1. Mathematics First
Dr Eva’s perspective on AI originates in building systems and understanding how complex systems behave, rather than entering the discipline through policy, commentary, or corporate communications.
Trained as a mathematician, she earned her doctorate developing AI algorithms for medical diagnostics. Her early work analysed complex biological and radiological data where signal-to-noise ratios were punishing, stakes were clinical, and the mathematics had to prove itself against physical reality. That foundation established an enduring standard: models had to be testable, evidenced and robust enough to operate under strict real-world constraints.
2. Building Production AI Before the Boom
By 2013, Dr Eva was already building dedicated Data Science and AI capability and production systems, years before the current wave of popular interest in artificial intelligence. At the time, enterprise AI had no standard playbooks or off-the-shelf deployment infrastructure.
Her team built production systems under intense commercial pressure, developing the practical disciplines of data pipeline integrity, drift monitoring, algorithm robustness, and operational control that later came to define mature enterprise machine learning.
3. From Models to Enterprise Systems
As organisations began exploring AI at scale, Dr Eva’s work expanded from models and production data science into the architecture and execution of large-scale enterprise transformation.
She served as Chief Data Scientist at KPMG London, building data science and analytics capability across risk, audit, advisory and financial-services environments.
She later joined IBM, becoming CTO for AI for the Middle East & Africa. There she worked at the intersection of technical AI strategy, architecture and enterprise implementation across one of the world’s most operationally diverse regions.
She then joined EY as Lead Partner for Data & AI in MENA, building and leading the regional practice and working with governments, sovereign institutions and major enterprises on large-scale AI and data transformation.
4. High-Consequence Environments
Operational condition: Experience across environments where AI is part of systems in which reliability, security, accountability and operational control materially matter — including government, banking, public sector, and intelligence-related contexts.
Throughout her career, Dr Eva has repeatedly operated in settings with very low tolerance for failure. In these environments, an algorithm error or unchecked bias does not result in a minor customer service complaint; it carries sovereign, financial, security, or human consequences.
This work crystallised her understanding that AI rarely fails because of purely theoretical model deficits. It fails because of the organisational vacuum around the model: weak data contracts, distorted operational incentives, poor system integration, and the absence of clear operational accountability.
5. Executive and Institutional Experience
Translating deep technology into clear choices for boards, prime ministers, cabinet offices, and enterprise C-suites became a natural extension of her implementation career.
Dr Eva serves on the boards of international non-profits including The Future Society, advancing sovereign AI capability and global governance frameworks, and the PVBLIC Foundation, mobilising media and data technologies for sustainable development. She frequently briefs cabinet ministers, regulators, and audit committees on how to maintain operational control over automated and autonomous decision systems.
6. Still Operating
Dr Eva does not advise from the sidelines. She remains an active AI operator leading hands-on enterprise implementation.
Through The Hummingbird Group, she works directly with leadership and technical teams on AI transformation, architecture, operating capability and implementation.
The Group’s AI consulting lineage dates to 2013. Its operating model focuses on creating permanent institutional capability — helping organisations own, operate and sustain their AI rather than remaining dependent on large external delivery teams.
7. Current Intellectual Work
Her forthcoming book, Decision Quality AI: From AI Ambition to Operational Success, is now available for pre-order. The book addresses why so many technically viable AI projects fail to become successful operating capabilities inside real organisations. Drawing on more than a decade of frontline delivery, the work outlines a rigorous approach for organisations moving from experimental ambition into dependable, evidenced production.
Alongside her active delivery work, the Dr Eva Institute develops research, executive education, and rigorous methods around AI, evidence, organisational capability, and consequential decision-making.
Connect with Dr Eva directly on LinkedIn for executive essays and institutional analysis. Selected event and keynote photography is archived on Instagram.