The emergence of Generative AI has sparked a heated debate about its potential to replace traditional roles in software engineering. If you are a leader in a tech company, it is critical to understand the impact of Generative AI in software engineering and guide them to take advantage of it.

We will unpack:

1. The implications of Generative AI on software engineering roles

2. Steps that engineers can take to develop their practices and meet the demand for software SMEs who understand AI

3. Philosophies (or whatever we’d want to call it) that organizations can adopt to ensure their people are equipped to learn, grow, and deliver on the promise AI offers

Your expertise influences the usefulness of AI

As an expert in your domain, consider this: you decide to hire an assistant and begin delegating your workload. You possess the skill to efficiently guide your assistants and evaluate their work, providing feedback for better output over time. Eventually, your assistant might even outsmart you in certain scenarios and give you a competitive edge as a team over the competition.

Now, let’s imagine the scenario where your organization hires for an entry-level position and equips them with an assistant. They have not gained enough experience to judge the quality of outputs from the assistant and lack the ability to provide constructive feedback. So, they may underutilize the assistant or, worse, ship bad-quality output by blindly trusting their assistant’s work.

Just like that, the productivity gap between the experts and the entry-level staff members can increase over time. The less experienced could face the risk of being replaced by AI assistants orchestrated by the experts. Gartner explains that the same phenomenon will likely happen to the software industry:

To stay competitive in the ever-evolving tech landscape, junior software engineers must prioritize acquiring in-depth domain expertise. Conversely, experienced professionals should focus on automating tasks and leveraging Generative AI’s capabilities to enhance productivity and efficiency.

Economic Factors Influencing Demand for Engineers

Suppose your team’s total output became 3 times greater thanks to the successful adoption of Gen AI at all levels. Does this mean you should consider downsizing your team to ⅓ the original to maintain the previous output but at a fraction of the overhead?

In a thriving economy, where taking on additional projects easily boosts your bottom line, you would want to expand your workforce. Leveraging AI can amplify every individual’s output with code completions, AI-assisted debugging, and generating unit tests and documentation. The increased productivity will lead to more revenue opportunities per head. If you choose to downsize your employee count, you are merely generating ⅓ of revenue compared to your competition.

Conversely, during economic downturns where finding new projects is challenging, reducing the workforce while hoping to maintain a certain level of productivity boost from Gen AI makes sense. If you can retain junior-level workers, it is a great time to upskill their software engineering fundamentals and principles to recover strong.

AI as a Tool for Enhanced Productivity

Rather than pondering if the demand for software engineers will increase or decrease due to AI, the more critical question is whether organizations will adopt operational models that effectively harness augmented productivity per employee. However, a comprehensive approach beyond mere training in how to use AI tools is needed; as Gartner advises, this approach must focus on fostering deep, foundational engineering expertise among software engineers, a process that takes years of ongoing effort and learning.

This perspective aligns with the findings of the Gartner report, which asserts that through 2027, Generative AI will not only transform roles within software engineering but also demand that 80% of the engineering workforce upskill to meet evolving challenges.

Conclusion

In summary, while Generative AI has the potential to significantly enhance productivity in software engineering, it is far from a replacement for skilled engineers. Instead, the future will require engineers to cultivate their domain expertise and adapt to new technologies. The true challenge lies in navigating the changing landscape and ensuring that engineers are equipped to leverage AI as a powerful tool in their toolkit. By embracing continuous learning and upskilling, software engineers will remain indispensable in a technology-driven world.