Each year brings a new set of opportunities and challenges for CIOs, their enterprises and their teams. For software engineers, 2025 brought increased attention to AI integration and capturing efficiencies, as most organizations grappled with the volatility tied to macroeconomic conditions.
“We are at an interesting crossroads,” Arun Batchu, VP analyst at Gartner, said during a session at the firm’s IT Symposium/Xpo last fall. Enterprises had two key priorities: innovate with AI and bolster their software engineering practices and discipline.
While businesses pursued AI-native software engineering, teams faced difficulties last year, such as failure to shift mindsets, erosion of critical and foundational skills and the nondeterministic nature of large language models, according to Batchu.
Other challenges include managing costs and infrastructure complexity tied to building LLM-based applications and agents. Skills gaps have only exacerbated the problems.
“You need to invest in upgrading your skills; that’s going to be very important,” Batchu said. But organizations also need to be wary of overreliance on AI tools, which can lead to stunted skills.
Burnout is also a critical issue plaguing software engineering teams — another in a litany of issues expected to play out in 2026.
Enterprise expectations of their software developers have increased alongside growing AI adoption. More than two-thirds of developers say pressure has mounted to deliver projects faster, according to a HackerRank report.
“We can work really hard on this and in a highly competitive room, we can actually burn out,” Batchu said. “The management team needs to be aware of it.”
Challenges ahead
While the efforts to integrate AI into development life cycles are already changing workflows for software engineers, more transformation is on the way.
“First, we see shifts in the way we work,” Dave Micko, senior director analyst at Gartner, said during a session at the firm’s IT Symposium/Xpo. “Then we see shifts in what we build and deliver.”
The priority for software engineering teams last year centered around integrating AI into workflows, but CIOs will need to continue to maintain the competitive edge as AI-assisted software development becomes table stakes in 2026 and beyond.
Rather than predicting reductions in headcount, Gartner predicts the cycle will result in enterprises needing to tap more software engineers.
“The demand for differentiated software, and in turn, developers, is going to increase,” Micko said. “The only people who can do this at scale are software engineers.”
CIOs can begin to prepare by investing in their talent pools and upskilling team members, Micko suggested. IT leaders will also need to reconsider what they identify and track as metrics of success.
“As AI commoditizes that productivity in software engineering, effectiveness is going to be assessed based on creativity and innovation — instead of the traditional product-based measures, such as velocity, deployment frequency or, God help us, lines of code,” Micko said.
Skill profiles in 2026
Soft skills have steadily risen in importance as technology moves closer to the core of the business. New technologies, such as generative AI, have also contributed to the trend by democratizing technical skills.
“We call them power skills, like those who are good at analysis or have high levels of curiosity,” Skillsoft CIO Orla Daly told CIO Dive. “Those skills can be very successful in adding value in the context of AI … and some of those power skills can have an even greater impact in a technical capacity than previously.”
For software engineers, the core skill of coding is likely to evolve into the ability to analyze, validate and secure code that AI has generated, Daly said.
If all employees have some coding ability, the true competitive edge becomes which organization can create the best software, however that’s defined by the organization, from most customized or least vulnerable to quickest to market.
“By 2030, nobody is going to care about the productivity gains,” Micko said.
