What We Learnt at WEF Davos About Putting Data to Work in the Age of AI

“I don’t think there’s any uncertainty about AI. The capital expenditure needed to build this out is one of the great opportunities for the world to come.”— Larry Fink, Chair and CEO of BlackRock, speaking at the World Economic Forum Annual Meeting in Davos

The above quote from Larry Fink perfectly encapsulated the conversation about AI at Davos 2026. Rather than focusing on future promise, leaders concentrated on how AI is being operationalised today, the data strategy required to support it, and the conditions needed to deliver value at scale.

Across the WEF Annual Meeting, AI was consistently framed as an enterprise capability rooted in data, infrastructure, and execution discipline.

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These are our 5 key pull outs:

AI strategy is now an organisational capability question

At the World Economic Forum, AI strategy was rarely discussed in isolation. Jensen Huang, CEO of NVIDIA, described AI as a full-stack platform spanning data, software, compute, and deployment. His emphasis was on building these layers together to enable enterprise AI at scale. This framing reflects how AI is now developing inside organisations. Value is shaped by operating models, decision rights, and how data flows through the business. AI initiatives increasingly sit alongside wider transformation programmes, rather than as standalone innovation efforts.

From a data strategy perspective, this highlights the importance of integration. AI performance depends on how well data, technology, and governance are aligned across the organisation.

Data strategy underpins effective AI adoption

Data strategy was a recurring theme across Davos sessions. While advances in models and computing power continue, speakers repeatedly returned to the fundamentals of data readiness, quality, and access.

Huang’s comments on building AI grounded in language, culture, and domain knowledge resonated strongly with enterprise audiences. Organisations with well-defined data architectures, consistent definitions, and strong data governance are better positioned to deploy AI systems that support real decision-making.

Data infrastructure was treated as a strategic asset. Topics such as data lineage, interoperability, and data ownership were discussed as enablers of scalable AI, not compliance exercises. This reflects a growing consensus that data maturity is central to long-term AI success.

Infrastructure, energy, and AI economics are converging

The physical and economic dimensions of AI featured prominently at WEF. Data centres, energy consumption, and infrastructure investment are now core elements of AI strategy discussions.

Elon Musk spoke about constraints emerging between chip availability and the ability to power AI systems at scale. His comments aligned with broader conversations about capacity planning and long-term sustainability. In parallel, Satya Nadella, CEO of Microsoft, linked infrastructure investment to outcomes, noting that AI must deliver clear economic and societal value to justify its resource demands.

These discussions reinforced a central idea at Davos: AI investment must be accompanied by a clear value narrative. Data strategy plays a key role here, enabling organisations to track performance, measure impact, and demonstrate return on investment.

AI diffusion depends on data maturity and ROI

Several World Economic Forum sessions explored how unevenly AI capability is spreading across regions and sectors. Kristalina Georgieva, Managing Director of the International Monetary Fund, discussed the implications of AI for productivity and labour markets, highlighting differences in readiness between economies. Whilst, Brad Smith, vice chair and president of Microsoft,  described AI infrastructure as highly localised, shaped by regulation, skills, and investment. Meanwhile, India’s IT Minister Ashwini Vaishnaw emphasised that adoption will ultimately be driven by return on investment.

Taken together, these perspectives underline the role of data capability in AI diffusion. Organisations with strong data strategies can deploy AI more efficiently, lower costs, and scale use cases more broadly.

Responsible AI is becoming an implementation discipline

Unsurprisingly, trust and responsible AI were discussed at Davos as practical design challenges rather than abstract principles. Best selling author and thought leader, Yuval Noah Harari observed that increased intelligence does not necessarily reduce error, particularly in systems that influence human behaviour at scale. As a result, AI governance, monitoring, and transparency should be treated as core components of AI strategy. Data plays a foundational role here, enabling auditability, performance tracking, and human oversight.

Responsible AI, in this context, is built through measurement and feedback. Organisations need data systems that allow them to observe AI behaviour, understand outcomes, and intervene where necessary.

What WEF means for data and AI strategy

For us the culmination of these themes at this year's Davos are music to our ears. They very much reflect the work that we are doing for our clients right now reinforcing the themes from our recent ebook.

Putting data to work today means designing AI systems that support decision-making, enable accountability, and deliver outcomes that stand up to scrutiny. Davos suggested that organisations able to combine AI ambition with disciplined data strategy are the ones best placed to achieve sustainable impact.