The Al-Kindi Society of Engineers hosted a workshop in London examining whether data has overtaken algorithms as the primary driver of artificial intelligence advancement, a question with direct consequences for smart energy and grid modernisation. Professor Diaa Al-Jumeily of Liverpool John Moores University led the session, arguing that the accelerating shift toward data-centric model development is reshaping how intelligent systems are built and deployed. Dr. Hossam Ali Hadi, a renewable energy engineering specialist focused on smart grids, attended to assess the implications for his field. The event underscored that continuous technical upskilling has moved from optional to essential for engineers operating at the intersection of AI and energy infrastructure.
The debate over data versus algorithms reflects a broader inflection point in applied AI. For years, algorithmic novelty — new architectures, optimisation techniques, and training paradigms — dominated research agendas and commercial differentiation. Today, the marginal gains from architectural tweaks are diminishing while the availability of high-quality, domain-specific datasets has become the binding constraint on model performance. In power systems, this translates to a practical reality: a grid-balancing model trained on rich, granular SCADA and smart-meter data will outperform a theoretically superior algorithm fed on sparse or noisy inputs. The workshop’s emphasis aligns with what energy operators are already experiencing — data readiness, governance, and interoperability are now the primary levers for unlocking AI value in asset management, forecasting, and real-time control.
For the energy sector, the implications extend beyond model accuracy. Regulatory frameworks, market designs, and cybersecurity postures all assume data environments that are often fragmented, proprietary, or legacy-bound. The shift toward data-centric AI demands parallel investment in data infrastructure: unified ontologies, secure sharing protocols, and quality assurance pipelines that span generation, transmission, distribution, and behind-the-meter assets. Engineers like Dr. Hadi, who bridge power systems expertise with data science fluency, are becoming scarce strategic assets. Their ability to translate grid physics into labelled training sets, and to validate model outputs against operational constraints, determines whether AI deployments remain pilot projects or scale into operational backbone systems.
The Al-Kindi Society’s role in convening this discussion highlights the value of professional engineering bodies as knowledge bridges between academic research and industrial practice. By bringing Liverpool John Moores’ latest perspectives to a London audience of practising engineers, the society accelerates the diffusion of relevant insights into the UK and Middle Eastern energy engineering communities. As the sector navigates decarbonisation, decentralisation, and digitalisation simultaneously, forums that distil academic rigour into actionable guidance for continuous professional development are not merely beneficial — they are infrastructure in their own right.
Read the full report at Energy Central