It started as a groundswell and gained momentum throughout the year. Now, every article, conversation, and marketing plan seems to include something about AI and software development. As we move toward the end of the third quarter, let’s take a look at where we are as well as where we’re going.
The first thing I’ve noticed is that AI is no longer an experiment. It has become part of the operating environment. According to McKinsey’s 2026 global AI survey, nearly nine out of ten respondents said their organizations regularly used AI in at least one business function. Forty-four percent said AI was scaling across the enterprise, up from 38 percent one year earlier.
Those numbers tell us that adoption has accelerated. They don’t tell us whether companies are getting the results they expected. McKinsey found that only 37 percent of respondents could connect AI to an improvement in earnings before interest and taxes, essentially unchanged from the previous year. Companies are using more AI, spending more on AI, and introducing it into more parts of the business. Many still can’t show that it’s producing measurable financial value. That’s the central tension defining AI in 2026.
Software Development Became the Proving Ground
Software development has emerged as one of the most visible testing grounds for AI. The JetBrains Developer Ecosystem Survey 2026, which included more than 15,000 professional developers, found that 90 percent were using AI coding agents at work at least weekly. Sixty-eight percent were using them every day.
AI tools can now generate features, explain unfamiliar code, write tests, find defects, prepare documentation, and complete some development assignments with limited human involvement. The role of the developer is shifting from writing every line to directing, reviewing, and correcting work produced by machines.
That doesn’t mean the productivity question has been settled. METR reported in February 2026 that wider adoption of coding agents had made their productivity experiments harder to conduct. Some developers didn’t want to participate if they might be required to work without AI. METR’s preliminary results suggested that AI had begun accelerating some development work, but the organization said selection effects made the size of that improvement difficult to measure reliably.
I think that distinction matters. AI can increase output without increasing value. A development team may produce more code, close more tickets, and complete more pull requests while creating additional review work, security exposure, technical debt, and production failures.
The Validation Problem Is Growing
The New Relic 2026 State of AI Coding Report, based on research conducted by Hanover Research among 200 U.S. technology leaders, found that 67 percent said AI generated or significantly refactored between 51 and 75 percent of their organizations’ weekly code output. That’s a remarkable volume of machine-produced code entering corporate systems.
The challenge is that AI can generate code faster than most organizations can validate it. It can produce convincing explanations, well-structured documentation, and clean-looking code that still contains flawed assumptions. The problem isn’t always visible during a demonstration or code review. It may appear only when the system encounters real customers, incomplete data, unusual workflows, or production-scale demand.
This is why I believe the next phase of AI won’t be defined by generation alone. It will be defined by validation. Companies will need stronger testing, observability, security reviews, human oversight, and business-level performance measures. The question won’t be how much AI produced. It will be whether the result worked, remained secure, and delivered the intended business outcome.
Agents Are Moving Faster Than Governance
AI agents represent the next major shift. These systems don’t just answer questions. They can plan tasks, use tools, modify files, communicate with other systems, and take actions on behalf of an organization.
Governance hasn’t kept pace. Deloitte’s 2026 State of AI in the Enterprise report found that only one in five companies had a mature governance model for autonomous AI agents. Deloitte also found that just 34 percent of organizations were using AI to reimagine the business rather than applying it primarily to efficiency and productivity.
That’s where I believe we are in 2026. AI has moved from novelty to infrastructure, but management practices are still catching up. We’ve become better at giving machines work. We haven’t become equally good at defining accountability, measuring outcomes, and recognizing when those machines are wrong.
Where are we going? Toward more autonomous agents, smaller AI-enabled teams, faster software production, and new business models built around outcomes rather than labor. The companies that succeed won’t be the ones that adopt the most AI. They’ll be the ones that know what to delegate, what to measure, what to verify, and when a human still needs to make the final decision.
