Davos 2026: Beyond the AI hype

The conversations coming out of this year’s World Economic Forum point to a noticeable shift in how organisations are approaching technology-led change.

AI is no longer being discussed as a silver bullet. Increasingly, it is being treated as a capability that must earn its place inside the enterprise.

The cooling of AI hype

What stood out most was how far the conversation has moved from last year’s exuberance. The narrative of “rapid AI adoption”, often accompanied by sweeping claims of automation and workforce reduction, is losing credibility.

In many cases, this positioning feels less about transformation and more about competitive signalling.

Senior industry leaders were notably pragmatic. AI is increasingly understood to replace skills, not entire roles. Most jobs are collections of tasks and capabilities. Some of those can already be augmented by AI. Very few can be fully automated in a meaningful, responsible way today.

From replacement to augmentation

AI fluency is becoming a baseline assumption. Teams are expected to use intelligent tools to move faster, make better decisions, and reduce friction, while judgement, accountability, and customer understanding remain firmly human.

This shift is changing how organisations hire, train, and structure work. The value is not in deploying more tools, but in enabling people to use them effectively.

Legacy constraints are now strategic constraints

Another recurring theme was the growing gap between organisations building with AI in mind and those attempting to layer it onto existing environments.

Large enterprises were candid about the challenge. Integrating AI into complex legacy systems is slow and costly. It requires architectural change, data discipline, and sustained operating-model evolution.

By contrast, organisations designed with AI as a foundational capability are moving faster, not because of the technology itself, but because the surrounding systems, processes, and incentives are aligned. This is where disruption is emerging, and where incumbents are being forced to respond.

The shift toward decision-grade intelligence

There was also a clear move beyond fascination with generative outputs. Executives are increasingly focused on whether AI systems can support real decisions.

The demand is for intelligence that provides context, traceability, and confidence, not just content. As environments become more volatile, leaders want systems that help them act decisively, not simply explore possibilities.

Execution as the defining metric

Across industries, the same conclusion surfaced repeatedly: execution now matters more than intent.

Despite an estimated $1.5 trillion invested in AI globally, many organisations are still struggling to scale initiatives beyond pilots and embed them into core operations. The constraint is no longer access to technology, but the ability to integrate AI into existing architectures, processes, and decision-making.

Organisations making progress are not waiting for certainty. They are consolidating pilots, embedding adaptability into their operating models, and treating technology as both a growth enabler and a stabilising force. The gap is widening between those who can industrialise change and those who remain stuck in perpetual experimentation.

What this means going forward

The takeaway from Davos is not that AI is slowing down. Expectations have matured.

The next phase of transformation will be defined by:

• practical AI deployed with discipline
• human capability amplified, not displaced
• architectures designed for resilience and control
• an uncompromising focus on delivery

In today’s environment, confidence is reinforced through consistent delivery, built incrementally through systems that perform as intended.

If these points resonate, connect with us to discuss the practical impact of AI delivery at AbsoluteLabs.

‍

‍

Source: Davos 2026: Why scaling AI still feels hard - and what to do about it | World Economic Forum

‍