Technical debt is commonly referred to as the long-term cost created when teams prioritize quick, temporary software solutions, at the expense of having to eventually revisit and rework it later down the road.
It has always been a part of engineering, and it reflects the reality that no system is built perfectly the first time. In fact, most organizations will choose to incur technical debt in order to meet short-term business goals or get a product to market faster.
While technical debt itself hasn’t changed, the pace and volume at which it’s accumulated is higher than ever.
Today, organizations can accumulate the same level of technical debt in weeks that previously took months or even years to develop. And from what we’ve seen, in many cases, that debt isn’t fully understood until systems are pushed to scale or placed under pressure, at which point gaps that once seemed manageable become harder to unwind.
As organizations move faster and invest more heavily in AI, the distance between what is built and what is fully understood continues to grow. The risk itself is familiar, but it is now emerging earlier in the lifecycle and often with far less visibility than before.
Technical debt is starting sooner than it used to
Technical debt has always been tied to decision-making under pressure. Historically, the presence of structure was what slowed its accumulation—clear architecture planning, shared ownership, and the involvement of multiple perspectives before critical decisions were made.
While that structure still exists, it is no longer required in the same way. As Rahul Gupta, Head of AI Governance at Insight Global Labs, explains, “If you’re building fast without the right thought process, you’re increasing the chances of creating technical debt much sooner.”
With modern tooling, teams can move from idea to working output with minimal friction. There’s no doubt this acceleration creates value, particularly in early experimentation and iteration, but it also compresses the thinking that used to happen around those decisions. Systems may function as expected, but the context and understanding behind its buildout is less clear and less visible.
When these decisions are being made earlier and with less collective input, it allows them to compound much faster over time.
AI is reducing friction and accelerating shortcuts
AI has fundamentally changed both who can build systems and how quickly they can do it.
“Now you can prompt and build software on your own—and skip all the discussions that used to happen with domain experts,” Rahul explains.
With increased AI adoption, we are seeing faster development, reduced dependency on large teams, and new possibilities for experimentation. At the same time, we’re also seeing a reduction in friction that previously forced alignment across stakeholders.
We’ve found that what typically gets overlooked is surrounding context:
- domain expertise that grounds decisions in real-world complexity
- business alignment that ensures the system solves the right problem
- validation that accounts for edge cases and long-term implications
As a result, the output may appear complete, but the shared understanding behind it is often far from it.
Over time, this distinction becomes more important. As speed increases, it becomes harder to differentiate between genuine progress and simple forward movement. Systems appear to be working, but the assumptions they rely on may not have been fully exercised.
This is often where technical debt begins to take shape—quietly, and without immediate impact.
Most teams don’t see the debt forming
Technical debt is rarely ignored intentionally. More often, it develops in ways that are not immediately visible to the teams creating it.
Rahul highlights this gap directly, “If you don’t have the technical understanding yourself, then you really don’t know the cost you’re accumulating.” As AI hides more of the underlying complexity, teams can deliver output without fully understanding what’s happening beneath the surface.
At the same time, adoption is widespread and increasingly encouraged across all industries. Federal Reserve data suggests that 78% of the labor force now works at firms that have adopted AI, and 54% at firms using large language models.
This combination can create a blind spot. In addition to the increasing technical debt, the challenge is that it’s forming without clear visibility into where or how it is even accumulating, making it also significantly harder to address.
Challenges are being baked in from the start
When AI initiatives stall, the explanation often focuses on execution; the rollout did not land as expected, the model underperformed, or the implementation failed to meet requirements.
In practice, the underlying issue often begins earlier, before anything is built.
It starts with assumptions:
- that the selected technology is appropriate for the use case
- that the underlying data is sufficient and reliable
- that the expected value is clearly defined
- that risks have been adequately understood
These assumptions can propel momentum forward, but when proven wrong, the consequences can add up fast. According to survey by Gartner, 37% of leaders in lower-maturity environments say identifying the right use case is one of the biggest barriers to AI implementation.
Use-case selection is one of the first decisions teams make. If that decision is unclear or misaligned, the project can move forward on the wrong foundation, making the issue harder to fix later.
At the same time, the downstream impact is becoming more apparent. Gartner predicts that at least 30% of generative AI projects will be abandoned after proof of concept due to factors like poor data quality, inadequate controls, and unclear business value—issues that trace back to early assumptions rather than late-stage execution.
“The biggest issue is unrealistic expectations,” says Rahul. When expectations outpace understanding, teams end up executing against the wrong problem. The system was never aligned with the problem it was intended to solve.
You can’t spot technical debt without the right people in the room
As it stands today, technical debt is a visibility issue, and visibility depends on who is involved.
“I think [recognizing technical debt requires] having the right set of people on the table,” says Rahul, “You need to have domain expertise… the people who are going to build the control and harness around it… and people to make sure operations and security are taken care of.”
When systems are built in isolation—whether by a single developer or a narrow team—critical perspectives are left out.
Domain experts can’t challenge assumptions. Engineers can’t evaluate long-term implications. Operators can’t consider scalability, security, or resilience.
In practice, the difference is timing. Some teams are able to spot out issues earlier than others because more of the right conversations happen before anything gets too far along.
Technical debt doesn’t become visible through tools alone, but through the invaluable perspectives of human expertise.
Intentional vs. accidental debt
This isn’t to say all technical debt is inherently a problem. Some of it is deliberate, a conscious tradeoff made by teams to move forward, knowing they will be addressed later. This kind of debt is manageable because it’s understood.
The risk comes from the opposite. As Rahul puts it, “If you’re taking a deliberate debt, that’s fine. But if it’s accidental, that’s where the problem starts—because then you don’t even know what you’ve created.”
Accidental debt forms when systems are built without clarity and decisions are made without full context. In those situations, the problem isn’t that debt exists, but that no one knows it exists. In other words, organization can’t manage what they don’t recognize.
Early decisions, greater impact
As systems mature and begin to scale, the impact of early decisions will only become more expensive to unwind.
According to Rahul, “When AI programs fail, it’s often because leaders didn’t understand whether the technology actually fits the use case.”
As AI adoption continues to accelerate, this is becoming more important by the day. Teams are building faster, decisions are being made with less friction, and systems are reaching production before there’s a shared understanding.
At that pace, the distinction between deliberate and accidental debt becomes easier to miss in the moment and harder to recover from later.
So the question isn’t whether technical debt exists—it always will.
It’s whether it’s being taken on intentionally, with a clear understanding of the tradeoffs, or quietly accumulated as a byproduct of moving too fast. To connect with our experts about your organization’s technical debt, contact Insight Global today.
by Deepak Kaushik 
by Rahul Gupta 
by Erin Ellison