How Is AI Changing the IT Industry in 2026?

In 2026, AI in the IT industry is moving from suggesting code to carrying out multi-step tasks on its own: agents read a repository, plan changes across many files and run tests. Adoption is outpacing trust — in Stack Overflow’s 2025 survey, 84% of developers said they use or plan to use AI, yet 46% don’t trust the accuracy of its output. Whether a company benefits comes down to oversight: limits on what an agent may do, escalation points to a human, and an audit trail.

What is agentic coding, and how is it different from code suggestions?

AI assistants a few years ago finished a line of code a person had started. An agent gets a task at the level of “fix this bug” or “add this feature,” reviews the repository on its own, plans changes across several files and checks the result with tests before a person even looks at the code. Tools such as Claude Code, Cursor and GitHub Copilot offer this mode today. It is a full work cycle, not autocomplete. The scale is growing: Stack Overflow reports that agent usage has doubled since its 2025 survey, while concerns about the quality of generated code have grown alongside it.

Is one agent enough, or do you need teams of agents?

Increasingly, instead of one general-purpose agent, a team of specialized ones does the work: one plans, one writes code, one tests and checks the result against requirements. The industry has been through this before, in the move from monoliths to microservices — instead of one system doing everything, specialized components with clear interfaces. Gartner forecasts that by 2028 about a third of enterprise software applications will include agentic features (up from under 1% in 2024) and that at least 15% of day-to-day work decisions will be made autonomously (Gartner, June 2025). That is a forecast, not a measurement.

How is AI changing IT process automation?

Classic automation runs a scenario described in advance: if event A happens, do B. An AI agent copes where the scenario can’t be written down up front because the task needs judgment about context — for example, classifying a ticket, pulling data from several systems, or preparing a reply for approval. That doesn’t mean an agent replaces every workflow. Gartner points out that many use cases labelled agentic today don’t need agents at all, and repeatable, well-defined processes still work best as plain automation. We cover how to choose in where to start with business process automation and compare the types of solutions in types of business process automation. You can see how this looks in our offer on the Codari automation page.

How is machine learning changing software development?

Machine learning models now support code review, test generation, log analysis and anomaly detection in monitoring — that is, wherever there is a lot of repetitive data to search through. The gain is biggest on well-defined, measurable tasks. For architectural decisions the model suggests, but it doesn’t know the project’s business context, so the last word stays with a person.

Is AI-generated code secure?

Not by default — it needs the same review as code written by people. The Veracode report from July 2025 tested more than 100 models on 80 coding tasks and found OWASP Top 10 vulnerabilities in 45% of cases, with the security of generated code not improving across newer model versions. Developers see the same in practice: in Stack Overflow’s survey, 66% named answers that are close to correct but still wrong as their top frustration, and 45% said debugging AI-generated code takes longer than they expected.

The practical takeaway: static security analysis in the pipeline, security requirements spelled out in the task for the agent, and human review for high-risk changes. AI also works on the defensive side — it detects anomalies in network traffic and speeds up incident response — but attackers get the same capabilities.

Why does governance decide whether AI deployments succeed?

The more decisions an agent makes, the more important the question becomes: who supervises it, and how do you check afterwards. Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of rising costs, unclear business value or inadequate risk controls. That is a list of management problems, not of model quality. In practice three things are becoming standard: clear limits on what an agent may do without asking, mandatory escalation to a human for high-risk decisions, and a full audit trail — what was automated, when and why.

What changes in how IT teams work?

The biggest change is in what a developer is responsible for: less writing, more directing and verifying.

Area	Before	In 2026
Developer’s role	Writing code line by line	Directing agents and verifying results
Code review	A person reads every line	A person oversees high-risk points, automated analysis filters the routine
New skills	Language and framework knowledge	Framing tasks for agents, orchestration, integration protocols (e.g. MCP)
Risk	An error in a single line	A systemic error that scales faster and needs different safeguards

How do we approach this at Codari?

Agentic AI supports our team’s daily work, but it doesn’t replace the domain expertise we’ve built over years — that decides what needs to be done and how, while AI speeds up the execution. We don’t hand AI whole chunks of functionality to design on its own: architecture and design decisions stay with people who know a client’s business process inside out. AI works well where a task is well defined and repeatable; where experience and context matter, the team decides, not the tool.

FAQ

Will AI replace programmers in 2026? Not in the sense of full replacement — the role is shifting from writing every line to directing agents and verifying results. According to Stack Overflow’s 2025 survey, 64% of developers don’t see AI as a threat to their jobs.

What is agentic coding? A way of working where AI carries out multi-step tasks on its own (reads code, plans changes, tests) instead of suggesting single lines on request.

Is AI-written code secure? It needs the same review as human-written code, and more for high-risk tasks. In Veracode’s 2025 study, models introduced OWASP Top 10 vulnerabilities in 45% of the tasks tested.

How many agentic AI projects will fail? Gartner forecasts (June 2025) that more than 40% of such projects will be cancelled by the end of 2027 because of costs, unclear value or weak risk controls.

What skills do IT teams need now? Framing tasks for agents, orchestrating multiple agents, familiarity with integration protocols like MCP, and — most important — judging when an agent’s output can be trusted and when it needs manual review.

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Wondering how to bring agentic AI into your company’s processes, not just the IT team? Get in touch — we’ll help you find a starting point with real oversight.