3 weeks ago
5
ARTICLE AD BOX
AI training must be taken more seriously, argues Cloudera’s Francisco Mateo-Sidron. (Photo: Monkey Business Images / Shutterstock)
European businesses have a productivity problem. Output per worker still hasn’t returned to pre-2008 levels, and Europe’s labour productivity gap compared to the US has widened steadily since the mid-1990s – and again after the pandemic.
Governments are starting to recognise AI’s potential to address this output issue. The UK has launched a £2 billion AI Action Plan and national skills programme aimed at preparing students and workers for an AI-driven economy. In the Netherlands, the government has pledged €70 million to build a major AI research hub in Groningen focused on innovation and infrastructure.
These developments are promising – but they risk compounding the very issues they aim to address if businesses adopt AI without considering which members of their workforce will benefit most. Rather than driving productivity, AI could entrench a two-tier workforce: those equipped to use it, and those left behind.
The two-tier workforce is already here
Across sectors, AI adoption is becoming relatively widespread – but it’s happening unevenly. Only 11% of over-55s and 18% of women have received any AI training, compared to 30% of under-44s and 36% of men. Senior leaders are almost five times more likely to have training in AI than manual or clerical staff. This means there is a fast-moving “AI-literate” tier of teams and individuals learning how to use AI to drive performance, productivity and innovation, and a trailing cohort that risks being excluded from the benefits.
This gap isn’t hypothetical. Organisations already embedding AI into their workflows are pulling ahead – not just among C-suite and data teams, but across the workforce, helping to streamline tasks, reduce cost and improve decision-making. The results are telling. Those rolling out AI across teams are 43% more likely to record revenue gains and 40% more likely to report higher productivity, compared to organisations where only a few departments are AI literate. Industries most exposed to AI have also seen labour productivity grow almost five times faster than those that are not.
This story has played out before with cloud computing and digital transformation “haves and have-nots”. But the speed of AI adoption means the gap is growing faster, and the consequences are greater. Failure to address today’s skills gap is set to result in serious structural inequalities.
Why this matters for business, not just policy
A fragmented workforce isn’t just a cultural issue, it’s a business one. Transformation must be a company-wide initiative, but if AI capability sits with just a few people in specialist roles, growth will be slower.
To unlock real productivity, businesses need employees at every level experimenting with AI, building confidence and applying it to their everyday work. That means giving people an environment to learn, not just tools to use.
But scaling AI across the workforce is a readiness problem. Many organisations still rely on legacy systems that weren’t built for an AI era. Data is siloed across dozens of platforms, each with different formats, access protocols and compliance rules. When key data is limited to individual departments, technical teams or even tools, it deepens the divide between those who can experiment with AI and those who can’t.
This fragmentation creates an uneven playing field. Democratising data means giving everyone, regardless of role or region, access to consistent and well-governed data. Then AI delivers just as well for a marketing manager in Asia as for a developer in Europe.
The fix for fragmentation isn’t more isolated tools, but modernising infrastructure. A modern data architecture underpinned by a unified data platform brings siloed data sources together into a single, governed environment. This enables consistent access, automatic application of security and compliance controls, and provides visibility into how data is used. The risk of AI misuse also drops, which allows meaningful, safer experimentation. Only then can businesses encourage company-wide, responsible innovation, without compromising security, privacy or compliance.
The future of AI is access – and readiness
In isolation, implementing AI won’t fix the European productivity gap. Organisations need to ensure their workforce can use new solutions safely and effectively to drive their business forward. That starts with data infrastructure. Businesses need to enable consistent, governed access to data across the organisation – so that every employee, in every role, can have the same AI experience.
But true change also requires a culture shift. One that supports learning, encourages responsible exploration and opens up opportunity across all levels of the workforce. The future of AI relies on whether businesses make innovation inclusive – by investing in infrastructure, enabling experimentation, and making AI accessible to everyone.
Francisco Mateo-Sidron is SVP and Head of EMEA at Cloudera
