Cisco Field CTO Points To Cultural And Data Readiness As Prerequisites For AI Infrastructure

The views and opinions expressed are those of Bogdan Muraru and do not represent the official policy or position of any organization.

Many enterprise leaders are trying to chart a clear path for AI adoption while building infrastructure that supports both autonomy and compliance. In the process, they're running into a familiar reality. The long-standing challenges of cloud migration, data governance, and infrastructure modernization never went away. The surge in demand for AI capabilities is now magnifying these unresolved issues and forcing organizations to accelerate technology roadmaps that were already under pressure.

Bogdan Muraru brings more than twenty years of hands-on experience shaping enterprise technology strategy across global organizations. Now a Field CTO at Cisco, his background includes senior work with companies such as GSK, WPP, and Ericsson. Muraru has guided enterprises through cloud modernization, ITSM transformation, and the architectural decisions that support secure, data-driven operations, and in his view, leaders can’t pursue the promise of AI until they address a more fundamental, human-centric challenge.

"Before companies can actually make use of AI, they need to invest a lot more in creating the skills, the knowledge, and the capabilities for their organization to work with data and to put data to use," says Muraru. He explained that when an organization begins to see its data as a core strategic asset—its intellectual property—the need for data sovereignty can transform into a C-suite priority. From this perspective, establishing a foundational data culture is a prerequisite to purchasing any tools or deploying any models.

• Getting it right: "The required culture is about having the right data at the right time for the right reasons, with the right people," explained Muraru. That readiness depends on a disciplined approach to both data and process. "You need to curate data; you need to understand what data you have. A key skill, which has been crucial for the last decade, is automation. That's where we need to keep investing, so we understand how to automate our processes and then how to use AI to accelerate that automation."

• Smarter, not bigger: It's a mindset that can help leaders address the skills gap with a more strategic and sustainable approach. The result is a virtuous cycle that empowers the existing workforce with AI tools that make them more productive, agile, and innovative. "I don't think we should fight the skills gap just by adding more and more people. I think we really need to understand how to put the tools to practice."