2026 is the year when organisational AI moves from experimentation to enterprise adoption and real value creation. In recent years, organisations have been dipping their toes into the AI pond with many running proofs of concept and pilots to try and gauge how AI might fit into their operations and deliver tangible results. With 2026 underway, and with the box about feasibility largely ticked, the focus now moves to how AI can be adopted across the enterprise.
Management teams across Europe are increasingly framing AI as an operational and governance consideration, rather than an innovation play. And the organisations that are going to make progress this year will be the ones which are decisive and can fit AI into their operating models, knowing where it does and doesn’t work. With this in mind, what are the big AI issues organisations need to consider in 2026? What should be on their radar?
Building AI oversight
In 2026, AI governance is expected to become a priority at the executive level, acting as a key determinant of whether organisations progress or stagnate in their AI ambitions. Successful pilots will need to be taken from development into the operating model. This introduces risks, as at this point, AI systems start to use real production data, interact directly with humans and even require changes to the operating model. Addressing and managing these risks and putting appropriate compliance structures in place – including monitoring and oversight – are crucial. And it’s particularly pertinent for European organisations that are preparing for the upcoming EU AI Act. Management teams need assurance that AI risks are identified and that they’re clearly owned and actively managed through accountability structures. Organisations that approach governance as an enabling capability in 2026 will be able to scale from AI experiments and POCs to AI at scale.
Scaling AI beyond pilots into production
As organisations look to scale AI beyond pilots into production, a clear productivity gap is expected to emerge between those that successfully operationalise AI and those that remain stuck in the experimentation phase. In parallel to AI oversight, the demand for the technical capabilities needed to run AI at scale is escalating. Production-grade AI also depends on a scalable AI architecture, secure and accessible data and a high level of reliability, as AI systems operate continuously rather than episodically. Also, robust operational monitoring and clear escalation procedures when AI systems behave unexpectedly are required to operate at scale. As a result, the role of AI and data engineers and architects has become more and more complex.
At the same time, there is growing realisation that progress in AI is not driven solely by value creation through individual use cases and business cases, but also by getting the fundamentals right, such as data quality and integration, identity and access management and mature MLOps and orchestration practices to keep AI models tightly managed. As a result, AI investment decisions this year will focus on the realisation of use case initiatives and strengthening the foundations needed for scalable, reliable AI in production.
Agentic AI needs careful orchestration
The concept of Agentic AI is rapidly moving from theory to practice, and some organisations have made a significant structural impact with Agentic AI. The underlying technology landscape is maturing quickly, with an expanding range of platforms that enable the safe orchestration, control and monitoring of AI agents. What was experimental only a very short time ago is now becoming increasingly viable for real organisational use.
Against this backdrop, 2026 marks a critical inflection point. For many organisations, this is the year to move beyond exploration and pilots and begin deliberately building agentic AI capabilities. That does not yet mean full-scale transformation, but it does require hands-on experience, architectural choices and early integration into technical capabilities, and, where appropriate, into operating models. The focus in 2026 is on capability building; the years that follow will be about transforming organisations with agentic AI to unlock significantly higher productivity.
In spite of this momentum, a cautious approach is necessary. As AI systems become more autonomous with Agentic AI, organisations must carefully consider risks related to security, compliance and control. As a result, most organisations are taking time to assess where autonomy adds value and under which conditions it can be deployed responsibly. This period of careful evaluation is not a sign of hesitation, but a prerequisite for sustainable adoption.
But a clear differentiation is starting to emerge. In 2026, organisations that manage to embed AI at scale, supported by sound AI oversight, a robust architecture and clear accountability, will begin to pull ahead of those that remain stuck in a perpetual pilot stage. With the right fundamentals in place, agentic AI can move from experimentation to execution, delivering measurable outcomes and creating tangible business value.
