AI could truly transform software development in 2026 – but developer teams still face big challenges with adoption, security, and productivity

Software development is expected to continue evolving at pace across 2025, according to industry experts, with technologies such as AI prompting an overhaul of traditional processes and practices.

The profession has been in the midst of a significant transformation over the last two years, largely due to the advent of generative AI and, more recently, agentic AI.

Stack Overflow’s 2025 Developer Survey found 84% of developers were using – or planning to use – AI in their workflows, for example.

Similar research from JetBrains underlined the scale of AI adoption across the profession, with the 2025 State of Developer Ecosystem finding 85% use the technology frequently in daily tasks.

The influx of AI tools in software development was a contentious topic across 2025, prompting concerns about security, the impact on junior developers, and the prospect of widespread job cuts as processes are automated.

While these alarmist claims have grabbed headlines and airtime, what has become clear is that developers themselves will have to adapt and evolve to compensate for the use of the technology, as GitLab field CTO Marco Caronna told ITPro in December 2025.

Shifting developer priorities

Across the year ahead the focus will change, however. Martin Reynolds, field CTO at Harness, believes that the excitement surrounding AI-powered coding will subside as teams shift their focus toward better quality control practices.

“Organizations will move beyond using AI for coding alone, applying it to enhance testing and quality control,” he said. “AI-powered coding is becoming more refined and is already helping teams move faster, but so far, productivity gains at the front end have been erased by downstream bottlenecks.”

These "downstream bottlenecks” Reynolds refers to include an influx of bugs and “greater security exposure”. Both these issues were recurring talking points throughout 2025.

Research from CodeRabbit in December found that while developers may be moving quicker and improving productivity with AI, these benefits are offset by the fact they’re spending time fixing flawed code or tackling security issues.

Reynolds warned developer teams need to address the issue and reevaluate processes to streamline the broader development lifecycle.

“If left unchecked, this velocity problem will get out of hand fast – after all, no human can check thousands of lines of code and be expected to catch every issue,” he added.

So how will this quality control focus unfold? Reynolds noted that AI agents could play a key role here. There’s a sense that teams will essentially be stacking agents on top of assistants, but this could help reduce workloads and pick up slack for developers in what he described as a “continuous quality control” practice.

“This means crafting intelligent pipelines with multiple agents that can manage AI, optimize deployments, predict potential failures with high accuracy, and resolve incidents autonomously,” he explained.

“These agents will use a variety of models depending on their specific function,” Reynolds added. “This will be a real breakthrough for AI-assisted development in 2026, building trust in AI across the SDLC and ushering in a wave of automated processes that no longer require a human in the loop.”

Vibe coding will mature

“Vibe coding” was named the Word of the Year by Collins Dictionary, and the trend is likely to continue in 2026. Vibe coding involves using AI to automate coding processes, and gained significant traction early on last year after OpenAI co-founder Andrej Karpathy coined the term.

Research from Fastly in September found the practice was becoming more popular among developers, particularly those at a senior level. A host of industry figures, including Google CEO Sundar Pichai, hailed the trend as a landmark shift in software development.

Steven Webb, Capgemini’s UK CTO, believes 2026 will be the year when “AI-native engineering goes mainstream” as vibe coding practices mature and AI code generation gains further traction.

“Vibe coding, already crowned Collins’ Word of the Year for 2025, will take off in earnest, fundamentally reshaping software delivery pipelines,” he said.

This isn’t simply about speeding up development processes, however. Webb noted that AI code generation represents an opportunity to streamline modernization for enterprises globally.

“AI-driven code generation can rewrite legacy estates, reduce technical debt, and refactor entire modules autonomously, enabling organizations to break free from brittle, ageing systems at a pace previously impossible,” he claimed.

Security and governance in the spotlight

Webb warned that while AI has great potential in these areas, this means “trust again becomes a major concern”.

“Organizations will need strong traceability, provenance controls, and automated assurance mechanisms to ensure safety, security, and long-term maintainability,” he said.

“The maturity journey ahead is significant, but once confidence is established, we’ll see widespread, production-scale adoption.”

AI-related security concerns are multi-faceted, with cybersecurity practitioners warning about the malicious use of the technology and “shadow AI” as well as the fact the technology could create downstream security risks for development teams.

JetBrains’ State of Developer Ecosystem study found many devs are still wary about handing the reins over to AI tools entirely. Nearly half (48%) told the firm they prefer to “stay hands-on” when using AI in core tasks such as testing or code reviews.

Reynolds echoed Webb’s comments on security and trust, adding that the rapid pace of AI adoption could have a significant impact on software supply chain security in 2026.

“Many enterprises will say they have learnt supply chain security lessons after 2023’s SolarWinds breach – but that doesn’t mean their AI has,” he said.

“With AI expanding software supply chain volume and complexity, similar incidents become more likely and severe, as a single compromised component could cascade across thousands of enterprises.”

Reynolds warned that many AI coding tools are “trained on historical repositories”, meaning there’s a risk they lack “real-time CVE awareness and will happily draw from vulnerable libraries”.

AI-generated code is an equally pressing issue for developers, he added.

“Developers can’t trace where suggestions originated or whether they incorporate licensed code or vulnerable components,” Reynolds explained. “That makes it near-impossible to work backwards and identify if the company’s software is affected by issues like Log4Shell.”

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