EXECUTIVE SUMMARY
Artificial intelligence has entered a decisive new phase. Organizations are no longer experimenting with isolated generative AI pilots; they are moving toward embedded intelligence and agentic AI systems that can plan, reason, and act autonomously across operations, customer interactions, and value chains.
As with every major digital transformation initiative, successful and sustainable deployment of AI at scale depends on organizations having access to high quality data. Call it what you will - ‘the new oil’, ‘the new gold’, ‘the lifeblood’ - data provides the foundation for organizations to compete and win in the age of AI.
Business leaders recognize the critical importance of data in achieving their AI ambitions, yet, as our own research highlights, most admit that their organizations are falling short of the mark when it comes to data readiness. They’re not confident in the accuracy of data being used to build AI models, nor do they think their data governance processes are adequate to mitigate risk. In fact, leaders point to data quality and accessibility as the biggest barriers to AI success.
Evidently, organizations need to develop robust, forward-looking data strategies to support and accelerate their AI programs. And this requires new thinking.
For more than a decade, enterprises have relied on a framework known as the Five Vs of Data - volume, velocity, variety, value, and veracity. It was an effective model for the Big Data era, when the primary challenge was collecting, storing, and analyzing expanding pools of information. With the rise of AI, however, the nature of data’s role has evolved. The Five Vs still have relevance, but three now rise above the rest as critical to safe, reliable, and impactful AI: Value, Volume, and Veracity. They form the foundation for AI that can be trusted, governed, and scaled sustainably.
Value - data-fuelled AI initiatives must be grounded in real, measurable business outcomes.
Volume - large and diverse datasets are essential to prevent hallucination and bias.
Veracity - AI depends on data that is traceable, governed, and trustworthy.
This paper explores why these three dimensions now matter most, how they differ from traditional Big Data priorities, and what leaders must do to build the data foundations required for AI deployment at scale. The argument is simple: in the age of AI, data readiness is not just an IT requirement - it is a strategic imperative. Organizations that treat it as such will be in position to use AI confidently and responsibly. Those that do not will face stalled AI programs, spiraling remediation costs, and rising operational and regulatory risk.
