The Space Race of the 1960s saw global superpowers compete to develop superior technological capabilities. Today, a similar race is playing out and once again speed, ambition, and innovation are essential – but instead of mastering space flight, the aim is to master artificial intelligence.
Governments are launching bold national strategies, while organizations across all sectors are rapidly piloting and scaling AI models. In the UK, the government’s AI Opportunities Action Plan has spurred even more momentum. However, there’s a fundamental issue beneath this progress that could hobble the mission before we achieve “lift off”: a misalignment between leadership ambitions and the reality of IT and data readiness.
Data shows that businesses may be overestimating their preparedness. While 81% of global organizations are already piloting or scaling AI software, only 44% of UK business leaders report that AI has delivered meaningful improvements. So what’s behind the disconnect?
When ambition outpaces alignment
One major factor is a fundamental misalignment between leadership ambition and the actual readiness of organizational data infrastructure. The reality is that AI does not run on ambition alone; it requires huge volumes of clean, organized and accessible data. Yet in many organizations, data remains siloed, duplicated, or inconsistently managed. This leads to misalignment and the expectations become both premature and potentially costly.
And when leadership and IT teams aren’t on the same page about data strategy and management, that can inhibit the success of AI initiatives. Poor-quality data introduces bias, weakens model performance, and ultimately erodes trust in outputs. While inaccurate or “hallucinated” results demand human correction, wasting time and resources. At worst, they can damage customer relationships, brand reputation, or regulatory standing.
Don’t overlook data infrastructure
AI systems are only as good as the data they’re trained on, and this also goes for the infrastructure managing data throughout the lifecycle. After all, AI doesn’t just use data, it creates it too.
As a result, organizations must have a clear strategy for how that data is captured, stored, secured, classified, and retired. The ability to tag sensitive information, manage version control, and ensure traceability can give businesses the ability to audit how a model arrived at a decision. It’s a useful technical benefit, but also a prerequisite for regulatory compliance and ethical AI.
On the sustainability front, a robust infrastructure can help manage the environmental impact of growing data estates. This could include optimizing storage efficiency and reducing energy consumption for unused data. Similarly, technologies like data compression and tiering can lower the physical footprint of storage, which reduces the associated energy and cooling costs.
Over a quarter of businesses expect their data footprint to grow by 50% due to AI projects, so an effective data management strategy is crucial to support businesses to achieve goals alongside their AI ambitions.
Supporting compliance, security and resilience
When implementing AI, we can’t just think about the next quarter, or even the next year. To achieve long-term success, considerations for cybersecurity resilience and regulatory compliance must be embedded from the start as well. The alternative is to risk exposing sensitive data, data corruption, or costly breaches.
All of these can undermine AI output and increase regulatory risk. A resilient infrastructure ensures AI systems can recover quickly from disruptions and maintain performance, while compliance readiness enables safe, scalable AI deployment.
If built correctly, data infrastructure can also ensure businesses are not reduced to simply responding to security issues or regulatory requirements, but are on the front foot. As cyber threats become more sophisticated, intelligent storage can isolate and protect critical datasets, maintain immutable backups, and support rapid recovery.
As a result, businesses are both better placed to prevent data breaches, and are more resilient to such incidents. Increasingly, this is also a mandate – not just a nice to have. In the EU for example, DORA guidelines enforce that financial institutions have the measures in place to withstand, respond to and recover in the case of cyber-attack or system failure.
Unlike the 1960s, this race isn’t about who will be the first to deploy the biggest or most complex model. Instead, it’s who will be the ones to build sustainable, trustworthy, and scalable systems that stand the test of time. That starts with data.
Business leaders must ensure that they are internally aligned on their AI strategy to see long-term success. By starting with consolidating data management strategies, businesses can make sure that they start off the AI race on the right foot.
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