned into action.
Where AI is handled properly, executives may find they can respond faster to change and manage resources more effectively. But where it’s deployed without proper oversight or alignment with business goals, the consequences could be harder to manage. Mistakes at scale are not just expensive, they can be hard to reverse.
In practical terms, these AI agents act as a kind of middle layer between raw data and final decisions. They’re designed to pull in streams of information, assess them in real time, and guide leadership through the more complex layers of judgment. While they don’t remove the need for people, they do change how people approach strategy and planning.
Some firms have already begun to restructure how departments work together. Analysts and data teams are now expected to sit closer to management. Their role isn’t just to deliver charts, but to help shape what kind of questions get asked in the first place. AI becomes more useful when it’s matched with human judgment that knows where to focus.
Not every firm will get this balance right. In fact, the same forecasts warn that a large number of data leaders may fall short in managing the synthetic data they use to train and test models. This could introduce weak points in both accuracy and compliance, which in turn could affect broader business outcomes.
AI itself isn’t neutral. It reflects the quality of the data behind it and the rules set by those who deploy it. In the future, some company boards may even start to bring automated systems into their oversight processes. By the end of the decade, it’s expected that a portion of global boards will begin using AI to independently review and challenge high-stakes decisions made by executives. This doesn't replace accountability, but it reshapes where and how it's applied.
Meanwhile, a growing number of firms are considering whether to build their own generative AI systems instead of relying on external providers. Those who go that route often cite lower long-term costs and stronger control over how their systems evolve. But the choice also comes with increased pressure to understand the risks from the inside out.
The role of leadership is changing. It’s no longer enough to manage teams and review quarterly plans. Those in charge will need to understand what machines can do, where they fall short, and how to make the most of a future where decisions are no longer made in isolation.
Image: DIW-Aigen
Read next: Position Bias in AI Models Threatens Accuracy in High-Stakes Applications, MIT Warns
Recent industry analysis from Gartner suggests that artificial intelligence is becoming more than just a tool in the background. If current trends continue, by the middle of this decade, around half of the important choices made inside companies could be influenced or directly completed by AI. This shift is not simply about speed, but about how information is processed, evaluated, and turned into action.
Where AI is handled properly, executives may find they can respond faster to change and manage resources more effectively. But where it’s deployed without proper oversight or alignment with business goals, the consequences could be harder to manage. Mistakes at scale are not just expensive, they can be hard to reverse.
In practical terms, these AI agents act as a kind of middle layer between raw data and final decisions. They’re designed to pull in streams of information, assess them in real time, and guide leadership through the more complex layers of judgment. While they don’t remove the need for people, they do change how people approach strategy and planning.
Some firms have already begun to restructure how departments work together. Analysts and data teams are now expected to sit closer to management. Their role isn’t just to deliver charts, but to help shape what kind of questions get asked in the first place. AI becomes more useful when it’s matched with human judgment that knows where to focus.
Not every firm will get this balance right. In fact, the same forecasts warn that a large number of data leaders may fall short in managing the synthetic data they use to train and test models. This could introduce weak points in both accuracy and compliance, which in turn could affect broader business outcomes.
AI itself isn’t neutral. It reflects the quality of the data behind it and the rules set by those who deploy it. In the future, some company boards may even start to bring automated systems into their oversight processes. By the end of the decade, it’s expected that a portion of global boards will begin using AI to independently review and challenge high-stakes decisions made by executives. This doesn't replace accountability, but it reshapes where and how it's applied.
Meanwhile, a growing number of firms are considering whether to build their own generative AI systems instead of relying on external providers. Those who go that route often cite lower long-term costs and stronger control over how their systems evolve. But the choice also comes with increased pressure to understand the risks from the inside out.
The role of leadership is changing. It’s no longer enough to manage teams and review quarterly plans. Those in charge will need to understand what machines can do, where they fall short, and how to make the most of a future where decisions are no longer made in isolation.
Image: DIW-Aigen
Read next: Position Bias in AI Models Threatens Accuracy in High-Stakes Applications, MIT Warns
