In the race to unlock AI’s full potential, businesses can’t afford to build on shaky ground. No matter how advanced the algorithm, its real-world impact hinges on a simple truth: bad data leads to bad decisions.
As AI takes center stage in business strategy, the spotlight must now turn to the quality and security of the data that fuels it.
Chief Technology Officer for Artificial Intelligence at Hitachi Vantara.
Quality in, quality out: why data still matters
AI is only as effective as the data it’s trained on. Yet many organizations are still drowning in incomplete, unstructured, or low-quality data.
Unlike humans, who can draw on experience and context to make informed decisions, AI models rely solely on the data they’re fed. When data is fragmented or flawed – whether due to silos, inconsistency, or limited scale – the outcome is rarely better than the input.
In business contexts, where AI is increasingly deployed in critical decision-making, poor quality data can lead to costly mistakes. These range from wasted resources and customer frustration to significant operational disruptions and reputational damage.
The compliance imperative
The risks aren’t just operational – they’re legal. Misguided AI recommendations, especially in high-stakes sectors, like healthcare or finance, raise serious questions about accountability and liability.
Ensuring data compliance isn’t just best practice – it’s now a legal necessity. High-quality data improves machine learning performance by allowing models to identify accurate patterns and generalize effectively. This is what drives trustworthy, real-world results.
New regulations are sharpening this focus. The EU AI Act is one example of the shift towards stronger guardrails around how data is used, particularly in high-risk use cases. But compliance can’t be an afterthought. It must be designed into systems from the outset, with strong data management and auditability built in.
Despite this, there’s a clear gap between awareness and action. While 38% of IT leaders recognize data quality as the top driver for AI success, a strong majority are still testing models in live environments and 74% of IT leaders are learning on the fly.
Agility has its place. But without strong foundations, it can expose businesses to risk. To make AI truly pay off, data infrastructure must evolve in tandem.
Infrastructure before intelligence
AI needs more than high quality data – it needs vast volumes of it. And the demand is only growing.
According to the IEA’s special report, Energy and AI, global electricity demand from data centers is expected to more than double by 2030. A major driver of this growth? AI workloads. In fact, electricity consumption from AI-optimized data centers is projected to quadruple within five years.
This growth is unsustainable without serious upgrades to the data infrastructure beneath it. That means scalable, secure, and modern systems designed not to just store and process data, but to protect it and govern its use.
Thankfully, the tools to do this are already here. Hybrid cloud data platforms now offer powerful capabilities for integrating on-premises storage with cloud environments, meaning data is always optimized for high-performance systems. What once felt like a ‘nice-to-have’ has become a baseline requirement.
Regulatory pressures are growing too. From the Digital Operational Resilience Act (DORA) to GDPR and the EU AI Act, there’s a clear expectation that organizations can account for the data they use – and the decisions AI systems make with it.
Securing AI’s future: where to go from here
AI’s future will be shaped not by algorithms alone, but by the integrity of the systems supporting them.
That starts with data: standardized, secure, and accessible. It continues with infrastructure: resilient, scalable, and compliant by design. And it ends with trust – earned by building systems that are not just powerful, but transparent and responsible.
Today’s most innovative businesses understand this. They’re investing in centralised data platforms, automated compliance tools, and secure data pipelines to protect what matters most. These solutions don’t just unlock AI potential – they de-risk it.
By fixing the foundations, we ensure that AI isn’t just fast, but dependable. Not just smart, but safe.
The future of AI will be built on data. Let’s make sure it’s built right.
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