Is AI an Opportunity to Incorporate Technology with Less Technical Debt?

GenAI's opportunity to avoid the anchor of legacy missteps

Technical debt, a metaphor introduced by software developer Ward Cunningham, refers to the cost of additional rework caused by choosing an easy, quick solution now instead of using a better approach that would take longer. In enterprise IT, this debt accumulates over time, manifesting as outdated systems, inefficient processes, and security vulnerabilities that hinder a company's ability to innovate and respond effectively to market changes.

Here’s my concern. With a new technology update being released every day, whether a new powerful LLM or a new GPU chip to accelerate those LLMs, the hype often leads to the actual utility of these solutions.

It would behoove enterprises to be thoughtful in adopting solutions that may provide immediate gratification but can saddle an organization with years of legacy problems and costs and prevent future agility.

The Perils of Technical Debt

Technical debt encompasses various forms, from the tangible, like legacy systems that are costly to maintain, to the intangible, such as the lack of documentation or the use of outdated technology stacks.

The consequences of unchecked technical debt are significant. It was behind the cancellation of more than 13,000 Southwest Airlines flights in December 2022, causing a massive operational and PR crisis.

Furthermore, technical debt is a primary driver behind numerous security vulnerabilities, leading to costly breaches in major companies like Google, Apple, and Microsoft. The financial implications are staggering, with estimates suggesting that addressing this debt would cost approximately $1.52 trillion, with an annual impact of $2.41 trillion on the U.S. economy due to cybersecurity incidents, operational failures, and maintenance of antiquated systems.

GenAI as a Solution to Alleviate Debt

Generative AI presents an unprecedented opportunity to tackle technical debt head-on. By automating the creation of code, documentation, and even architecture designs, GenAI can significantly reduce the initial accumulation of debt. More importantly, it can help in refactoring and updating legacy systems and translating outdated code into modern, efficient, and secure languages and frameworks without the manual overhead traditionally required.

Expert Insights into Technical Debt

Industry experts believe that the advent of GenAI tools marks a turning point in the fight against technical debt. GenAI is not just about creating content; it's about enabling more innovative development processes that inherently avoid the buildup of debt. Such tools can analyze vast codebases to identify debt hotspots and suggest or even implement fixes autonomously.

Noted author and DevOps maven John M. Willis notes that one of the most important technologies we’ve seen in the last twenty years is Docker, which allows the packaging of applications in portable containers.

Docker uses containerization to ensure consistency across different development, testing, and production landscapes, mitigating challenges with technical debt. Before Docker, code inconsistency contributed to technical debt. Docker resolves this by packaging applications and dependencies in a lightweight package, ensuring consistency across any environment.

Docker revolutionized the technology landscape by reducing environmental discrepancies and streamlining deployment processes. Similarly, GenAI is on the verge of a breakthrough that has the potential to reshape software development. GenAI aims to tackle technical debt and streamline the development process by integrating predictive analytics and intelligent automation into development workflows.

However, we should also keep in mind that a quick solution today could be detrimental in the long term if not approached with a critical eye.

Challenges and Opportunities

Adopting GenAI is not without its challenges. Organizations must navigate the potential for creating new forms of technical debt through overreliance on AI-generated solutions and the need for rigorous validation and testing of AI-generated code. While we espouse the virtues of Microsoft Copilot for writing code and go gaga over demos like Devin, the world’s first AI coding “robot” it’s not clear that they aren’t just exacerbating the problems of the past.

However, the opportunities for reducing existing debt and preventing future accumulation are immense, offering a path to more sustainable and agile development practices.

Adopting GenAI with Less Technical Debt

• Gradual Integration: Start small with GenAI, integrating it into parts of your development process where it can have the most immediate impact, such as documentation generation or code review.
• Skills Development: Invest in training your team to work effectively with GenAI tools, focusing on understanding the strengths and limitations of AI-generated outputs.
• Continuous Evaluation: Regularly assess GenAI's impact on your development processes and technical debt levels, adjusting strategies as needed.

Moving Forward Debt-Free

Generative AI offers a promising avenue for organizations to reduce and manage their technical debt, enabling them to focus on innovation and growth. By adopting GenAI thoughtfully and strategically, businesses can free themselves from the shackles of past decisions and move forward into a future where technology enables, rather than encumbers, progress.

Prompt of the Week: Generative AI Implementation Analysis

One exceptional use of ChatGPT is to act as a consultant who can help me walk through a plan for a client. I use the role of an appropriate and well-known consulting group to provide guidance.

Then, I prompt ChatGPT to gather information on the organization before going through the steps. It complements the discussion above about technical debt as it can help you take a measured approach to adopting Generative AI without falling prey to short-term thinking.

This prompt aims to evaluate an organization's potential for adopting generative AI technologies, focusing on identifying opportunities to improve productivity and drive growth.

You will act as a management consultant from McKinsey's Quantum Black group. 

Using your knowledge of current generative AI capabilities and industry trends, analyze our organization's opportunities for implementing generative AI technologies. Consider operational processes, product development strategies, and market positioning. 

Your analysis should address the following key areas:

Productivity Improvement:

Identify specific operational areas or processes within our organization that could benefit from the efficiency and automation offered by generative AI. Examples might include automated content creation, data analysis, customer service enhancements, or streamlined design and development processes.

Assess the potential impact of generative AI on improving the productivity of these areas. Consider factors such as time savings, cost reduction, error minimization, and overall operational efficiency.

Opportunities for Growth:

Explore how generative AI can drive growth for our organization. This could involve new product innovations, enhancing customer experiences, entering new markets, or creating new business models.
Evaluate the feasibility of these growth opportunities, considering our organization's strengths, market demand, competitive landscape, and the capabilities of current generative AI technologies.
Comparative Analysis:

Conduct a comparative analysis of the potential benefits of focusing on productivity improvement versus pursuing opportunities for growth through generative AI. Consider the short-term and long-term impacts, resource allocation, risk factors, and alignment with our organizational goals and values.

Your analysis should be comprehensive, providing actionable insights and recommendations on the strategic implementation of generative AI to maximize both productivity and growth. 

Support your analysis with relevant data, case studies, or industry benchmarks where possible.

You will start by conducting an interview about the business to inform your analysis.         

The result will likely give you a list of questions to answer as it did in my example:

You can answer the questions in a word processing document and then upload it to ChatGPT for analysis. You can also add a line to make the prompt more interactive and walk you through the process. Copying and pasting the interview questions in a Google or Microsoft Doc is probably more practical for gathering the information, but you do what works best for you.

Create the interview by asking questions one at a time until you have all the information you need.