A clear path to AI success emerged at Davos this year, focusing on three key elements that enable implementation and scale.
Moving AI from experimentation to production requires a focus on these simultaneous priorities: technology deployment, workflow redesign, and workforce readiness, all underpinned by trust as the foundation for resilience and long-term impact.
Executives from industries including healthcare, insurance, manufacturing, and corporate functions pointed out that the gap between those still experimenting and those implementing at scale is widening every week.
Here are some of the key themes and examples that emerged from the World Economic Forum’s Annual Meeting, where this year's theme, “A Spirit of Dialogue”, emphasised the collaborative problem-solving we need to focus on.
The founder of an AI education company demonstrated something that would have seemed impossible two years ago. He uploaded a 30-page research document and asked AI to create a training course.
Within seconds, it generated a chapter structure. Two minutes later, a complete interactive course existed, something that previously took weeks of work.
“This brings new challenges,”, he said, noting that the skills required have shifted from execution to delegation and from coding to specifying problems and designing solutions.
This speed compression – AI collapsing timelines that once took weeks into seconds – is reshaping expectations across almost every industry. A chief of staff at a large financial services group described building a personal AI agent to support her CEO. The agent assists with curating speeches in his style and can manage follow-ups, something that was described as helping “scale up” the CEO's impact across the organisation.
Meanwhile, in healthcare, AI is tackling issues that have evaded solutions for decades. A senior executive at a major diagnostics company identified three specific barriers AI can address:
“Today, people are not receiving the right therapy because not all the testing has been done,”, he said, noting that 35% of cancer patients don't receive proper diagnostic testing.
In the EU, the average time from diagnosis to treatment for cancer is 120 days. “That's way too long for everyone,”, he said. “When you know, the time is counting.”
AI's ability to aggregate fragmented datasets and surface the latest research could significantly compress these timelines. Technologies already in development include blood tests that can detect different cancer types in early stages from a single sample.
Even so, adoption faces hurdles, including funding, regulatory constraints and concerns about hallucinations and risks, he said.
In commercial insurance, AI is transforming one of the industry's most complex processes: risk assessment for large corporate clients. A senior executive at a major insurance company described the traditional workflow. A broker sends data about a factory or business seeking coverage. Underwriters spend days extracting information, structuring it, checking internal systems, reviewing history, consulting public sources, and querying global claims databases.
“Now AI is already extracting all the data, structuring the data, going into our internal systems, looking at the history”, he said. “It's going to public sources, augmenting the data. Then we have a risk appetite, certain risks we'd like to have. So, AI is already looking at if this matches our risk appetite”.
The system also accesses the company's global data lake, containing risk, claims and policy information from every country they operate in. The central point is that, with manual tasks removed, time is freed up to review the risks. This puts the focus on human judgement, he said, but it also requires a complete overhaul of the current workflow.
Manufacturing leaders are grappling with a new kind of unpredictability as decision-making and ownership begin to shift to AI systems.
“Until now, my job was always to facilitate the work of human beings, make it easier, optimise”, said a CIO at a large industrial company. “But at the end, there was always a human being having ownership of the result”.
Now that's changing. “If you think of agentic AI further into the future, you will have ownership of results for AI. So, imagine a board meeting, like, who is accountable for this? And they're like, okay, probably we have an AI doing this”.
There’s also an element of unpredictability. At the moment, AI is primarily handling tasks, but over time, it will take on more decision-making as well. As a result, the mix of work and the workforce itself will evolve.
Another manufacturing executive, from the shipbuilding sector, described how AI is transforming both production, and maintenance operations.
The company introduced standard copilot tools for proposal writing and communication, saving significant time on routine tasks. But the real impact came in spare parts management.
Previously, when ships needed spare parts, the process took five days. This included: reviewing the email, identifying the parts, checking suppliers, pricing everything, and creating a proposal.
“Now the complete process is redesigned, and AI was put on it”, he said. “Five days is down to five hours”.
The company is relying on major software vendors to embed AI into engineering and design tools, which could dramatically increase productivity, he added. This could make today’s engineers far more effective, potentially delivering the equivalent output of many more people in a much shorter timeframe.
He also pointed out the potential in supply chain management, where AI can improve the timing of decisions, reducing costs from ordering too early, and avoiding losses from ordering too late.
That’s why organisations need to become Perpetually Adaptive Enterprises and embrace operating models where adaptability is embedded into every process, every day.
The consensus across sessions was that 2026 is the year of implementation.
“The tools are all there”, said the CTO of a major financial services technology company. “This year will be about turning ideas into reality.”
But implementation requires more than deployment. It requires structural change, and this, in turn, requires trust in the technology, the processes, and the organisation's commitment to its people.
One executive described his company achieving 95% participation in AI training across all employees. TCS executives explained how they ran the world's largest AI hackathon, with four weeks of ideation generating 278,000 ideas, followed by solution-building that produced 174,000 working prototypes.
A consistent pattern emerged across industries: AI can succeed when it handles complexity while humans apply judgement, but before that can happen, workflows need to be fundamentally redesigned for the best results. Adding AI to existing processes will likely only produce marginal gains.
What emerged clearly from Davos was that AI has moved from a fascinating possibility to an operational necessity.
The AI conversation has shifted from “what could we do?” to “what are we actually doing?” and “how are we going to do it?”
These examples from healthcare, insurance, manufacturing, and corporate functions share a common connection: the spirit of dialogue that enables AI to deliver value when organisations are willing to rethink how value is created, rather than just how tasks are executed.
Moving from pilot to production is now the primary objective. The technology is ready; the defining variable for 2026 will simply be the speed at which leaders adapt.
The path from AI experimentation to production is now clear:
Speed compression is real - AI is collapsing weeks of work into hours, and organisations that don't adapt will fall behind.
Redesign, don't just deploy - Adding AI to existing workflows produces marginal gains, while reimagining how work gets done will unlock exponential value.
Trust enables scale - Success requires trust in the technology, in redesigned processes and in the organisation's commitment to preparing its people.
2026 is the year of implementation - The technology is ready; the defining variable is the speed at which leaders act.