What are the real upskill needs for software engineers in the AI Era?

I have been posting quite a few articles in which I have encouraged people in different professions to educate themselves on what AI means for them, their company, and the potential future of their work.

Gartner says that generative AI will require 80% of the engineering workforce to upskill through 2027. AI will spawn new roles in software engineering and operations. Furthermore, according to Gartner, "AI will transform the future role of software engineers, but human expertise and creativity will always be essential to delivering complex, innovative software."

Gartner's press release, "Longer Term, Organizations Will Require AI Engineers to Build AI-Empowered Applications" divides AI and its impact on the software engineering role in three ways (from the press release):

• In the short term, AI tools will generate modest productivity increases by augmenting existing developer work patterns and tasks. The productivity benefits of AI will be most significant for senior developers in organizations with mature engineering practices.
• In the medium term, AI agents will transform developer work patterns by enabling developers to fully automate and offload more tasks. This will mark the emergence of AI-native software engineering when most code will be AI-generated rather than human-authored.
• In the long term, while AI will make engineering more efficient, organizations will need even more skilled software engineers to meet the rapidly increasing demand for AI-empowered software.

Gartner press release states that “building AI-empowered software will demand a new breed of software professional, the AI engineer,” said Walsh. “The AI engineer possesses a unique combination of skills in software engineering, data science, and AI/machine learning (ML), skills that are sought after.”

David Gewirtz, Senior Contributing Editor at ZDNet, finds four challenges in AI-generated code left out of the latest Gartner report. These omissions are as follows, according to Gewirtz:

1. It's always necessary to make revisions to get the code to meet spec.
2. The substantial testing and quality control required for all software.
3. The need for updates, maintenance, bug fixes, and performance tuning throughout the lifecycle.
4. It's much harder to maintain code you did not write.

Gewirtz states that it is not surprising that software engineers need to upskill, as programmers have always had to. I also encounter this when leading development teams. Some are receptive to learning new things, while others do not want to and are more comfortable keeping up with what they know from the past. The latter group is in a worse position from a career perspective when things get tough, and organizations are looking for people who can take the organization to the next level, whatever they are working on.

Omission 1: Revising coe to meet spec

Gewirtz's first omission is founded on the fact that even if AI generates the code, it will never be quite right on the first run. There will be bugs, and the work has to be revised on an ongoing basis.

Omission 2: Testing and quality control

The code has to be tested, as AI will sometimes hallucinate and just not work. This will require humans to review and understand the code. Gewirtz states, "The bigger the project, the more complex the code, the more we will need human programmers and project managers to shepherd this all through the process."

Omission 3: Updates, maintenance, bug fixes, performance tuning

Software is never static; bugs will need to be fixed, updates will need to be implemented, and performance will need to be tuned according to Gewirtz. None of these are things AI can do across the whole product.

Omission 4: Maintaining code you didn't write

The final omission is that working on code you did not write is always hard. Sometimes, the code is structured in a way that is based on a personal style that is not compatible with other programmers. Based on my experience, there is also a big difference between programmers when it comes to the quality of the code, and this could become an issue in the long run.

Gewirtz's article aims to convey the message that "beyond using AI to help program or using AI to make programs more capable, there is a lot of work to be done related to the fundamental logistics of programming projects."

It is important to recognize that Gewirtz is not saying that there is anything wrong with the Gartner report, but he wanted Gartner also to emphasize the four topics that he feels should have gotten more attention in the report.

One thing is certain: In any profession, you have to learn every day to be relevant and keep up with the direction of your domain. As a business owner, I must regularly refine and adjust?our offerings? to ensure we stay relevant and bring value to our customers. An example of this is when we delivered software pricing/packaging workshops for software vendors; now, we have added GenAI-related pricing characteristics to our TELLUS workshop offerings.

I would love to hear your views on this topic. How are you participating in the new era of AI, and how do you plan to get educated in whatever profession you happen to be?

Yours,

Dr. Petri I. Salonen

PS. If you would like to get my business model in the AI Era newsletters to your inbox on a weekly or bi-weekly basis, you can subscribe to them here on LinkedIn https://www.dhirubhai.net/newsletters/business-models-in-the-ai-era-7165724425013673985/