businessinfomag.uk magazine 32 gains. Tools like Glean, Claude and ChatGPT proved their value by helping employees be more productive, make smarter decisions and work faster through rapid agent development and propagation. However, the differences between generative AI solutions is going to materially shrink as vendors aggressively innovate and compete. Today, many large platforms such as Salesforce or Outreach are still developing their agentic capabilities, which increases the attraction of generative AI and point solutions like Momentum, Rox and Granola. However, the big platforms will rapidly increase their agentic capabilities through 2026, which will drive a pivot back to leveraging platforms to simplify and agentify user workflows, leveraging the power of integrated data sets to fuel platform AI capabilities across workflows. availability to fundamentally undermining trust is one of the most dangerous evolutions in cybercrime this decade, requiring organisations to immediately invest in AI governance and data lineage integrity controls. Dan Carpenter CIO, Amplitude Platform AI will surpass point solutions In the last 12 months, generative AI delivered real productivity conflicting business rules, complex identity and permissioning models and non-deterministic behaviour leading to unpredictable outcomes. Most autonomous agents will need tight orchestration layers and human-in-the-loop controls. In other words, they’ll need new platforms. Autonomy only works in fantasy. It’s orchestration that wins in reality. Andy Syrewicze Security Evangelist, Hornetsecurity Ransomware shifts from encryption to data integrity manipulation Threat actors are now no longer focused solely on encrypting data for access, but also on compromising its integrity by subtly altering, corrupting or falsifying records to weaponise mistrust. This shift from disrupting AI PREDICTIONS …continued Simone Larsson EMEA Head of Enterprise AI, Lenovo ISG Every watt matters: power becomes the first design pinciple In 2026, energy will overtake compute as the primary design constraint for AI infrastructure across EMEA. Europe’s grid systems remain under significant strain, with the International Energy Agency projecting continued electricity demand growth and persistent price volatility through 2026. At the same time, organisations are approaching ambitious sustainability commitments set pre-pandemic, forcing CIOs to treat energy not as an operational cost, but as a strategic limitation. Every watt now matters. This shift will fundamentally redirect infrastructure strategy. Data centre planning will begin with energy availability, efficiency and location, not server density. Power-aware design encompassing low-footprint systems, advanced cooling and intelligent workload placement will become essential, particularly in secondary markets and edge locations with limited grid capacity. Regions with favourable energy profiles, such as the Nordics with its abundant renewables, will continue to attract AI investment, while Southern and Eastern Europe will accelerate innovation in colocation and micro-grid development. Across the Middle East and Africa, hybrid and on-site generation models will move from experimental to mainstream as enterprises seek to stabilise and scale AI operations. As compute demand accelerates faster than utilities can expand capacity, energy access becomes the new competitive differentiator. Colocation facilities will shift to the centre of AI deployment thanks to their proximity to renewable clusters, high-density rack support and scalable interconnects. Decisions across the infrastructure stack (hardware, cooling, network architecture and workload placement) will increasingly revolve around power availability, efficiency and compliance. In 2026, EMEA’s AI leaders will be those who design for energy adaptability from the start, gaining speed, resilience and regulatory confidence in an increasingly power-constrained world. Inference takes centre stage as AI moves to where the data lives The AI landscape across EMEA will undergo a fundamental shift: the centre of gravity will move from training massive models to running and refining them at scale through inference. As organisations deploy AI deeper into their operations, a powerful data-gravity effect will take hold, pulling compute closer to the point where data is generated, regulated and consumed. For EMEA, this trend is amplified by the region’s unique combination of stringent data sovereignty requirements, cross-border regulatory fragmentation and highly distributed markets. Enterprises will increasingly rethink infrastructure placement, moving away from centralised training environments toward edge and neardata centre deployments that keep sensitive information within national or regional boundaries. Inference will become the dominant workload, driving demand for distributed, efficient and complianceaware infrastructure capable of delivering real-time insight. In sectors such as manufacturing, retail, financial services and public administration, all of which rely heavily on local data, this shift will unlock faster decision-making, better responsiveness and greater operational resilience. In 2026, EMEA’s competitive advantage will come not from building the biggest models, but from deploying intelligence exactly where it creates the most value: at the edge, close to the data and deeply integrated into everyday operations.
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