Lee Rorison is Founder and CEO of Seriös Group, where he leads the company’s approach to delivering data and AI solutions focused on speed, accessibility and measurable business outcomes. His career began in ERP data solutions and business intelligence consulting, building data platforms and warehouses for large enterprises as part of major transformation programmes.
Over time, Lee became increasingly frustrated with the inefficiencies of traditional delivery models, characterised by fragmented vendors, high resource costs and limited focus on tangible outcomes. This experience shaped his perspective that data and AI initiatives should be faster to deliver, more accessible across organisations and grounded in clear business value from the outset.
In 2020, Lee founded Seriös Group around an outcome-first, technology-agnostic philosophy. As the company evolved, these principles led to the development of Seriös ONE, an AI-driven data solution designed to automate foundational work and enable teams to focus on engineering quality and business impact.
Working closely with a range of organisations, Lee has reinforced his view that AI capability is fundamentally dependent on strong data foundations. He leads with a pragmatic approach, ensuring that delivery remains aligned to organisational priorities and that data initiatives consistently translate into measurable results.
Lee’s leadership reflects a focus on simplifying complexity, accelerating value realisation and embedding data and AI into the core of how businesses operate.
How do you expect the data and AI leadership role to evolve over the next 12–24 months?
“Over the next 12–24 months, I expect the data and AI leadership role to shift from experimentation toward disciplined value delivery. The initial surge of enthusiasm around AI has created significant opportunity, but also noise. A key responsibility will be to balance innovation with realism, ensuring that AI initiatives are grounded in strong data foundations, clear governance, and measurable business outcomes.
“For me, the role increasingly centers on three priorities. First, strengthening core data capabilities: high-quality, well-governed, and accessible data remains the prerequisite for any scalable AI. Without this foundation, even the most advanced models cannot deliver reliable value.
“Second, guiding the organisation through the AI hype cycle. Data and AI leaders must act as pragmatic stewards, helping teams distinguish between promising use cases and costly distractions, while setting realistic expectations about what AI can and cannot do.
“Third, embedding AI responsibly into products, operations, and decision-making. This means focusing on targeted, high-impact applications, building repeatable and metadata driven delivery frameworks, and ensuring appropriate oversight around risk, ethics and security.
“Ultimately, the role evolves from championing technology to orchestrating sustainable impact, where strong data foundations and disciplined AI adoption drive lasting business value.”
Can you share a data and AI initiative you’ve led that you’re particularly proud of?
“I’m particularly proud of leading on the development of our Metadata-Driven, AI-Native Data Operating System, which we created to fundamentally change how modern data platforms are built and operated. The challenge we saw across many organisations was that data platforms were still being engineered through large volumes of manual code, fragmented tooling and inconsistent governance, making them difficult to scale and increasingly expensive to maintain. We addressed this by designing a framework that defines platforms through structured metadata and configuration rather than code, enabling pipelines, governance rules, quality checks and documentation to be generated and managed automatically.
“The initiative was delivered through a combination of architectural design, internal platform engineering and iterative deployment across our customer delivery projects. We worked closely with engineering teams to embed the framework into real-world platform builds on technologies such as Databricks, Snowflake and Fabric, ensuring it could operate across multiple cloud and data environments while remaining GitOps-driven and developer friendly.
“The outcome has been a framework that significantly improves delivery speed, consistency and governance while creating a strong foundation for AI-driven automation. It has also become a core part of our company strategy, enabling us to deliver autonomous, metadata-driven data platforms that can increasingly optimise and operate themselves.”
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
“Effective data and AI leadership requires a combination of strategic vision, strong technical understanding, and the ability to simplify complex problems into scalable operating models. Data and AI initiatives often fail not because the technology is inadequate, but because organisations struggle to translate ambition into practical, repeatable ways of working. Leaders therefore need to be able to see beyond individual tools or projects and focus on foundational architecture, long-term operating models, and sustainable delivery practices.
“In my organisation, the most influential traits have been clarity of vision, technical curiosity, and a strong bias toward building structured foundations before scaling capability. As AI adoption accelerates, it becomes even more important that organisations have well-governed, well-structured data platforms that AI systems can rely on. That belief has been central to the direction we’ve taken with our metadata-driven, AI-native data operating system.
“Equally important is the ability to bring people with you on that journey. Data transformation is as much about culture and operating models as it is about technology. By clearly articulating the shift toward metadata-driven engineering and aligning our teams around that vision, we’ve been able to evolve how we deliver platforms while positioning the business strongly for the next generation of AI-enabled data ecosystems.
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 aiming for the C-suite?
“One non-traditional piece of advice I would give an aspiring data or AI leader is to stay genuinely open-minded and say yes to opportunities that sit outside your planned path. Careers in this space rarely follow a straight line, and many of the experiences that shape strong leaders come from unexpected roles, projects, or industries.
“Early in my career, the moments that accelerated my growth were not always the ones that looked most relevant at the time. Taking on unfamiliar challenges exposes you to new ways of thinking, different business problems, and diverse teams and skills that are essential for leadership but hard to learn from technical work alone.
“Being open to opportunities also builds adaptability and curiosity, both critical in a field evolving as quickly as data and AI. Technical expertise matters, but the ability to learn from every experience and connect ideas across domains often becomes the real differentiator in leadership.”