Solving AI’s Information Problem
AI can only be as effective as the information behind it. For many organizations, unstructured content, disconnected systems, and years of accumulated information debt have become the biggest obstacle standing between AI investment and AI value.
Out of the Lab and Onto Your Laptop
November 30, 2022 hasn’t quite entered the historical record as a “where were you when it happened?” date. For business leaders, though, it may prove more consequential than many events that have. That was the day OpenAI released ChatGPT to the public. Within five days, the platform attracted one million users. Within a few months, that number eclipsed 100 million, making it one of the fastest-growing technology products in history. After mostly being confined to theoretical conversations and research labs, generative AI became accessible to anyone with a web browser.
The response in businesses across the globe was immediate. Boards began pressing CEOs for AI strategies. Software providers scrambled to incorporate AI into their product offerings. Business owners of every size started evaluating how this new technology was going to impact something into which they had spent years pouring cash, sweat and tears. Almost overnight, AI moved from a future consideration to a present-day business priority.
By 2025, that global reaction had turned into a bona fide arms race. Organizations were investing heavily in AI research and development, while the world’s largest technology companies were pouring hundreds of billions of dollars into the infrastructure needed to power them. Combined capital expenditures among major hyperscalers surged from roughly $162 billion in 2022 to more than $448 billion in 2025, as the race accelerated.
Yet, despite the eye-popping investment, there was a nagging cluster of dark clouds forming in the otherwise sunny skies of the AI boom. Many organizations discovered that deploying AI was easier than practically incorporating it into their day-to-day operations. Gartner found that only 28% of AI initiatives met ROI expectations, while poor data quality, limited data access, and operational misalignment were among the most cited causes of failure.
The factors organizations cited for the failure of these initiatives didn’t arrive recently either. They have been a thorn in the side for businesses at least as long as there has been ink and paper. The AI boom simply dragged them into the spotlight. Problems that had been tolerated as nuisances suddenly became genuine barriers to growth.
The lesson itself is not a new one. Every technological revolution carries the same warning: garbage in, garbage out. Companies that embraced industrialization using inferior raw materials eventually discovered the limitations of the most advanced machinery of their time. Organizations are learning the same lesson with AI. The model may be cutting edge, but if the information is fragmented or otherwise inaccessible, the results will fall short of expectations. The good news is that these problems are solvable.
Solving AI’s Underlying Equation
The traditional solutions companies relied upon to paper over the cracks in their information management processes were no longer going to cut it.
Leading the shift toward AI-driven automation requires democratizing access to intelligent information. Lowering the cost of data extraction and removing traditional barriers to entry can give organizations a practical path to modernizing operations without the complexity often associated with large-scale transformation initiatives.
The central challenge for many organizations is not a lack of technology, but an inability to leverage the information they already possess. Decades of records often contain valuable information that remains inaccessible until it is incorporated into a modern workflow. When information stays scattered across paper archives and disconnected business systems, employees struggle to find it, leaders struggle to govern it, and AI struggles to use it.
Picture a classic “Mom and Pop” office where everything has been written on legal pads for 45 years. The problem is actually much larger than that. IDC finds that approximately 90% of enterprise data is unstructured. Invoices arrive as PDFs. Onboarding packages sit on email servers. Contracts bounce around shared drives for months before anyone signs them. Businesses continue investing in modern systems, but the work itself still slows down at the document level.
This information needs to be unlocked before it becomes another casualty of digital neglect. Through services like backfile scanning and Intelligent Document Processing (IDP), organizations can transform decades of paper records into AI-ready assets. Information that once sat untouched in filing cabinets suddenly becomes usable. Digital transformation succeeds or fails based on how effectively organizations can interpret, validate and operationalize the information captured inside those documents.
The organizations realizing the greatest value from AI understand that information accessibility alone is not enough. Information must move in order to support decisions and trigger action. This is where automation becomes just as important as information management.
One example is returned mail. It seems mundane, but for many organizations it creates a waterfall of manual work. Employees spend valuable time identifying customers and researching records. Returned mail automation capabilities can transform that process by capturing information, validating it, routing it to the appropriate systems, and triggering the next step automatically. The lesson applies everywhere. Value is created when organizations can move information efficiently through the business rather than letting it get trapped inside hard copies and disconnected processes.
The same principle applies to governance. Automation initiatives cannot exist outside an organization’s compliance and accessibility requirements. ADA Title II document accessibility, for example, illustrates a growing reality: intelligence without governance creates a new set of vulnerabilities. It’s easy to think of a firm’s documents as part of an archive, but those are operational assets. They certainly cannot be governed or trusted if they can’t even be found. Thus, their value disappears long before AI enters the equation. Connecting information management, automation, and governance, helps organizations reduce these risks as they scale.
This imperative extends across industries. A Salesforce report showing 84% of people who work with data every day said their organization’s data strategy needs fixing before AI can deliver meaningful value. The companies making the greatest progress with AI are focusing less on chasing the newest tool and more on creating the operational foundations that allow technology to succeed.
The opportunity is to maximize the value of information already within the enterprise. Content management, intelligent automation, and process optimization can create the conditions for AI to deliver measurable, durable business value.
Looking Ahead
The next phase of AI’s impact on global business is unlikely to be defined by whomever leverages the most powerful models, as so much of that market has already been saturated. The organizations that will realize the greatest return on investment from their AI initiatives will be the ones that differentiate themselves through the quality of their information.
AI strategy conversations are already moving in this direction, with less emphasis being placed on experimentation and more pressure being placed on execution. Trust, transparency, and governance are becoming competitive differentiators. The organizations that can orchestrate information effectively will have a significant advantage over those still struggling to manage it.
Like the previously cited example of industrialization, this is not a new development. Every business transformation follows a familiar path. First comes the hype cycle, followed by a mad scramble to adopt. The organizations that eventually win are the ones that master their own operational excellence. Once firms understand the value of the information trapped on email servers and buried in download folders, they can begin identifying where better governance and workflow design can create real efficiencies. Many of the roadblocks employees compensate for every day existed for a long time before AI brought them into sharper focus.
In the four years since LLMs made AI strategy the dominant conversation in boardrooms across the globe, companies have tripped over each other trying to differentiate themselves through the size of their investment or the sophistication of their models. In the long run, however, the organizations that best manage and leverage their information will be the ones that win the race. For organizations still working through that challenge, making information accessible, governable, and actionable is the foundation for unlocking lasting value.
Meg Fleming
As Vice President and General Manager of BlueIrisIQ, Meg Fleming focuses on client-centric innovation, high-performing teams and intelligent solutions. Her leadership journey began at SymQuest, where she rose from client support to President, serving in that role for more than nine years. With more than 30 years in the business, she leads with accountability, collaboration and a deep understanding of client needs. Based in New England, Meg brings clarity, connection and a results-driven approach to every challenge.