We are no longer anticipating an AI future: It is here. AI’s rapid transformation has made it a common and expected part of business. A recent report showed that 92% of Fortune 500 companies have already adopted AI tools. Google reported that more than 25% of its code is now generated by AI. Rapid adoption has often precluded thoughtfulness and strategy in applying AI usage — especially in software development. With software more critical than ever to overall business success, organizations need to implement the right risk management strategy to adopt innovation and positively impact productivity through AI while maintaining safety and security. As AI is still evolving and is trained on human-written code, it is intrinsically imperfect. Just as to err is human, the same can be said for AI. Developers must take this code with a grain of salt, understanding that AI can and will make mistakes when generating code, and those mistakes come with a cost. Research shows that about half of the AI-generated codes are at least partially incorrect. On top of that, when left to run wild, AI can increase costly technical debt, a problem that already plagues businesses. Poor-quality software is also estimated to cost at least $2.4 trillion. Therefore, developers must prioritize quality when using AI coding assistants
Code quality issues can be mitigated, but not without thought. To prioritize code quality, support software teams and reduce the harmful effects of code churn and technical debt simultaneously, CIOs need to invest time and effort in developing an AI coding strategy. A solid strategy, with a ‘trust and verify’ approach at its core, is the best way for organizations to embrace the productivity gains associated with AI while avoiding its pitfalls.
Thoughtful AI Use Enables Better Developer Productivity
AI is a critical tool in the developer’s toolbox — and, consequently, essential for organizations. Leaders simply can’t sit back while their competition achieves success with AI tools. Action must be taken. Embracing AI coding assistants while creating a clear path for using them allows an organization to bolster developer productivity and lighten their workloads while ensuring top-quality code in the software they deploy.
Leveraging AI coding assistants can positively impact developers’ workload, relieving burned-out developers. Research shows that 73% of developers have experienced burnout at some point in their careers. While already high, that number could further grow as businesses raise their software-related expectations. By taking over and performing repetitive, tedious coding tasks, AI allows developers to lighten their loads while focusing on more innovative projects and priority work that requires human creativity and critical thinking from the start.
That said, AI is not perfect. It is still learning and evolving. Right now, these coding assistants are susceptible to a bevy of mistakes, errors and vulnerabilities. For example, a study conducted last year found that about 30% of AI-generated code needed to be corrected, and about 23% were partially incorrect. Additionally, another study from Stanford found that programmers using AI were more likely to write insecure code, leaving software open to security threats and vulnerabilities.
Software teams need to be cognizant of that while using AI coding tools. To get full value, developers need to play a role in owning code quality assurance. Only by taking an approach that considers these truths will teams achieve tangible productivity with AI.
‘Trust and Verify’ is the Key to Effective AI Use
As AI coding assistants continue to experience rapid growth and transformation, CIOs need to develop a strategy quickly and thoughtfully to make the best utilization of these tools from a business perspective.
This strategy should start with a ‘trust and verify’ mindset. Organizations should be able to trust AI output to help keep up with their competitors. At the same time, software development teams must be able to verify the quality and security of the generated code, especially when moving at a breakneck pace.
To balance these needs, developers must have code testing and analysis in place as early as possible in the software development lifecycle (SDLC). Early detection catches problems before they become significant and costly. The best way to do this is through a combination of trusted, automated tools and human critical thinking. Additionally, developers should keep in mind that the most logical approach for code analysis is a deterministic one. This is relevant to the AI-generated code which is by nature non-deterministic and will help drive predictable outcomes. Paired with the ‘trust and verify’ mindset, this is the best way for developers to ensure the health and quality of their code.
Together, humans and tools can better guarantee software quality that powers business. Alongside tools to automate the testing processes, early detection makes fixing bugs that impact software quality cheaper and easier.
Standardize AI’s Usage and Regularly Evaluate
Another component CIOs should consider when developing a robust AI coding strategy is standardizing AI use and policies. Disjointed practices make it more difficult for teams to collaborate and for organizations to solve the tricky problem of measuring AI’s return on investment (ROI).
Standardization is vital. Microsoft found that 52% of its employees leverage AI for priority tasks, and 78% use their own AI tools. Development teams that lack consistency can neither work together nor easily pick up tasks where a colleague left off. Collaboration is crucial in software development. Teams need to work together to determine where AI in code development makes sense, then educate and train their teams on those best practices to avoid productivity losses associated with disjointed use. This can also cause security problems, as using AI tools incorrectly can allow vulnerabilities to slip through the cracks.
Regular evaluation of AI use and success also helps guide an overall strategy. By reviewing processes and policies, leaders can help ensure a business’s AI use transforms alongside its tools. We don’t know what this technology will look like in five years. Keeping up with its evolution will help optimize how it is used throughout SDLC, empowering developers to perform at their best while ensuring maximum output and widespread, standardized use.
Strategic Use of AI Coding Assistants Enables Better ROI Measurement
As AI becomes more entrenched within software teams, it is realistic to think businesses will increase their investments in this technology. Leaders expect ROI, but measuring it within the sphere of AI is challenging because these tools are so new. However, having a developed strategy and executing it can support this effort. A clear strategy allows organizations to better understand where their money is going and how these tools are impacting their product and bottom line.
In this vein, testing analysis will improve software quality and, consequently, help businesses reach their goals. Moreover, standardization and defined policies make any kind of use easier to measure, reducing the complexities of gauging against disjointed use. Moreover, routine evaluations of the strategy can create room for the necessary optimization and improvement and provide benchmarks for measuring ROI.
A strategy not only guides initial use but helps better direct it as we experience growth and change within AI. As organizations continue to pour money into these tools and software development as a whole, using these policies to measure their worth will only help development teams continue to churn out quality software at a higher rate.
Better AI Strategies = Better Future-Proof Business
AI is rapidly changing how businesses operate, which will only continue to be true. However, organizations are in danger of wasting their investments if they don’t leverage AI coding tools strategically and thoughtfully. Irresponsible and careless use could lead them to miss out on potential software development benefits while putting their businesses at risk. Effectively harnessing AI requires organizations to build and execute a clear strategy that emphasizes responsible use. Without this, the consequences could be costly.
A strong AI strategy that focuses on a ‘trust and verify’ approach, standardized use cases, and regular progress evaluations will maximize the organization’s tools, developers’ time and overall investments. This will not only improve current practices but also help better measure ROI and prepare organizations for the future of AI.
