Managed Vs. Unmanaged Technical Debt
How Prepared is Your Company for Disruptive Technology?
The past few years have been an eye-opener for many companies, especially with the release of technologies like ChatGPT. On November 30th, 2022, OpenAI unleashed what would become one of the fastest-adopted technologies in history. Companies that were nimble, experimental, and prepared jumped on board, starting pilots, proofs of concept (POCs), and scaling projects within just a few months. These were the organizations that understood the importance of not just embracing change but running toward it.
Fast forward nearly two years, and it’s surprising how many companies are still scratching their heads, wondering what to do with generative AI. Some are still paralyzed by uncertainty, while others have simply ignored the opportunity. And yet, ChatGPT was just the beginning. If you think its adoption was rapid, imagine what the next big thing will look like. It will likely be digital, and the wave of adoption might move even faster.
This begs the question: How prepared is your company for the next big disruptor?
What Exactly is Technical Debt?
The concept of technical debt originated in software development, where it refers to taking shortcuts in writing code to achieve faster results, but at the cost of creating harder-to-maintain, messier code. It’s similar to financial debt: you get a quick payoff today, but it accrues "interest" in the form of future inefficiencies, bugs, and higher maintenance costs. Over time, this concept has expanded beyond software to encompass the entire IT landscape and, more broadly, everything that surrounds the tech stack. Technical debt now includes not just outdated technology, but also poor data management, lack of collaboration tools, gaps in digital skills, and even cultural resistance to change. If leadership doesn’t fully buy into digital transformation, or if employees lack the necessary skills to adapt, that too is a form of technical debt.
The key distinction is between managed and unmanaged technical debt. Managed technical debt means you’re aware of the trade-offs and have a plan to address them over time. It’s strategic and accounted for. Unmanaged technical debt, on the other hand, is chaotic, unplanned, and accumulates without oversight, leading to roadblocks when disruptive technologies—like generative AI—come along. Companies with unmanaged technical debt will find themselves scrambling, with too many gaps in their organization to fully integrate the new technology.
The Tetris Analogy: Lining Up for Disruption
Let’s talk Tetris.
In Tetris, the goal is to clear lines by neatly stacking blocks. The holy grail is waiting for that big "I" piece—the long block that perfectly fits into that narrow gap, clearing four lines at once (yes, the Tetris moment). But what happens when you’ve stacked blocks poorly, leaving gaps everywhere? When the "I" piece finally comes, it has nowhere to go, and the game just gets messier.
Generative AI, like ChatGPT, is the "I" piece. For companies with managed technical debt, all the other pieces (culture, skills, leadership, budget, infrastructure) are nicely aligned. They were ready for that "I" piece and made their "Tetris" happen. But for companies with unmanaged technical debt, the gaps—be they in leadership support, a lack of digital skills, or a misaligned tech stack—prevented them from fully capitalizing on the moment.
The lesson here? You can’t always control when the next big technology will hit, but you can control how ready you are to integrate it.
Managed vs. Unmanaged Technical Debt
Managed technical debt means knowing you’re not perfect and planning around it. You acknowledge the limitations in your systems and strategies but have a roadmap to address them. This gives you enough flexibility to pivot when something disruptive comes along.
Unmanaged technical debt, on the other hand, is like leaving Tetris pieces scattered with no strategy. You’re constantly reacting to issues, patching problems instead of solving them, and when the "I" piece (disruptive technology) drops, you’re stuck. There’s no way to make it fit.
In other words, if you’ve been ignoring your technical debt or addressing it haphazardly, you’re playing with fire—or at least playing a losing game of Tetris.
The Secret Behind Early Adopters: Readiness for Disruption
Why were some companies able to jump on the ChatGPT bandwagon while others are still figuring it out? The simple answer comes down to readiness. Readiness isn’t just about knowing something is happening; it’s about being structured to take action as soon as it does. There are several layers of readiness that early adopters had in place:
Technological Readiness: These companies had already laid the groundwork by ensuring that their IT infrastructure, software, and data ecosystems were flexible, scalable, and open to integration. They didn’t need to overhaul their systems to bring in new tools—they had built a tech foundation designed to incorporate disruptive technologies quickly. In essence, they were prepared to plug ChatGPT into their operations without friction.
Cultural Readiness: Organizations with the right culture foster an environment that encourages curiosity, experimentation, and learning. These companies didn’t just wait for a memo from leadership to start exploring ChatGPT. They had teams empowered to try new things, fail fast, and iterate. Innovation was already a core part of their DNA, so embracing a groundbreaking tool like generative AI was seen as an opportunity, not a risk.
Organizational Readiness: This readiness goes beyond technology and culture—it’s about having the right structure, roles, and budgets in place to support rapid innovation. The companies that took off with ChatGPT already had digital transformation teams, innovation departments, or cross-functional squads that could take charge of POCs and pilots. Their leadership understood the need to allocate resources and budget for experimenting with new tech, which allowed them to quickly mobilize and integrate ChatGPT into their workflows.
Strategic Readiness: Lastly, early adopters had a clear vision for the role of emerging technologies in their future. These companies didn’t see ChatGPT as just another tool—they saw it as part of a broader strategy to leverage AI, automation, and digital transformation for competitive advantage. With a long-term vision in place, they could align their immediate actions with future goals, making it easier to integrate disruptive technologies like generative AI without losing focus.
The companies that excel at adopting new technologies like ChatGPT are those that understand readiness in these dimensions. They don’t just respond to disruption—they anticipate it and prepare for it at multiple levels.
Looking Ahead: The Next Disruption is Around the Corner
ChatGPT was a wake-up call, but it’s just the tip of the iceberg. The next wave of disruptive technology will likely hit sooner than we think, and companies need to be ready. The adoption curve could be even steeper, and the companies that fall behind won’t just be late—they’ll be out of the game.
Now’s the time to ask yourself: How prepared is my company? Do we have our Tetris pieces lined up, or are we leaving gaps? Are we managing our technical debt, or are we waiting for the next disruption to expose all the cracks in our foundation?
Whatever your answer, remember this: In the race to adopt new technologies, the companies with managed technical debt and a readiness mindset will always be the ones to clear the board.
