Many organizations — including some of the highest-performing companies in the world — are struggling under the weight of technical debt.
IT and development teams waste anywhere from 20 percent to more than 40 percent of their time and resources on technical debt. In other words, one or two days every week are spent shoring up legacy infrastructure, patching up brittle systems and building new services on weak foundations.
In addition to eating up a significant portion of the IT and software development budget, technical debt has real consequences for the business. Projects take significantly longer than expected, and users experience downtime and disruption when quick fixes create cascading problems. Organizations lose the agility needed to remain competitive in today’s fast-paced marketplace.
These issues are becoming more critical as organizations adopt AI. Underperforming systems, data silos and difficult integrations make it difficult to capitalize on the benefits that AI can deliver.
The term “technical debt” originally referred to the additional work needed as a result of shortcuts and suboptimal decisions made in software development. Developers sometimes take an expedient approach to roll out new code quickly or deliver a proof of concept. If not paid back promptly, however, technical debt can make software difficult to maintain and increase future development costs.
Today, technical debt has become synonymous with any legacy or outdated technology in the enterprise. Hardware upgrades that are postponed and systems that aren’t integrated create technical debt that can accrue a substantial amount of interest over time. IT teams spend a lot of time fighting fires rather than completing projects that drive the business forward.
Not surprisingly, a 2024 survey by 451 Research found that two-thirds of organizations are modernizing their data centers or planning to do so within the next two years. Upgrades are needed to meet the demands of AI workloads and manage large volumes of data.
However, outdated IT equipment is only part of the problem. Many organizations are finding that legacy data creates as much technical debt as legacy systems. Systems designed to perform specific tasks don’t talk to each other or share data. While some data is highly structured and well-managed, other data is collected and stored in the most expedient way possible. The result is data silos that hinder many AI projects.
In fact, data silos are one of the biggest roadblocks to AI adoption. Organizations face significant time and effort to normalize data and aggregate it into a data lake or data lakehouse so that AI-enabled applications can access it. They must also develop enterprise data standards and data governance programs to improve data quality and sharing.
Getting a handle on technical debt starts with understanding its scope. The IT team and software engineers are the best source of this information, but they don’t speak the same language as business executives. Anonymous surveys allow technical staff to speak candidly about the challenges they face and the business impact of those challenges.
Those findings should then be translated into actionable intelligence that can guide IT investments and strategic initiatives. IT leaders will be able to offer a business case and link investments directly to cost savings and other business benefits. Business leaders will have a clear understanding of the ROI of reducing technical debt.
Technologent’s consultants have the technical expertise and business acumen to help guide this assessment and identify high-impact opportunities to reduce technical debt. We can help you prioritize initiatives that will reduce recurring costs and remove barriers to business objectives, regardless of their technical complexity. If you’re planning an AI implementation, we can help you lay the foundation for long-term success.