Building the Workforce and Culture for the Future

Editor’s Note: This blog is adapted from a talk by Anupam Chaturvedi, Head of Zeiss Digital Partners, at thegeekconf 2025. Leading the digital organization within the Zeiss Group, Anupam explores the critical shift from predictive analytics to the generative AI era. His session focuses on the dual challenge of addressing job displacement fears while building a talent-rich, future-ready culture that treats AI as a long-term journey rather than a short-term sprint.

Hi, I am Anupam Chaturvedi, and like many of you, I am navigating this massive transition into the generative AI era. At Zeiss, we are moving beyond simple predictive models to explore how these technologies fundamentally change the way we work. We all read the headlines about AI replacing humans, and the World Economic Forum recently noted that 85 million jobs might be replaced. However, as an organization in the middle of this shift, I see the other side of the coin. We are desperately looking for talent that can build these solutions and a culture that can sustain them.

The talent gap is not a myth. It is a very real challenge that organization leaders face every day. While millions of roles may be displaced, the World Economic Forum also estimates that 97 million new roles will emerge, specifically aligned with data and AI. This gap is no longer limited to software engineering; we are seeing a skill deficit across all functions: marketing, sales, operations, and finance.

Over the past two years, I have learned that upskilling internally can beat hiring alone. You cannot simply go out and hire hundreds of AI engineers from scratch to make your organization ready. Instead, we focus on curated learning paths and project-based experiential learning. In my organization, we have created "citizen AI practitioners" across different functions.

Building a workforce is about more than just technology. It requires a solid foundation in culture, infrastructure, and governance. The first pillar is cultural readiness, specifically psychological safety. When a finance professional hears about AI automation, their first thought is often "will I lose my job?" We must work to provide them with approved tools and skills, so they see AI as an enabler rather than a threat.

Infrastructure is the second pillar. Successful AI implementation is impossible without a robust data foundation or a semantic layer. Many organizations attempt to deploy AI agents while still struggling with their data foundations. However, these agents are only as effective as the data supporting them. We must invest in data landing zones and governance to ensure our results are accurate and measurable. This foundation reduces frustration for both the management and the developers.

Leadership serves as the bridge for this entire transformation. I believe the single biggest way to impact an organization is to focus on leadership development. Leaders must champion AI by modeling the right behaviors and ensuring every project aligns with business goals. This starts with a long-term strategy, typically a 12 to 24-month plan, rather than a short sprint.

We establish a clear operating model to avoid confusion. At Zeiss, we use a digital council that sits with stakeholders to understand their needs. This council is supported by a Center of Excellence (COE) team that provides the platforms and tools, while communities of practice help people learn. This three-layer structure ensures that everyone knows where to go for help and who is responsible for which part of the AI journey.