Why data readiness is now a strategic imperative for businesses

Artificial intelligence (AI) is advancing at breakneck speed but its success depends on something deceptively simple: data quality. As organizations rush to deploy generative artificial intelligence (GenAI) and agentic AI, many overlook the critical role of clean, secure and well-governed enterprise data.

Without it, models falter, trust erodes and transformation stalls. To unlock real business value, data foundations must move from a technical afterthought to a board-level priority.

The year 2025 has marked a turning point in how leaders view AI's role in business transformation. It’s no longer seen as just a productivity booster but as an engine of change.

From predictive maintenance to dynamic pricing and hyper-personalized experiences, AI is reshaping how businesses operate – delivering solutions across functions that once seemed impossible to automate.

But that engine runs on data and not just any data – a foundation of high-quality, integrated and trusted information.

Despite widespread experimentation, fewer than one-in-five organizations recently reported high maturity in any aspect of data readiness. Most struggle with integration, quality and governance.

Once considered a back-office concern, data readiness – especially data quality – has become a strategic imperative. In our latest research on AI perspectives, more than half of business leaders cite data quality and availability as major challenges to accelerating AI adoption.

When asked about their fastest-growing areas of investment for technical AI capabilities over the next 12 months, 72% said they will prioritize data foundations and pipelines.

Too many AI projects fail because data is inadequate, siloed, outdated or poorly governed. When foundational data is fragmented or inaccurate, AI models generate outputs that appear sophisticated but are fundamentally wrong, creating an illusion of intelligence.

Instead of driving efficiency or improving customer experience, organizations end up with irrelevant recommendations, compliance risks and broken trust.

Even small gaps can derail transformation. Inventory systems may show products available, yet none can be shipped. Vendor files might validate but lack banking details. Customer records appear clean but lead to billing errors and regulatory failures.

That missing 5% of data accuracy adds up fast – and with GenAI, these issues multiply. Models trained on incomplete or inconsistent data don’t just make mistakes – they scale them. The result is expensive AI initiatives that fail to deliver measurable impact.

This is why data readiness must shift from an IT to a business metric. It’s not about whether data loads correctly – it’s about whether it drives the intended business outcome. Without that alignment, Gen AI becomes costly “AI slop”: outputs that look impressive but fail to create real value.

AI amplifies poor data quality. Quick fixes can’t replace strategic data management. Flawed outputs often get blamed on AI, when the real issue is the data; expectations for AI accuracy are higher than for humans, making errors more damaging.

Building AI on weak data platforms drives inefficiency, erodes trust and slows transformation. As AI-generated content grows more autonomous, the cost of bad data rises exponentially. Simply put: without high-quality data, AI just multiplies mistakes faster.

In conversations with CEOs globally, especially in North America, one priority stands out for 2026: data readiness. GenAI and agentic AI demand more than smart models – they require clean, secure, integrated and well-governed enterprise data to deliver real business value.

The investment may seem daunting but it’s essential for performance, innovation and resilience.

Instead of starting with isolated use cases, the conversation must begin with business priorities – efficiency, speed, cost reduction, innovation, risk management and customer experience – and address data readiness strategically.

A critical component of building a strong data foundation starts with elevating the data conversation. Data readiness is no longer an IT project – it’s a CEO and board-level responsibility.

Leaders must ask: do we have a single source of truth? Are governance and compliance embedded in our operating model? Is data quality monitored continuously? These questions define whether AI initiatives deliver impact or stall.

These five strategies can get businesses there:

The future of AI is autonomous, adaptive and everywhere. From cobots to hyper-automation, the next generation of intelligent systems will reshape industries. But without robust, trusted data ecosystems, even the most advanced AI will fall short.

The organizations that master this will lead the next wave of transformation – not just in AI but across every digital frontier.