contributors
November 6, 2026 • 3 Min Read
For years, organizations have paid the price of technical debt in familiar ways: maintenance costs, manual work and systems that struggle to share information. Employees learned to keep the business running despite those limits. Now AI is bringing a more consequential cost into view. The very compromises that allowed companies to get by may prevent them from taking advantage of what comes next.
The danger is not only that aging technology will hold AI back. AI could make those compromises easier to live with, extending the life of work that should be reconsidered. Organizations face a choice: use new intelligence to improve the work they already do, or confront whether that work still needs to be done at all.
Gartner’s 2026 research describes technical debt as a constraint on AI investment and warns that AI can introduce new debt of its own. The deeper issue is organizational: technical choices become woven into processes, controls and jobs. Paying down the debt requires more than replacing applications.
When a Workaround Becomes the Work
Technical debt is not always the result of a poor decision. A company may accept a temporary integration to meet a deadline or preserve an older platform because replacement costs outweigh the immediate benefit. The debt grows when those decisions are no longer revisited.
Consider a finance function that reconciles figures across systems that do not communicate reliably. Employees build spreadsheets, checks and approval routines to make the numbers dependable. Years later, those routines have become part of the close. They may even be embedded in job descriptions and performance measures.
A new platform does not settle which reconciliations remain necessary, who should own the data or whether the existing approvals still serve a purpose. Those are business decisions. Avoiding them can reproduce the old complexity inside new technology.
In March 2026, Gartner examined the organizational behaviors that sustain technical debt, including reactive cultures and recurring short-term fixes. The implication is worth taking seriously: some of the debt sits in the decisions organizations have stopped questioning.
AI Can Deliver a Return—and Leave the Problem Intact
An AI tool might reduce that finance team’s reconciliation time from days to hours. Employees gain capacity, costs fall and the pilot looks successful. Yet the systems still disagree. AI has made it cheaper to correct the mismatch, not removed the reason for the mismatch.
That distinction can get lost in productivity reporting. RGP’s 2026 AI×Human research found that 73% of active AI users get more done in a typical week, while 80% say time saved by AI fills with more work. Additional output is real. What it becomes for the enterprise is a separate decision.
AI-assisted development adds another concern. Teams can create automations quickly, sometimes before anyone has established how they will be maintained, governed or retired. Local ingenuity is valuable, but disconnected solutions can leave the company carrying fresh dependencies alongside the old ones.
An attractive return on one activity is not proof that the organization has improved how its business works. Leaders need to ask which underlying problem an AI investment solves—and which it merely makes less visible.
The Change Decision That Comes Too Late
This is where traditional transformation practices can work against the goal. Companies choose systems, approve budgets and design implementation plans before asking employees to change how they work. By the time change management begins in earnest, the opportunity to question what should be built may have passed.
RGP’s 2026 CFO Perspectives findings expose the disconnect. Forty-four percent of CFOs cited change management and adoption among leading sources of delays in strategic initiatives. But when asked where they would invest an additional $10 million, only 6% selected change management.
The reluctance is understandable, if shortsighted. A technology purchase comes with a vendor, a price and deliverables. The return from eliminating unnecessary work may cross budgets and functions, leaving no single executive accountable for capturing it. People then inherit the burden of making the new system fit the old operating model.
Change management should enter before the business case closes. Employees who understand the work need a voice in what should change; functional leaders need to resolve competing requirements; and the investment case should identify what the organization expects to stop doing. Adoption matters, but it is not the only outcome.
Paying Down Debt Without Waiting for the Next Overhaul
AI capabilities will continue to evolve. A once-a-decade modernization cycle is a poor match for technology that can change the economics of work within months. Gartner predicts that organizations making continuous work redesign a core capability will be twice as likely to sustain AI-driven transformation by 2028. That reflects a meaningful change in how leaders are being asked to think.
The alternative to episodic overhauls is not perpetual reorganization. It is a routine way to examine the processes and technical compromises on which the business depends. When employees identify an unnecessary control or a recurring data mismatch, there should be a way to challenge it and an accountable leader able to act.
This also makes debt management more selective. Some compromises remain economical. Others block capabilities the business now needs. The right question is not whether all debt can be eliminated, but whether leadership can distinguish useful trade-offs from inherited constraints before they become expensive to unwind.
These choices give executives a way to see whether modernization is changing the enterprise itself. They also make it harder for a successful deployment to conceal work the business still should not have to do.
Review technical debt as a recurring business choice. Establish a quarterly cross-functional review of the few dependencies most likely to constrain strategic priorities. Give employees a route to surface friction between reviews. Track concrete measures—manual reconciliations eliminated, duplicate tools retired, decision cycle time reduced and exceptions still requiring intervention—rather than simply reporting how many applications were replaced.
Make every AI pilot answer a second question. In addition to measuring time saved or output improved, ask whether the pilot removes a source of complexity or depends on keeping it in place. If AI automates a workaround, set a deliberate date to revisit the underlying problem. Record who will maintain the automation, what data it depends on and when it should be retired or integrated into a shared capability.
Fund the operating change alongside the technology. Before approving a platform or AI business case, require the sponsoring leaders to specify which processes will end, which responsibilities will move and what employee capacity is needed to make those changes stick. Assign one accountable business owner for the outcome, with IT responsible for technical delivery. Avoid counting implementation milestones as evidence that the old work has disappeared.
Follow one consequential decision across the enterprise. Choose a process that matters to performance, such as responding to a shift in demand or closing the books. Trace the data, handoffs, approvals and manual repairs required to reach a decision. Bring the people who actually perform that work into the review. The purpose is to distinguish necessary controls from activities that exist mainly because systems and functions cannot coordinate.
The work begins with a business conversation, not a system inventory. Senior leaders do not need to decide which application to retire themselves. They do need to establish where inherited complexity is preventing the company from delivering value—and who is accountable for changing it.
A More Cohesive Enterprise Is Within Reach
The greater opportunity is not a cleaner technology estate for its own sake. It is an organization able to use human judgment and AI together across functions. Sales might detect a change in demand; operations could test the capacity response; finance could see the implications before the opportunity passes. That requires trustworthy information and people with the authority to decide together.
Enterprise cohesion is not produced by connecting software alone. It becomes possible when the organization can keep adjusting its work, decisions and responsibilities as its capabilities change. Technical debt matters because it can hold those arrangements in place long after they have stopped serving the business.
The next AI investment should therefore begin with a harder question than how much time it can save: what will the business be able to do differently? If the answer depends on leaving the underlying organization unchanged, the company may be buying relief from technical debt rather than a way beyond it.