AI-Ready Isn’t Optional Anymore: Why Most Enterprises Still Miss the Last Mile

Curious about what keeps experts, CEOs and other decision-makers in the Intelligent Document Processing (IDP) space on their toes? Get food for thought on IDP-related topics from the industry’s leading minds.

In this opinion piece, Chris Huff, CEO of Intelligent Document Processing (IDP) vendor Adlib Software, unpacks why document preparation is the essential first step in any enterprise AI strategy – and offers a new lens on how to solve this challenge efficiently.

In 2018, I joined Kofax to help reposition the company from an RPA and BPM provider to a full-spectrum intelligent automation platform. Over the years, I worked with enterprises across regulated industries and saw the challenges of scale, compliance, and complexity firsthand. What was clear then remains true now: technology alone doesn’t drive transformation, the ability to operationalize it does. That’s why I chose Adlib as the next step in my career.

Interestingly, I wasn’t the only one to draw that conclusion. Anthony Vigliotti, who led product innovation alongside me at Kofax, has also recently joined Adlib. Together, we saw a common pattern emerge: enterprises wanted to embrace AI, but their content infrastructure simply wasn’t ready.

This isn’t a software problem. It’s a content operations problem.

And we believe this is where the next chapter of enterprise transformation will be written.

The Real AI Roadblock: Unstructured Content

It’s estimated that 80% of enterprise data is unstructured. Let that sink in for a moment: only 1 in 5 pieces of critical business data is actively managed and ready to be leveraged by AI.

And while AI vendors continue innovating on model performance, most organizations are still struggling to prepare the raw materials: the documents. Documents, scanned, layered, fragmented, and unlabeled, remain the raw material that today’s AI isn’t equipped to handle on its own.

In my view, the most urgent challenge is not building a better model, it’ s getting clean, structured, validated data into the model in the first place.

Without that foundation, enterprises face:

• Hallucinated insights from misunderstood context
• Fragmented compliance documentation that fails audits
• Broken handoffs between business systems due to missing metadata

The cost of getting this wrong is more than technical. Middle managers reportedly spend up to 2 hours a day searching for information, and 59% say they regularly fail to find the data they need. That lost time affects everything, from decision quality to time-to-market to regulatory readiness.

This Isn’t About Technology, It’s About Design

I’ve spent much of my career (at Kofax/Tungsten and Deloitte) helping organizations fix broken workflows. What I see now is that unstructured data governance is the next major bottleneck. Without the ability to ingest, clean, classify, validate, and route unstructured content, even the most promising AI use case remains theoretical.

If your organization can’t:

• Recognize and classify documents on arrival
• Extract key data points accurately
• Validate them against business or regulatory rules
• Route and structure that data into AI-ready formats

…then no amount of algorithmic horsepower will make a difference.

We need to design workflows, not just install tools.

From Tech Differentiation to Vertical Experience

One of my trusted advisors recently reminded me of a hard truth: tech features aren’t enough. Smart chunking, vector embedding, and prompt chaining are everywhere now. What separates winners is experience. That means:

• Knowing the vertical (Do you understand the intricacies of insurance claims workflows?)
• Demonstrating measurable outcomes (Can you speak to how validation impacts batch delivery in life sciences?)
• Speaking the customer’s operational language (Have you designed document streams that feed ERP, PLM, and QMS systems simultaneously?)

These lessons stuck with me.

These are not abstract problems. They are daily operational frictions. And solving them is where IDP earns its place. Not as a product, but as a discipline.

The Real Opportunity: IDP as Strategic Infrastructure

Intelligent document processing is no longer about digitization. It’s about enabling:

• AI trust and explainability through validated inputs
• Real-time orchestration across compliance workflows
• Adaptive processes that learn and improve over time

The numbers back it up. A 2023 Kickstart survey of over 1,000 full-time employees at large enterprises found that organizations implementing document automation platforms saw up to a 64% reduction in time spent searching for information and an 89% increase in productivity due to fewer manual data errors. Even conservatively, saving just one hour per employee per day adds up, translating to roughly $8 million in annual savings per 1,000 employees when accounting for fully loaded labor costs.

What excites me most is that we’re not talking about theory anymore. The tools exist. The knowledge exists. The challenge now is execution at scale.

Organizations that treat IDP as infrastructure, not an accessory, will be the ones that thrive.

Final Thought: The Missed Link in AI Strategy

There’s a reason why leaders from across the automation and compliance space are shifting their focus to intelligent document processing. It’s not a trend. It’s a recognition that AI-readiness starts long before the model is deployed.

The work begins in the documents. In the complexity of legacy systems. In the nuance of compliance rules. And in the ability to orchestrate all of it with precision.

If we want to lead in the age of AI, we need to rethink where the real value starts. My view? It starts on page one.

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