The AI gap — why enterprises are failing to prepare their data

Just as the Internet completely changed our lives, there is no doubt that artificial intelligence (AI) has the potential to do the same. So, while AI is rightfully a top priority across all industries and regions, there’s an elephant in the boardroom... things aren’t going according to plan. As generative models evolve faster than most enterprise systems can adapt, the gap between ambition and readiness is widening.

Enterprises are under immense pressure to showcase measurable ROI from digital innovation and launch effective AI models. But, put simply, the gap between ambition and execution has never been wider. So, what’s the solution?

Fundamentally, the problem isn’t AI itself. Instead, it’s organisations rushing in without a strategy and neglecting the data that fuels these models. As a result, if businesses want to start seeing meaningful outcomes, they need to be more targeted with their investments. And it starts with the right foundation.

The data problem

There’s a phrase in developer circles that neatly explains why bad data is so detrimental: “garbage in, garbage out”. All too often, organisations are feeding outdated or even erroneous information into their AI models and are then disappointed by the output. But what most businesses don’t realise is that they’re sitting on mountains of valuable content in the form of unstructured data; it’s just not being used.

Unstructured data encompasses everything, from emails to video footage, that lacks a predefined format, creating significant management and analysis challenges. According to Gartner, it represents a staggering 80% of enterprise content, and with enterprise data doubling every two years, it can very quickly become unmanageable.

Unlocking the potential of this data is key to maximising the value of AI. Unstructured data, once converted into AI-ready, usable information, can often provide the nuance and context needed for fully comprehensive responses. 

The sheer scale of unstructured data, however, means it’s impossible for humans alone to sort through and categorise what is often thousands of terabytes of information. Fortunately, content management solutions have come a long way in the past decade, and, with the right platform, businesses can access their valuable data more easily than they think.

Back to basics

If effective AI is the destination, content management platforms are the vehicle to get you there. Too many organisations are still running on siloed systems, meaning that content, business processes, and applications are all fragmented. Even if the data across these disparate platforms was structured, AI cannot possibly operate efficiently in this kind of environment.

The public sector illustrates this point clearly. Valuable information is often siloed across different departments and agencies, and the restrictions imposed by legacy systems force AI models to operate on limited context. For GenAI, this means low quality responses, but for a more autonomous AI agent, this could lead to some dangerously flawed decisions.

As well as consolidating data, modern content management platforms can make it AI-ready as well. These tools can transform and standardise different content types into a format that can be fed into AI. Scanned copies of hand-written notes, for example, are turned into machine-readable text, and video recordings are automatically transcribed. This automation means more data can be analysed and AI models can offer more valuable, accurate insights.

In fact, it’s precisely these advancements in content management that have made successful AI use cases so impactful. We’re now entering the era of ubiquitous enterprise intelligence, where organisations have a living record of their business, which in turn enables large-scale automation. As AI evolves and needs increasingly large quantities of high-quality data to fuel their output, this holistic, contextual understanding of enterprise content will only become more important.

Building from the bottom

The organisations that have seen success with AI are taking the time to get the basics right. While it’s easy to get distracted by the long-term possibilities of AI, none of its potential can be realised without the foundations of infrastructure and data quality.

Crucially, those investing now are laying the groundwork for the future. It’s important to remember that AI is still in its infancy and the pace of innovation is accelerating. But if enterprises are struggling to integrate AI effectively now, they’ll find themselves even farther behind in a couple years.

It essentially boils down to digital maturity. And while it’s easy to become fixated on AI-specific shortcomings, the real story reveals a much deeper issue -- businesses aren’t making the most of their data, and often they don’t even know what they’re missing.

The real AI gap is between the organisations that truly understand this technology and those that are just chasing trends. AI cannot be treated as a bolt-on upgrade, a quick one-size-fits-all fix, or a replacement for human creativity. Enterprises need to be much more intentional by tailoring their implementation to the business’s specific needs and investing in the infrastructure that can support their ambition.

As with any new technology, teething problems were inevitable, but there’s been enough time to know what works and what categorically does not. Businesses can’t afford to bury their heads in the sand any longer. Now is the time to learn from the past and invest in your future.