The race to become an AI-first enterprise is accelerating, but as companies rush towards the future, they are dragging a growing weight behind them. Technical debt is no longer just an IT problem but a business risk hiding in plain sight.
The ambition to become an AI-first business is rapidly transforming from aspiration to expectation. Boards are demanding results, executives are greenlighting pilots, and leaders are staking reputations on transformation. However, while innovation’s speed has accelerated, many organisations’ structural readiness has not kept pace. The result is an explosion in technical debt, most invisible until it begins to hurt.
“There is massive benefit to becoming AI-first, and that is where many companies are heading,” Jon Knisley, Product Marketing Manager of Process AI at ABBYY, explains. “But I think it is contributing to increased quantities of technical debt. That is not entirely bad, but it needs to be managed and addressed.”
The problem is not simply that models fail. It is that they accumulate. A relentless wave of pilots and proof of concepts is generated across the enterprise with little coordination or scrutiny. According to the latest figures, just eight per cent of AI models ever make it into production. That statistic is often used to paint a picture of failure, but Knisley argues it is not necessarily a sign of dysfunction.
“You are trying to identify the signal from the noise,” Knisley adds. “Ultimately, that is what you want to put into production. That is where you need to be more worried. The rest, if managed well, can be considered part of the strategic learning curve.”
Pilot purgatory and the illusion of progress
The symptoms are familiar to executives who have lived through past waves of digital transformation. AI’s ‘pilot purgatory’ feels alarmingly similar to the early days of robotics and automation in manufacturing. There is a flood of exploratory projects, innovation labs springing up across departments, dashboards, demos, and decks. But what there is less of is production-scale value.
“It is worse with AI than before,” Knisley reflects. “The push has come from the board level. Everyone feels like they must do something with GenAI, which has led to a proliferation of pilots. But just because many of them do not make it into production, that does not mean it is bad.”
In theory, AI shares many of the characteristics of software in agility, modularity, and scalability. This has led to a mindset of ‘fail fast, iterate often’. However, that approach often fails to address the complexity of AI’s integration within existing systems, workflows, and governance models. The problem is not the pace of failure but the illusion of success.
“There is this impression that technology is the easy button, and you can just click on it and it will solve all your problems,” Knisley says. “But it does not. Most of the time, the issue is not technology but people and processes. That is where organisations are underinvesting, and that is what leads to real debt later.”
The digital attic of unused potential
Technical debt is not just about broken code or rushed deployments. It also accumulates in the shadows, in the attic of forgotten tools, unstructured data, and unused features. Enterprises are hoarding technology at a pace far exceeding their ability to consolidate, rationalise, or even remember what they have.
“It is like the things you put in your attic at home,” Knisley explains. “You bought them for a reason. Maybe they served a purpose for a while. But now they are just sitting there collecting dust. Nobody wants to throw them away, but nobody uses them either.”
Tool proliferation and redundant platforms are one side of the problem; the other is data. Siloed, inconsistent, and poorly documented datasets are a silent threat to AI success. The value is often there, but invisible to those who need it most. “You have so many siloed data sources,” Knisley adds. “Some get built, used once, and forgotten. That is valuable information just sitting there. But if nobody knows about it, it becomes a liability.”
AI needs reengineering, not just automation
When AI is dropped into existing systems without rethinking how those systems operate, it amplifies inefficiency rather than removing it. There is a temptation to view AI as a plug-in solution that can ‘optimise’ broken workflows without addressing their underlying flaws.
“Too many processes today are fundamentally inefficient,” Knisley explains. “You cannot just take a little piece, drop AI into it, and expect everything to be fixed. What you are doing is just pushing the problem upstream or downstream. It is not optimisation. It is reengineering that is required.”
AI deployment, at its best, requires more than automation. It demands a rethink. Organisations must be willing to tear down and rebuild how they work, using the full capabilities of available technology, rather than simply applying a digital veneer to analogue inefficiencies. “To get the most value from AI, you need to start with a blank sheet of paper,” he adds. “Build from the bottom up, rethink your operations, and integrate the technology into the design of the process, not just on top of it.”
Process AI is a diagnostic tool for enterprise debt
One of the tools gaining traction for addressing this challenge is Process AI. Unlike traditional analytics, which report on what happened, Process AI reveals how work gets done, across systems, departments, and hidden dependencies. It is diagnostic, forensic, and deeply actionable.
“In my opinion, most companies do not fundamentally know how they operate,” Knisley says. “And that makes change incredibly hard. You cannot get to your target state if you do not understand your current state. Process AI provides that clarity.” Beyond discovery, Process AI supports continuous monitoring. It allows organisations to predict the ROI of proposed automation projects and track whether that value is delivered. This full-cycle visibility is becoming increasingly important in environments where AI programmes are under greater executive scrutiny.
“Stakeholders are being held accountable,” Knisley says. “They need to prove the promises they are making. Process AI gives you that visibility, before and after. And that is where it drives real value.”
People are not the problem, but they are the key
At the heart of the issue is a recurring theme: people. AI is often presented as a technological challenge, but success or failure depends on human behaviour. When users resist change, do not understand how systems work, or revert to old habits, even the most sophisticated AI initiative can stall. “The goal is not to force fit humans into AI systems,” Knisley continues. “It is to design AI systems that support how people work best. That combination of people and technology delivers the real value.”
That includes aligning deployments with how teams work in reality, not how they are expected to work in theory. It also means prioritising behavioural design, change management, and training, not just technical implementation.
Data debt is the iceberg beneath the surface
Without clean, structured, and accessible data, AI cannot function. Yet many enterprises treat data quality as a maintenance rather than a strategic priority. The result is growing data debt, an invisible but increasingly expensive technical risk. “You have to audit your data, streamline it, consolidate the silos,” advises Knisley. “That means removing the clutter, structuring the architecture, and ensuring the right people have access to the right data at the right time.”
He describes clean data not as an overhead, but as an enabler. Enterprises that invest in foundational data hygiene see faster deployments, more accurate models, and greater scalability from their AI programmes.
AI is both part of the problem and the solution
Ironically, while AI contributes to technical debt, it also holds the potential to resolve it. Tools are emerging that can identify inefficiencies, refactor legacy code, and monitor systems for long-term drift or degradation. Soon, AI may become the enterprise’s own digital auditor.
“There are two specific areas,” says Knisley. “Process AI for understanding how the company operates. And GenAI for analysing and remediating code. AI can help alleviate the debt it creates, if it is deployed intelligently.”
The AI debt manifesto
To scale AI successfully, enterprises must confront some uncomfortable truths. Technical debt is not just an engineering issue. It is a strategic blind spot. Left unchecked, it will slow innovation, inflate costs, and erode trust in AI.
Knisley offers a simple but urgent set of principles: balance debt elimination with innovation, favour reengineering over surface-level optimisation, use purpose-built AI where possible, and design from the ground up with governance, privacy, and people in mind.
“Debt is not always bad,” he concludes. “But it must be managed. And it must be seen as a business issue, not just a technology one. Because you cannot build the future on foundations you have not maintained.”
