When businesses are looking to optimise resources and cut costs, investing in new technology or upgrading their systems can seem like an expense that isn’t worth the risk.

But even though a company may think it is saving money by not investing in new tools, upgrades, and digital transformation projects, the ‘technical debt’ it will accrue from holding onto its older systems will quickly mean it is paying much more in the long run. 

What is technical debt?

Technical debt builds as technology becomes more dated and unfit to effectively meet a company’s needs. In this day and age, technology can be considered legacy in a matter of years – and sometimes even sooner. If a company outgrows its technology, or it no longer serves its purpose properly, it can become a blocker to progress and growth rather than an aider of it. 

Directly, technical debt means it costs more to run and maintain systems. But it also impacts company efficiency, productivity and innovation, creating a broader debt that becomes increasingly difficult to pay off. The same can be said for investing unnecessarily in new technology or too quickly, when instead only minor upgrades or maintenance are needed. 

This is why it’s crucial to be able to effectively assess what technology a company actually needs. Not only that, but to also have a flexible amount of talent and skills available for when system maintenance is required and for when a digital transformation project is required. This is all part of the overall digital evolution journey.  

How can a company avoid falling into technical debt? And if it has debts needing to be paid off, how can it move back into the green? 

Assessing company needs

A hesitancy to adopt new tools can be driven by the idea of having to take on new software and transform working processes. But a digital evolution is not a case of simply ripping up and replacing what is in place. While holding onto legacy systems can leave a company accruing technical debt, they also hold the key to understanding current workflows and what an organisation needs. 

By collecting historical data from the various systems in operation, a company can assess its legacy application suite and see what applications are cost- and resource-intensive – this shows what’s working, what’s blocking processes, and even what isn’t being used. Decision makers can then cut down their tech stack, resolve issues in business-critical applications and optimise existing resources. 

Not only will their staff be working more efficiently and using tech with a purpose, but more money will be saved. This can then be invested into upgrading existing software and integrating new solutions if needed, or hiring outsourcers to help carry out such changes. 

With this evaluation completed, a company is better placed to plan ahead and create an adaptable, future-proofed infrastructure. And to avoid accruing technical debt, this must be an ongoing process.  

Approaching the world of AI

Today, there can be no conversation about technical debt without mentioning AI. According to McKinsey’s latest Global Survey on AI for 2024, AI adoption has surged to 72% after hovering around 50% for the previous six years.

The technology is becoming a necessity in some capacity for all businesses and so companies must think about plans on how to best integrate it. If they have already done this, then it becomes a case of how to improve/fine-tune models and adopt newer tools. 

For any AI project, companies must outline why they are integrating the tool and what business value they hope to derive from it. What data will it use? How will it help workers increase their efficiency? 

This is especially apparent with generative AI (GenAI). The survey also revealed that two thirds (65%) of respondents said their organisation is regularly using GenAI – more than double the percentage from McKinsey’s survey ten months ago. 

Having a clear overview of why you are using GenAI can choose what tool to use for you. Some organisations may build their own models, whereas others will leverage pre-trained, open-source large language models. It’s important to note that the quality of data fed into the model will determine how accurate, effective and valuable it is for your business. 

While AI’s rapid evolution has changed how companies view and use their technology going forward, tools such as GenAI are still in their early stages. To ensure their successful adoption, companies need to develop ethical frameworks that can guide how they are integrated into the business and used by workers. 

Paying off your technical debt

While Mckinsey’s survey shows a high percentage for current AI adoption, it’s worth highlighting that other reports and surveys quote much lower, but still sizeable, figures. However, one thing they all point to is that adoption will significantly increase year on year. 

In such an environment, companies can’t afford to accrue technical debt while others use AI to move further into the green. At the same time, those who rush AI adoption too quickly without a robust plan and strategy will acquire a new technical debt of their own. 

Companies need to effectively assess their current technology stack and business needs. In this position, they can best plan how to optimise their existing application suite and resources, implement new tools and AI, and ultimately avoid or reduce their technical debt.