An Olympic-sized effort: CIOs prep for AI disruption in 2026
The management issue is not whether AI exists in the stack, but which AI decisions deserve internal ownership. The article points to a useful distinction for CIOs and transformation leaders: generic capabilities are increasingly commoditised, while proprietary workflows, data and domain-specific prediction may justify in-house build. That means AI sourcing should be governed by use-case criticality, data sensitivity and the strategic value of differentiation โ not by a blanket preference for speed, cost or novelty.
The second implication is organisational, not technical. Modernaโs โflow of workโ framing and the IOCโs cross-functional agenda both suggest that AI adoption stalls when it is treated as an IT project. Leaders should expect questions about decision rights: who owns prioritisation across HR, digital, operations and product teams; who approves exceptions; and how success is measured across productivity, service quality and risk. If AI is to become routine, the operating model has to make it routine.
That creates a portfolio trade-off. Smaller models, vendor roadmaps and open-source options may reduce cost and accelerate deployment, but they also increase the need for disciplined selection, integration and lifecycle management. The practical next questions are: which use cases are truly enterprise-wide, which are niche and defensible, and which should be deferred because the organisation cannot yet support the change?
For executives, the test in 2026 will be whether AI investments are attached to explicit business outcomes and accountable owners. Cost savings alone will not sustain adoption. A stronger management case is to define where AI improves throughput, decision speed, safety, customer experience or scientific and operational advantage โ then fund only the initiatives that can prove those gains while keeping security, compliance and workforce impact visible from the start.
Moderna’s ย prescription for AI internal adoption
Moderna leverages AI to redefine work, said Tracey Franklin, chief people and digital technology officer at the pharmaceutical and biotech company. “We are applying AI to most areas of the business. But how we’re doing that is looking at what we call a flow of work.” Franklin said that redefinition of work includes exploring software, robotics and AI, combined with the human element. Further, she said her role at the company was shaped around such considerations. Traditional human resources management tends to focus on workforce planning, Franklin said, while the IT team focuses separately on IT portfolio planning. Moderna combined those elements rather than keep them siloed. “We’ve brought that together to say that’s just how work gets done in the future, as we have a broad-based approach to AI,” she said. [link VIDEO] Moderna was an early adopter of this recent upswing in AI. “We have a partnership with OpenAI, and we moved quickly to democratize that across the organization,” Franklin said. This meant every single employee started to use AI, she said, with the potential for further development and use to be explored. Franklin said Moderna refined its AI strategy further to include assessing related software already in use, such as SAP and Workday, to understand what those vendors have on their roadmaps regarding AI. Moderna continues to look at what it can feasibly build in-house. “We’re identifying what’s unique to our company and where we can focus our effort and our time and our engineers to really have that unique advantage,” she said.IOC: A marathon rather than a sprint to AI gold
AI is already part of the international sports scene. Ilario Corna, chief information and technology officer for the International Olympic Committee (IOC), said his organization launched the Olympic AI agenda in April 2024 after six months of lead-up work. “For us, it was very important to go out as broad as possible to understand where we should apply, where we should not apply AI,” Cornaย said. The Olympic AI agenda contains five points of focus:-
Use AI to support athletes and keep them safe.
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Provide equal access to AI.
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Use AI to optimize the Olympic Games, which can include how to organize them to make them more efficient and sustainable.
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Increase engagement to reach more fans and the community.
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Apply AI to increase efficiency within the IOC administration.
Small models, big adoption in 2026? It’s all about culture
D’Ercole said he expects the tech race to continue among the big providers with their signature, marquee large AI models, but there may be more adoption and scale to come from lower-cost alternatives. “What is maybe more interesting is that the smaller models — the Gemini Flash, the GPT Nano and the Claude 2, all of them — will become good enough to be applied in day-to-day applications in technology,” he said. Corna concurred that smaller models have the potential to see accelerated adoption in 2026. He also said that open source models are gaining popularity. Franklin said that if a company decides to build its own AI, it should be built into the culture. “It has to be the way that you operate. If it’s important to you as an organization and you want to advance it, it should be critical that you develop your culture around the use of technology and innovation,” she said. Adoption and interest in AI tend to increase in an organization when employees see they can use it to innovate and upskill themselves, Franklin said. She went on to say that leading with cost for AI is ill-advised to motivate a company to explore its capabilities. “No one gets excited about cost and cost savings,” Franklin said. “That’s not a driver of an employee. A driver of an employee is innovation.”Enjoyed this article? Sign up for our newsletter to receive regular insights and stay connected.
