Software Engineers Must Upskill: Report reveals 80% Professionals Need to Adapt by 2027

Impact of AI on Software Engineering

AI is no more just a buzzword, but in fact, fundamentally changes the way software is being developed, Gartner study says, referring to the point that AI will not replace software engineers but enhance their skills. The AI tools are already being applied toward automating repeated coding tasks so that more creative and complex problems can be directed toward engineers. Thus with this change comes the upskilling of software engineers to stay relevant in the AI-driven industry.

Gartner predicts that we enter “AI-native” times, where code, generated by AI, becomes the new rule of law. Engineers will be told to adopt an “AI-first” approach and align prompt engineering with natural language as a way of nudge AI agents in the desired direction. As AI falls from being simply a tool toward being a co-developer, software engineers must acquire new competencies.

The Birth of the AI-Engineer

The AI is likely to give birth to a new class of professionals called “AI engineer.” While mastering the requirements of software development, ML, and data science, these engineers are going to design and deliver AI-based solutions in large scale. Companies are expected to upskill engineers as they move increasingly up the AI adoption curve.

Organizations will invest in AI developer platforms that simplify embedding AI within enterprise applications. Essentially, these platforms will force engineers to gain AI capabilities and thus add to the pressure on upskilling. In tomorrow’s workforce, AI engineers will be at the forefront-in other words, frontline professionals who possess the integration of traditional software skills with supermodern AI knowledge.

Why Software Engineers Need to Upskill

In the realm of AI, it is now crucial for existing and aspiring software engineers, more so those entering the workforce, to possess these associated skills. According to the Gartner’s research, these skills entail coding and debugging, which are no longer adequate in the contemporary workplace. For the software engineers to compete, they have to extend their experience to encompass the application of AI competencies, for example,

Machine learning algorithms
Data analytics
Natural language processing (NLP)

The study indicates a huge gap in the skills that engineers are required to learn to apply AI and ML in reality. Already, over 56% of tech leaders feel AI/ML know-how is the number one job in demand in 2024. This continues to prove that engineers must reskill or end up playing catch-up.

Implication for graduates in Engineering

Engineering graduates, especially BTech graduates, are going to join a workforce in which there will be an even more serious, much stronger demand than for just the traditional software skills. Upskilling in AI and machine learning is no longer a choice but a necessity. In every way possible, graduates should seek to get hands-on exposure to AI and ML through certifications, specialized courses, or hands-on projects.

In the near future, software engineers would need to be interdisciplinary, where they combined software engineering with data science and machine learning. In such a world, interdisciplinary collaboration would be the key to survival.

Preparing for an AI-Powered Future

Gartner’s findings are a wake-up call for the tech industry. By 2027, 80% of software engineers will have to upskill or get left behind as AI becomes a central component to software development. Anyone who accepts the challenge will be well prepared for the future. “Human experience will still be needed, but those who take the initiative to master AI/ML will be the architects of the innovations of tomorrow,” predicts Gartner’s Philip Walsh.

The software engineer must upskill to keep up in a rapidly changing tech industry. As development in AI is the new trend, people face challenges as well as opportunities, and it is those who embrace this AI revolution that will be in high demand in the years ahead.