Artificial intelligence has moved decisively into the mainstream of enterprise operations. According to the 2026 State of AI report by Airia, nearly 88 percent of organisations worldwide now use AI regularly in at least one business function. This level of adoption would have been unthinkable just a few years ago.
Yet widespread usage has not translated into proportionate business outcomes. As enterprises enter 2026, a clear pattern is emerging. AI is present across organisations, but its ability to deliver sustained, enterprise-wide value remains limited. The coming year is likely to expose a widening divide between companies that have structurally integrated AI and those that have only layered it onto existing systems.
Only 39% See EBIT Impact Despite Widespread Use
The report highlights a persistent gap between AI adoption and measurable financial performance. While most organisations report active AI use, only a minority can link it directly to earnings.
Less than 40 percent of organisations report any EBIT impact from AI initiatives, and among those that do, the contribution is typically modest. In most cases, AI accounts for under five percent of EBIT. This suggests that AI is still operating at the margins rather than as a core driver of business performance.
The underlying issue is not model capability. Instead, organisations are deploying AI within workflows that were designed for human decision-making and manual execution. Without redesigning these workflows, AI remains constrained, regardless of how advanced the technology becomes.
55% have Scaled AI Beyond Pilots
More than half of surveyed organisations claim to have scaled AI across the enterprise. However, scaling in this context often means replication of isolated use cases rather than true operational integration.
Many AI programmes stall after initial success due to predictable constraints. Legacy systems limit integration, data remains fragmented across departments, and AI skills are concentrated within small specialist teams. Governance frameworks are frequently introduced late, once risks have already surfaced.
As a result, AI becomes harder to manage as it grows, leading to stalled rollouts or cautious retrenchment rather than expansion.
Agentic AI Is Growing, but Execution Remains Difficult
Agentic AI is widely viewed as the next phase of enterprise automation. The report estimates that the global agentic AI market could reach 35 billion dollars by 2030, starting from approximately 8.5 billion dollars in 2026.
Despite this growth, many early agent deployments have failed to deliver expected outcomes. The primary reason is overreach. Organisations attempted to build broad, general-purpose agents capable of handling complex, ambiguous tasks.
More successful deployments follow a different approach. Enterprises are implementing narrowly scoped agents with clearly defined responsibilities. These include workflow-specific agents, domain-focused agents aligned to regulatory and industry context, and small task-based agents that can be combined to support larger processes.
This move towards specialisation reflects a growing understanding that reliability and control matter more than versatility in enterprise environments.
Infrastructure and Orchestration Are Emerging as Key Differentiators
The report draws a clear line between advances in AI models and an organisation’s ability to use them effectively. While models are improving rapidly, the ability to deploy AI at scale depends far more on the underlying infrastructure and how AI systems are coordinated.
By 2026, inference workloads are expected to account for almost two-thirds of all AI computing. Although there is growing interest in running AI at the edge, most high-impact applications will still depend on centralized, high-performance data centres to deliver reliable results.
Many organisations struggle to scale AI because they lack the platforms needed to manage AI agents, access data in real time, and apply consistent security and governance controls. As a result, attention is shifting towards AI-native development and orchestration platforms that are designed to support scale, reliability, and control from the beginning.
Only 6% Achieve Material Enterprise Impact
A small subset of organisations consistently report strong business outcomes from AI. The report identifies this group as approximately six percent of enterprises, defined by EBIT impact of five percent or more.
What distinguishes these organisations is not experimentation, but discipline. They treat AI as strategic infrastructure rather than as a collection of tools. Workflows are redesigned before automation is introduced. Leadership accountability is clearly defined, with senior executives owning AI outcomes.
These organisations also invest differently. A significant portion of their digital budgets is allocated to data, platforms, and operating models, rather than short-term pilots. Measurement of AI performance is systematic, and scaling is approached as an organisational change initiative rather than a technical rollout.
Governance Is Becoming a Prerequisite for Scale
As AI adoption accelerates, so do associated risks. The report outlines emerging threats such as shadow AI usage, adversarial manipulation, model poisoning, and prompt-based attacks.
At the same time, regulatory pressure is increasing. The European Union AI Act enters full enforcement in 2026, and other regions are developing their own regulatory frameworks. By 2027, more than half of countries are expected to operate region-specific AI platforms.
In this environment, governance is no longer a post-deployment control. It is becoming a core operational capability. Organisations are integrating explainability, auditability, and AI-specific security measures directly into their platforms. Those that delay this shift are finding it increasingly difficult to scale AI without exposing themselves to regulatory, operational, and reputational risk.
2026 Will Expose Structural Readiness
The central message of the 2026 State of AI report is not about faster models or wider experimentation. It is about organisational readiness. Incremental adoption has largely run its course, and further gains will depend on deeper structural decisions.
Enterprises that treat AI as an overlay on existing processes will continue to see limited returns. Those willing to redesign workflows, modernise core systems, and establish clear ownership for AI outcomes are more likely to convert adoption into measurable impact.
In 2026, AI success will be determined less by what technologies organisations acquire and more by how decisively they reshape the way work is organised and governed. The difference between sustained value and stalled investment will be structural, not technical.
