Rethinking Work in the Age of AI 

Ravin Jesuthasan: After an initial wave of enthusiasm about genAI applications among many business leaders, a sense of realism is now setting in. As is often the case, we overestimate the short-term impact and underestimate the long-term potential of a technology. But we can only tap into this potential if we move away from the mindset that “AI is just another tool.”

Kai Anderson: AI is a groundbreaking technology, but it only unleashes its full potential when combined properly with human work. So, first and foremost, it requires a substantive discussion.

Ravin Jesuthasan: Companies often lead with the technology and ask what a particular solution can do. But that’s the wrong approach. The questions should rather be: What is the work currently being done or to be done and how can technology make it better or create previously un-envisioned offerings.

Kai Anderson: You need to break work down into its individual tasks and analyze which of them AI can replace, where it can provide support, and where it can create new possibilities.

Ravin Jesuthasan: That sounds simple, but it's challenging in practice. Companies need to learn to think in terms of tasks and skills—not jobs. A real-life example might help: In many companies, compliance staff spend a lot of time on standardized reporting. These reports are important, but the underlying tasks follow clear patterns and involve repetitive, rules-based verification processes.

Kai Anderson: This is exactly where there is high potential. With AI, these repetitive tasks can largely be automated—for example, with systems that automatically analyze data, flag anomalies, and prepare reports. The compliance professional then only needs to review the critical cases, develop risk assessments, and support strategic decisions. This not only creates efficiency—it also elevates the role.

Ravin Jesuthasan: This is a prime example of how the combination of technology and a new division of labor not only reduces costs but also enables higher-quality work. And that’s exactly our goal: to rethink work—meaningful, efficient, and future-ready.

Ravin Jesuthasan: Indeed, even though the business benefit of breaking down and redesigning work is obvious, many companies struggle to move past historically evolved, rigid job structures and the associated ways of thinking and working. Only through deconstruction, redeployment and reconstruction can work be sensibly redistributed—between humans, machines, or both.

Kai Anderson: At first, we often encounter very high expectations of AI—many hope for quick, breakthrough effects. In the past, companies would typically implement technologies without fundamentally questioning or adapting their workflows. When the expected results didn’t materialize, disappointment followed. But that’s often the point at which the real priority becomes clear: Technology doesn’t come first—understanding work does. Only when work is clearly structured and reimagined can AI reach its full potential.

Ravin Jesuthasan: It’s also important to keep the bigger picture in mind—that is, the entire process, end-to-end, including all stakeholders and adjacent or related technologies.

Ravin Jesuthasan: Many decision-makers in companies see generative AI as just a tool. But its true value emerges in the context of its specific application—how it affects and is affected by various stakeholders and can connect to other technologies, such as robotic process automation and machine learning. It’s this combination that holds the secret to realizing its full impact.

Kai Anderson: This also shows that companies must think of human labor and technology together.

Kai Anderson: Experience shows that these skills aren’t always found in the people who currently hold the roles. Sometimes it’s worthwhile to reassign tasks—to junior staff, for instance, or international teams.

Kai Anderson: This aspect should be considered even before implementing AI, especially since many new, previously overlooked areas of activity quickly emerge. Ideally, experienced employees gain the capacity to work on strategic tasks, more service-oriented customer communication, and targeted client acquisition. The gains in effectiveness are usually significant—often just as large as the savings from automation.

Ravin Jesuthasan: Clearly: Work design must become a core capability of the organization. It’s not enough to just have discrete use cases. There needs to be collaboration across all areas—from leadership to IT to HR. Only by continually asking: “How can we reinvent work to optimize human and machine capabilities?” can companies remain adaptable—especially given the rapid development of AI.

Kai Anderson: Yes, exactly—and this is nothing new. We just need to say it out loud and dispel the myth: You implement a technology once, and everything runs smoothly. No—now it actually gets exciting with regard to continuously improving AI usage. After the cost savings, the real performance gains begin.