The spending is real, but so is the hype. Everywhere you look, businesses are racing to adopt generative AI, hoping to unlock the kind of efficiency and innovation promised in the headlines. But for many, the reality is far less glamorous. Despite the investment, the impact remains mostly invisible—trapped behind the scenes or, worse, stuck in "pilot purgatory".
Why the disconnect? After digging into why some implementations soar while others stall, the answer rarely lies in the technology itself. The biggest obstacle preventing organisations from capturing real value from AI is data silos.
Data silos—isolated pockets of data held by one department that aren't accessible to others—are the silent killers of AI potential. When your customer data sits in sales, your financial data in accounting, and your operational data in logistics, an AI model can never see the full picture. It’s like trying to complete a puzzle with half the pieces missing.
In this post, we’ll explore the true cost of these disconnected systems, why high adoption rates are hiding low impact, and—crucially—why the solution is likely cultural, not technical.
The Financial Reality of Disconnected Data
The cost of messy, segregated data isn't just an operational headache; it is a massive drain on the global economy. Research from McKinsey estimates that data silos cost businesses a staggering $3.1 trillion (£2.4 trillion) annually.
For an individual company, the numbers are equally sobering. The average business loses around $15 million (£11.8 million) a year simply due to poor data quality. When systems don't talk to each other, you aren't just missing insights; you are actively bleeding revenue. IDC Market Research suggests that disconnected data systems can result in potential losses of up to 30% of annual revenue.
The operational toll is just as heavy. Forrester reports that knowledge workers spend 30% of their time just searching for information. That is nearly a third of the work week lost to navigating disconnected systems. It is no wonder that 68% of organisations now cite data silos as their top concern—a figure that has risen 7% in just one year.
The Gen AI Paradox: High Adoption, Low Impact
We are currently witnessing what McKinsey calls the "Gen AI paradox". On the surface, adoption is booming. By 2025, 78% of companies had implemented some form of generative AI, with 71% using it regularly in at least one business function.
However, widespread use hasn't translated into widespread value. Most deployments are failing to materially impact earnings. Why? Because AI needs good data to function, and only 12% of organisations report having data of sufficient quality to support effective implementation.
This disconnect leaves organisations in a difficult spot. They have bought the Ferrari, but they don't have the fuel to run it. Consequently, less than 10% of vertical use cases ever reach production. The rest remain stuck as interesting experiments that never quite deliver a return on investment.
It’s Not a Tech Problem, It’s a People Problem
Here is the most critical finding for any business leader struggling with this issue: the barriers are almost entirely organisational.
A September 2025 survey by The Information revealed a fascinating trend. When business leaders were asked what was stopping them from breaking down silos, less than 25% cited a lack of appropriate tools or unclear ROI. The technology exists, and the business case is clear.
So, what is the problem? The top barriers were:
- Poor data quality and disorganisation
- Privacy and security concerns
- Employee resistance and adoption challenges
This suggests that organisations believe AI tools could provide value if they had the expertise to manage the change. The challenge isn't buying a better piece of software; it's building better governance, overcoming internal resistance, and fostering a culture where data is shared rather than hoarded.
What Happens When You Connect the Dots
The good news is that when you do get this right, the payoff is immense. Organisations that successfully use AI to break down data silos aren't just seeing marginal gains; they are transforming their operations.
According to survey data, over 50% of these successful adopters report faster decision-making, while more than 40% cite improved visibility and better collaboration. Perhaps most telling is that less than 13% say they aren't seeing value.
The ROI numbers back this up. Real-time data integration implementations see an average ROI of 295% over three years. For top performers, that figure jumps to 354%.
Real-World Success Stories
We can see this impact in action across various industries:
- NASA: By partnering with Stardog to create a unified view of enterprise data, NASA enabled real-time discovery of relationships between tests, faults, and designs across previously siloed systems.
- BRF: This Brazilian food giant used embedded AI for demand planning, achieving a 33% reduction in planning time.
- Martur Fompak: In the automotive sector, this Turkish company reduced HR execution time by 98% through process automation.
- Uniper: The German energy company achieved 95% procurement accuracy by automating their data workflows.
Seizing the Market Opportunity
The gap between those who have mastered their data and those who haven't is creating a significant market divide. Currently, 40% of organisations are using AI tools to break down silos, with another 19% planning to do so in the next year.
That leaves 41% of the market completely untapped. This represents a massive opportunity for platforms that can bridge the gap between business leaders and practical AI expertise.
The market for solutions is exploding to meet this demand. The data fabric market alone is projected to grow from $2.5 billion in 2024 to $9.6 billion by 2032. Meanwhile, the streaming analytics market is set to rocket from $23.4 billion to over $128 billion by 2030.
Moving From Pilot to Production
Knowing which model fits your business is the first step to a successful AI strategy. The research is clear: the technology is ready, but are you?
Success in the AI era won't come from buying the most expensive tool. It will come from the hard, unglamorous work of cleaning your data, breaking down internal walls, and governing your systems responsibly. It requires shifting focus from "what tool should we buy?" to "how do we organise our teams and data to make this work?".
For SME leaders, the path forward is to prioritise practical outcomes over hype. Focus on proven use cases that address specific bottlenecks—like the ones slowing down your decision-making or confusing your customer service. By addressing the organisational roots of data silos, you can finally move your AI initiatives out of the pilot phase and into production, where they belong.
Ready to find the right path for your company? Discover tailored, high-impact AI use cases on NowHow today.