Despite billions pouring into AI, research suggests most projects stall or fail because the underlying data isn’t ready. Gartner estimates that six in ten AI initiatives without AI-ready data will be abandoned. Leaders may blame the technology, but the reality is simpler: “garbage in, garbage out” is alive and well in the AI era.
The quiet revolution
Look around most organizations and you’ll see the shift happening already. AI use at work has nearly doubled from 20% to 40% in just two years, according to a Gallup poll. The use cases are growing daily:
- Hospitals are using AI for disease detection, diagnosis, and treatment plans.
- The insurance industry expects to save $390 billion by 2030 through AI efficiencies.
- Financial AI agents analyze market data to detect fraud, manage risk, and execute trades autonomously.
- Fast food chains utilize AI “virtual recruiters” to find candidates, reducing hiring time by as much as 75%.
These are small glimpses of what’s next. The bigger change is coming in how work itself gets done for everyone. Agents and copilots are starting to replace a patchwork of applications. Instead of juggling multiple systems, employees will increasingly rely on agents to orchestrate workflows, generate reports, and surface insights. White-collar work will run through intelligent intermediaries.
You won’t be toggling through screens to get work done in the not-too-distant future.
But for those agents to make the right call, they need one thing above all: accurate, connected, and real-time data. Without it, their answers are incomplete at best, misleading at worst.
The hidden AI bottleneck
The average large enterprise runs more than 800 systems. Some of them have thousands of systems where data is stored. Customer information sits in one application, financial data in another, HR records somewhere else. Operational data might live inside spreadsheets on someone’s desktop.
When AI pulls from this patchwork—assuming it even can—the results are inconsistent, outdated, or flat-out wrong. Most executives agreed in one survey that AI could be transformational, yet they lack the high-quality data required to power it. It’s no surprise that poor data quality is now the number one barrier to AI adoption. It’s also a significant risk as the outputs are unpredictable.
The new rules of enterprise data
As Manish Sood, founder and CEO of Reltio, puts it: “We’re entering the Age of Intelligence, when AI agents generate content, make decisions, and continuously learn. In this environment, the old rules of data don’t apply.”
The new rules are clear. Enterprise data must be:
- Connected across every function, from customer engagement to finance.
- Accessible in real time, not trapped in yesterday’s spreadsheets.
- Structured for AI, so models and agents can consume and act on it effectively.
Ignore these rules, and companies risk building sophisticated AI capabilities on quicksand. Follow them, and AI becomes a durable advantage.
Lessons from industry leaders
Some companies are already showing the way.
- Mastercard had to integrate fragmented data across acquisitions to gain a single view of customers, merchants, and products. By unifying data into one trusted foundation, it built stronger experiences and smarter decision-making.
- L’Oréal recognized that its most valuable asset wasn’t its brands, but its data. Treating information as an engine of growth, it connected and cleaned data to drive direct-to-consumer strategies.
These examples underscore a critical truth: the competitive edge doesn’t come from having the flashiest model, but from having the most reliable data.
The path forward
The message is clear. Winning in enterprise AI isn’t about picking the “right” LLM. It’s about preparing the ground they run on. Unified, trusted, and real-time data is what allows agents and copilots to deliver accurate insights, automate workflows, and scale decisions across the business.
Organizations that continue to operate with siloed systems and fragmented information will find themselves outpaced. Those who invest in a data foundation designed for the AI era will unlock the real prize: not just using AI, but using it well.
The old rules no longer apply. Read more about the 10 New Rules reshaping enterprise data, along with additional resources to guide your AI journey.
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Source: News