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Maximizing AI Investments: Key Considerations for Enterprises

Maximizing AI Investments: Key Considerations for Enterprises

The Impact of AI Technology on Enterprise Departments

Of all enterprise departments, product and engineering spend by far the most on AI technology. Effectively implementing AI technology can generate huge value, as developers can complete certain tasks up to 50% faster with generative AI, according to McKinsey.

However, simply investing in AI technology is not enough. Enterprises need to understand how much to budget into AI tools, how to weigh the benefits of AI versus new recruits, and how to ensure their training is on point. A recent study also found that the decision of who is using AI tools is critical for businesses, as less experienced developers get far more benefits out of AI than experienced ones.

How AI is Revolutionizing Software Development

Failure to make these calculations could result in lackluster initiatives, a wasted budget, and potential loss of staff.

At Waydev, we have dedicated the past year to extensive experimentation on the optimal use of generative AI within our own software development processes. Our efforts have involved not only the development of AI products, but also the measurement of their effectiveness within software teams. Through this journey, we have garnered invaluable insights on how enterprises can best prepare for a substantial AI investment in software development.

Understanding the Value of AI in Engineering Teams

When your CIO is faced with the decision of allocating budget towards additional hires or investing in AI development tools, it becomes crucial to conduct a proof of concept. For our enterprise customers integrating AI tools into their engineering teams, conducting a proof of concept is a pivotal step to determine the tangible value generated and its impact. This not only plays a key role in justifying budget allocation but also in fostering acceptance across the team.

Defining Areas for Improvement

The initial step involves defining the areas within the engineering team that require enhancement. Whether it pertains to code security, velocity, or developer well-being, clarity in identifying the improvement areas is essential. Subsequently, utilize an engineering management platform (EMP) or a software engineering intelligence platform (SEIP) to monitor the impact of AI adoption on these variables. The metrics could encompass tracking speed through cycle time, sprint time, or the planned-to-done ratio. Evaluating the reduction in the number of failures or incidents, as well as the enhancement in developer experience, is imperative. Always incorporate value tracking metrics to ensure standards are not compromised.

Assessing AI Tools Across Different Tasks

When evaluating AI tools, it's important to assess outcomes across a variety of tasks. Instead of restricting the proof of concept to a specific coding stage or project, broaden the assessment to include diverse functions. This approach will help in understanding the performance of AI tools under different scenarios and with coders of varying skills and job roles.

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