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The Drum Network article
This content is produced by The Drum Network, a paid-for membership club for CEOs and their agencies who want to share their expertise and grow their business.
October 22, 2025 | 9 min read
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Don’t leave your AI systems idling. Neal Balar of VML explains how to get the most from your datasets and integrate AI into your business workflows.
Models trained on outdated data can frustrate customers, explains Balar / Tim Mossholder via Unsplash
Artificial intelligence (AI) has moved from the sidelines to the driver’s seat of business transformation. But here’s the truth: AI is only as powerful as the data you feed it.
In the early years of digital transformation, AI was treated as one of many promising tools – a chatbot here, a predictive model there. Today, our independent survey of business leaders across multiple markets shows that more than eight in 10 view AI as the most important technology for achieving their strategic goals.
The difference between AI hype and AI impact comes down to one thing: data readiness. Without clean, connected, and accessible data, even the most advanced AI models will fail to deliver meaningful results. Data readiness transforms AI from a promising concept into a measurable growth engine – and leaders can take immediate steps to get there.
When AI was treated as a ‘component,’ it was bolted onto existing processes, delivering incremental improvements but rarely changing the game. Now, AI is the engine that orchestrates transformation – shaping priorities, redesigning operations, and reimagining customer experiences.
But engines need fuel. For AI, that fuel is high-quality, unified data. Without it, even the most sophisticated algorithms are left idling.
Our research shows that 85% of digital transformers now view AI as the most important technology to support their goals, and it’s little surprise that AI is their most demanded skill from third parties they rely on to support their transformation programs. Yet the organizations seeing positive ROI outcomes – 63% of those surveyed – are those that have fostered stronger digital maturity and embraced enabling technologies like AI. And these technologies are most effective when built on a foundation of high-quality, well-governed data.
The gap between ambition and execution is often a data problem. More than half of leaders (59%) admit their organization’s data isn’t fully AI-ready. The challenges are familiar: information trapped in silos, inconsistent formats that make integration costly, and incomplete or inaccurate records that undermine accuracy.
These aren’t just technical headaches; they are strategic risks. A recommendation engine trained on outdated product data will frustrate customers. A predictive maintenance model fed incomplete sensor readings will miss critical failures. In both cases, the credibility of AI – and the leaders who champion it – takes a hit.
By contrast, organizations with unified, high-quality data can deploy AI faster, at lower cost, and with greater accuracy. They can unlock advanced capabilities like real-time personalization and anticipatory service – advantages that widen the competitive gap with every passing quarter.
Strong data governance is more than a compliance exercise; it is the foundation for predictive and anticipatory operations. When accurate, timely, and well-structured data flows freely across an organization, AI can detect patterns and act before issues arise.
In practice, this means, for example, that an airline can automatically rebook passengers before they reach the gate, a retailer can adjust promotions in real time based on local weather and demand, and a bank can block suspicious transactions before the customer even notices.
In a reactive model, value is lost in the lag between event and response. In an anticipatory model, that lag disappears – replaced by seamless, often invisible interventions that prevent disruption and create better experiences. And none of it is possible without data readiness.
Technology alone won’t make you data-ready. People and culture matter just as much. Our research shows that 83% of leaders agree that digital change programs are as much about people as tech. And 74% cite a lack of change management around people as the cause of digital project failures focused on growth or transformation.
Building a data-literate workforce means more than teaching employees how to use AI tools. It’s about helping them understand why data matters, where it fits in the bigger picture, and how it will change their role for the better. It’s about clear communication that links AI initiatives to business goals, and change management that addresses fears head-on – from job security to ethics – replacing uncertainty with confidence.
When people trust the data and the AI it powers, adoption accelerates. Employees are freed from repetitive, low-value tasks and can focus on creative problem-solving, strategic decision-making, and higher-order analysis. The result is not just a more efficient organization, but a more engaged and empowered workforce.
In the AI era, few organizations can go it alone. The complexity of AI adoption – from data integration to ethical governance – makes collaboration essential.
The most successful organizations build ecosystems that combine complementary strengths: technology providers to deliver platforms and tools, data partners to enrich and expand datasets, academic institutions to push the boundaries of research, and in some cases even competitors to tackle shared industry challenges.
These partnerships accelerate time-to-value, reduce implementation risk, and help organizations become data-ready faster than they could alone.
Leaders in AI transformation from successful global enterprises share three traits: they integrate AI into core strategy, they invest in data readiness, and they prioritize human adoption.
Critically, successful AI transformation is not a one-off project – it is an evolving capability. The most advanced organizations treat AI as a living system: continuously learning, adapting, and improving in response to new data, market shifts, and customer expectations.
In the AI era, data readiness is the new competitive advantage. The organizations that invest in it now will move faster, personalize deeper, and anticipate better than their competitors. Those that don’t will be left behind – no matter how advanced their AI tools.
VML
VML is a leading creative company that combines brand experience, customer experience, and commerce, creating connected brands to drive growth. VML is celebrated…
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