Among all the departments within an enterprise, the ones that allocate the most significant portion of their budget towards AI technology are the product and engineering divisions. This strategic investment has the potential to yield substantial benefits, as indicated by McKinsey, where developers can accomplish certain tasks up to 50% more efficiently with the assistance of generative AI.
However, the effective implementation of AI technology is not merely a matter of financial investment. It requires a nuanced approach. Enterprises must carefully determine the appropriate allocation of funds for AI tools, weigh the advantages of AI against hiring new personnel, and ensure that the training provided aligns with the organization’s objectives. Recent research underscores the importance of considering the user demographics when implementing AI tools, revealing that novice developers tend to derive greater benefits from AI compared to their more seasoned counterparts.
Failure to conduct these strategic evaluations could result in underwhelming outcomes, financial resources being squandered, and potential attrition of valuable staff members.
At Waydev, our focus over the past year has been on exploring the optimal utilization of generative AI in our software development workflows, refining AI-driven products, and evaluating the efficacy of AI tools within software teams. Based on our experiences, we have distilled key insights on how enterprises should prepare for a substantial investment in AI technology for software development.
Initiate a Proof of Concept
Many of the AI tools currently emerging for engineering teams are founded on innovative technologies, necessitating significant internal efforts for integration, onboarding, and training.
When the Chief Information Officer (CIO) is deliberating between expanding the workforce or investing in AI development tools, the initial step should involve conducting a proof of concept. Our corporate clients who are integrating AI tools into their engineering departments typically embark on a proof of concept phase to ascertain the tangible value generated by AI and quantify its impact. This stage serves not only to rationalize budgetary decisions but also to foster team-wide acceptance of the technology.
Begin by identifying the specific areas within the engineering team that could benefit from enhancement. Whether it pertains to code security, productivity, or developer satisfaction, leveraging an Engineering Management Platform (EMP) or Software Engineering Intelligence Platform (SEIP) can facilitate monitoring the influence of AI adoption on these key performance indicators. The metrics employed can vary, ranging from tracking efficiency through cycle time, sprint duration, or the ratio of planned-to-completed tasks. Has there been a reduction in the number of errors or incidents? Are developers reporting an enhanced experience? It is crucial to incorporate metrics that gauge value to ensure that quality standards are upheld.
Furthermore, it is essential to evaluate outcomes across a spectrum of tasks. Avoid confining the proof of concept to a specific phase of coding or project; instead, diversify its application across various functions to observe how AI tools perform in distinct scenarios and with developers possessing varying skill sets and job responsibilities.
