DEVOPSdigest

As technology rapidly advances, IT pros are increasingly saddled with technical debt. According to McKinsey(link is external), technical debt accounts for about 40% of IT balance sheets, with companies often paying an additional 10 to 20 percent on top of the costs of any given project to address this debt. This has been a weight on the shoulders of IT and operations leaders for over five years, as evidenced by recent Gartner I&O(link is external) surveys.

The Problem with More Code

Adding more code might feel like progress, but every new line can bring its own set of bugs, inconsistencies, and complexities that need managing down the road. Without a lasting solution, developers are just passing the problem on to someone else. This approach leads to a tangled web of quick fixes and temporary solutions, piling up technical debt.

Not only that, but code often only makes sense to the person who wrote it. Many long-standing enterprises — think well-established banks and insurance companies — have decades upon decades of systems running on ancient coding languages that no one even uses anymore. Now, the next wave of developers has to figure out how to make this old code play nice with the latest technologies.

That's why all the hype around generative AI code assistants can be a bit concerning. These "smart" tools might churn out a lot of code quickly, but that could just add more spaghetti to an already tangled mess. And when you consider that AI sometimes produces code of questionable quality, you've got a recipe for a technical debt disaster.

In the end, code should be a means to an end — helping businesses automate workflows, decisions, and processes — not the other way around. If we focus only on producing more code, we risk missing out on the true benefits that generative AI can offer.

Achieving Meaningful Transformation with Low Code and GenAI

The obvious alternative to traditional coding is to take a low-code approach — one that is faster, more scalable, and more easily understandable for all stakeholders. This avoids all the pitfalls of manual coding and makes modernization easier down the road.

But how do low coders still benefit from the power of GenAI?

GenAI's real value lies in innovating and optimizing business processes from the center out. This means starting with your desired customer outcomes, rather than in any particular channel, where business and process logic often gets buried and creates dozens of silos. GenAI should be used to streamline, modernize, and automate workflows rather than adding to the existing mess of code.

More and more low code application development environments are infusing GenAI features to help developers speed the process. These include enhancements like workflow suggestions, generation of testing data, system integration support, and other areas that will help developers save even more time and automate even more manual work.

However, the holy grail of GenAI in low code is not just productivity and speed gains. We can also imagine a world where GenAI is the catalyst for innovation. Providing GenAI models with examples of application best practices could suggest all new ways for enterprises to reimagine how they structure their workflows and get work done. And with GenAI, this is entirely scalable so that it could consider scenarios across many different industries.

The future of software development is moving towards less or no code, emphasizing the importance of developers who understand business needs and solve problems, rather than just being code warriors.

GenAI can also prove helpful when applied to reassessing current workflows rather than starting new ones from scratch. By letting GenAI analyze existing models, it could identify areas for improvement and suggest new paths forward.

Consider this as the ideal usage of GenAI in an enterprise setting: Instead of generating new code at turbo speed, the AI analyzes existing workflows, identifies inefficiencies, and proposes streamlined processes. This not only reduces the amount of code, but also ensures that the code that remains is cleaner, more efficient, and easier to maintain. This approach helps mitigate the risk of future technical debt by focusing on quality over quantity.

Key Takeaways for IT Leaders

Addressing technical debt stands to transform the overall efficiency and effectiveness of an organization's technology infrastructure, offering a whole heap of benefits to leaders, IT teams, and customers alike.

First off, less technical debt means lower long-term business costs. With fewer hours spent patching, maintaining, and updating systems, IT resources can be strategically reallocated. Teams will have more bandwidth to focus on innovating and improving workflows and product capabilities, driving business growth and competitiveness.

Cleaner, more efficient code also leads to enhanced security. A less complex codebase reduces the opportunity for security breaches. Further, intuitive programs lower the learning curve for new developers and minimize the risk of introducing new vulnerabilities. This results in a more robust and reliable technological environment, safeguarding sensitive data and operations.

Finally, customers benefit from fewer interruptions and faster, more responsive applications. Better implementation of AI and machine learning also facilitates more personalized services, enhancing customer satisfaction. Delivering seamless and tailored experiences allows companies to build stronger customer relationships.

Eliminating technical debt for good will require a shift in focus for many developer teams. GenAI has a pivotal role to play in this transformation — not by producing more code, but by enabling businesses to innovate and optimize their operations. By leveraging GenAI to automate processes, IT leaders can reduce technical debt, lower costs, and deliver better outcomes for their customers. The future of business innovation lies in harnessing the power of GenAI to drive meaningful change beyond mere code generation.