Maria Zervou is Chief AI Officer for EMEA at Databricks, where she works with organisations across industries to accelerate the adoption and scaling of AI. Her career has evolved alongside the development of the data and AI industry itself, giving her a unique perspective on both technological innovation and organisational transformation.
Maria began her career as a data scientist and ML engineer, well before the recent surge in AI adoption and the emergence of today’s foundation models. Through multiple waves of innovation, from traditional ML to modern GenAI and agentic systems, Maria developed a consistent view of what drives success: solving real business problems, building on high-quality data, establishing clear ownership, and ensuring adoption.
During more than five years at Databricks, Maria has progressed through roles spanning Solutions Architecture, AI and Product Specialisation, and currently Chief AI Officer for EMEA. Working with organisations at different stages of AI maturity has reinforced her belief that the greatest barriers to success are often organisational rather than technical. She advocates for strong executive sponsorship, clear accountability, and defined ownership to ensure AI initiatives deliver measurable outcomes.
Maria is a strong proponent of continuous learning. She co-created VectorLab, a YouTube channel dedicated to AI, where she interviews practitioners, researchers, and product experts from across the industry. Through this work, Maria has strengthened her conviction that curiosity, knowledge sharing, and collaboration are essential to long-term success.
Her leadership philosophy centres on combining technical expertise with communication, trust, and the ability to bring people together to create lasting impact.
How do you expect the data and AI leadership role to evolve over the next 12–24 months?
“I expect data and AI leaders to evolve from technology sponsors into enterprise transformation leaders. The conversation is already shifting from ‘Which model should we use?’ to ‘How do we redesign processes, teams, and operating models around AI?’
“As organisations deploy agents and autonomous workflows, leaders will need to address governance, accountability, security, workforce transformation, and business value realisation. Success will depend on balancing innovation with trust, ensuring AI systems are governed, observable, and aligned with business objectives. I also expect data and AI leaders to spend more time with CEOs, CFOs, CHROs, and boards.
“AI is no longer a technology initiative; it is a business strategy. Discussions increasingly focus on productivity, operating margin, customer experience, risk management, and competitive advantage.
“Another major shift will be the importance of business context. While organisations have invested heavily in data platforms, agents require a clear understanding of business processes, definitions, policies, and decision-making frameworks. Data leaders will increasingly become custodians of organisational knowledge, not just data assets.
“Finally, AI economics will become a board-level discipline. Leaders will be expected to measure and optimise AI consumption demonstrating not only that AI works, but that it delivers sustainable value at scale.
“Ultimately, the role will evolve from managing data and models to orchestrating how humans, data, and AI systems work together at enterprise scale.”
Share a data or AI initiative you’ve led in your current role that you’re particularly proud of. Please outline what was done, how it was delivered, and the outcomes achieved.
“One initiative I am particularly proud of is creating and scaling the Databricks AI Roundtable programme across EMEA. As I engaged with executives across industries, I noticed a recurring theme. While organisations were investing heavily in AI, many leaders were asking the same questions:
- What are other companies doing?
- Which use cases are delivering value?
- How are they approaching governance and security?
- Why did they choose a particular architecture or operating model?
- Most importantly, what lessons have they learned from moving AI into production?
“To address this, I launched the Databricks AI Roundtable, a peer-led community bringing together CIOs, CDOs, CAIOs, and AI leaders to openly share experiences, challenges, and best practices. Rather than creating another vendor-led event, the objective was to create a trusted environment where leaders could learn directly from one another.
“The greatest success was seeing customers become advocates and educators for one another. The most valuable discussions were often those where customers shared why they made certain decisions, what they would do differently, and how they measured success.
“The programme reinforced a lesson I strongly believe in: customers learn best from other customers.”
Which traits and skills do you believe matter most for effective data and AI leadership? Which of these have been most influential in your organisation and why?
“Effective data and AI leadership requires technical credibility, business acumen, and the ability to drive change. Working for Databricks gives me a unique perspective because I see AI adoption across organisations, industries, and stages of maturity.
“The most successful leaders are not those focused solely on technology, but those who can connect AI initiatives to business outcomes and help organisations redesign workflows, operating models, and decision-making processes. In my experience, one trait I consistently see in successful leaders is the ability to recognise what they do not know. The pace of AI innovation means no individual or organisation has all the answers.
“The strongest leaders have a clear vision of the outcomes they want to achieve, but they are willing to partner closely with technology providers, peers, and experts to accelerate their journey. They are transparent about challenges, open to learning, and willing to leverage external expertise when needed.
“Personally, the skill that has been most influential in my role is the ability to build alignment across diverse groups.
“Externally, I work with executives, business leaders, and technical teams to help organisations move from experimentation to production. Internally, I collaborate across sales, field engineering, product management, engineering, and other teams to translate customer challenges into solutions, product priorities, and go-to-market strategy.
“I have also learned that meaningful AI transformation takes time. The most valuable initiatives often require process redesign, governance frameworks, and coordination across multiple teams. Slow progress does not mean no progress.”
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader?
“My advice would be to invest in understanding people before trying to influence them. Early in my career, I believed influence came from expertise.
“Over time, I realised that the biggest differentiator is the ability to understand how people think, what motivates them, what challenges they face, and what success means from their perspective. This starts with active listening. Whether I am working with a CEO, engineer, product leader, customer, or colleague, I spend more time understanding than explaining. The faster you can put yourself in someone else’s shoes, the faster you can build trust, align stakeholders, and help teams move forward together.
“Equally important is making people feel that you are genuinely trying to help them. People are far more willing to share challenges, ask difficult questions, and embrace change when they trust your intentions. Some of the strongest relationships I have built throughout my career started not with a solution, but with a willingness to listen and help.
“As AI becomes more capable, I believe this becomes even more important. Technology will continue to evolve, but leadership will remain a human skill. The leaders who create the greatest impact are those who understand people, build trust, and bring others with them on the journey.”