Artificial Intelligence
Consulting
Development ServicesAutonomy Sounds Efficient Until You Own the Outcome
As AI tools become more capable,autonomy feels like a natural next step. On paper, it promises cleaner workflows and faster execution.
McKinsey’s State of AI research shows that many organizations experimenting with generative AI are already exploring systems that operate with less direct human input across workflows. That shift has very concrete implications for developers.
In practice, autonomy changes the balance in a few key ways. Tasks move through the system with less manual coordination, and when failures happen, accountability snaps back to the human immediately.
Autonomy doesn’t eliminate ownership. It concentrates it into fewer, higher-stakes moments.
As AI tools spread across development workflows, fragmentation is inevitable. DevActivity’s analysis of the algorithmic shift in software engineering shows that new AI tools enter teams faster than standards can keep up. Adoption happens bottom-up, driven by individual productivity, not coordination. That pattern shows up quickly when developers solve the same problems in different ways or what counts as “done” quietly diverges, because of different expectations from the AI output.
Fragmentation itself is a natural phase of exploration.The risk appears when teams never stop to align on what actually changed.
At Intersog, we help technology leaders make deliberate decisions about AI adoption and engineering infrastructure. Whether teams are integrating AI into daily development work or preparing systems to scale responsibly, we focus on aligning technical ambition with clear ownership and real-world constraints.
If you write code every day, AI is no longer something you experiment with. It’s something you work alongside. You type less. You accept suggestions almost without noticing. You refactor more freely because trying ideas feels cheaper than before. At the same time, you reread more. You pause more often. You find yourself double-checking code that looks correct, even when it compiles.
What changed is not just how fast you write code, but where your effort goes. Less time is spent producing lines. More time is spent deciding whether those lines make sense, fit the system, and are worth owning.
Let’s take a closer look at how AI has changed the developer stack—and what that means for developers heading into 2026. Adoption Came First. Confidence Did Not.
AI didn’t enter development teams through a formal decision. It became part of the workflow because it helped. SonarSource’s State of Code Developer Survey puts numbers behind that experience. More than 75% of developers report using AI-assisted coding tools, and around 74% say they use ChatGPT or similar LLMs in development-related tasks. At this point, adoption is no longer the open question.
The same survey shows that 96% of developers still review or modify AI-generated code before trusting it. That detail explains a lot. Developers use AI because it saves time, not because they believe it’s always right.
In practice, AI speeds up the first draft, but the final decision still belongs to the developer. That balance between assistance and ownership is what’s quietly changing how development work feels.
Speed Went Up. Cognitive Load Didn’t Go Away.
From the outside, AI adoption is often described as a productivity win. And to some extent, that’s true. PwC’s Global AI Jobs Barometer shows that roles exposed to AI tend to see measurable productivity gains, along with faster shifts in the skills people are expected to develop. Work gets done faster, and the pace of change increases.
From a developer’s perspective, the experience feels more uneven. You save time on repetitive tasks, but you spend more time thinking through results. You move faster through familiar problems, but slow down when decisions matter. The time didn’t disappear. It changed shape.
This explains why many developers feel more productive, but also more mentally tired. Less effort goes into writing code, but more energy goes into evaluating it.
Would you like to learn more
about talent solutions
The AI Stack You’re Actually Using Most developers don’t use the term an “AI stack.” They act in moments: they reach for AI when something feels slow, unclear, or mentally expensive. When the code is familiar but tedious, when it’s unfamiliar and they need context. When it works, but they want a second opinion before moving on.
That’s why the AI stack doesn’t feel like a clean set of tools. It feels more like a collection of habits that show up at different points in the day.
Looking at how today’s coding assistants are positioned and compared, a pattern becomes clear. Developers don’t replace one tool with another, instead they layer them. Different tools show up depending on whether they are writing, refactoring, understanding, validating, or reasoning.
Digitalapplied showed us how the AI coding revolution looks like. Today, autonomous coding agents that can build entire features, fix bugs, and even review code like senior developers. So a human developer has to decide which one will give them the greatest competitive advantage.
| Tool | When developers reach for it | What value it adds | Where friction shows up |
| AI-assisted code quality tools | Before merging or releasing | Extra safety layer for AI-assisted code and reviews | Can create false confidence if treated as a replacement for human review |
| Claude | Reasoning through complex logic outside the IDE | Strong long-form reasoning and clearer explanations | Context switching and no direct IDE integration |
| Cursor | Refactoring or exploring alternatives | Intent-driven iteration and faster experimentation | Harder to reason about final authorship and ownership |
| GitHub Copilot | Writing routine or repetitive code inside the IDE | Faster drafts, less friction in familiar patterns | Subtle logic errors and over-trusting suggestions |
| Windsurf | Navigating large or unfamiliar codebases | Better context awareness across files | Still requires building a solid mental model |
Why Measuring Productivity Now Feels Incomplete
Once AI becomes part of daily development work, productivity starts to feel harder to explain.
Research published on arXiv around human–AI collaboration in software engineering shows that AI-assisted workflows tend to speed up certain tasks(ex.code completion or initial refactoring), while leaving others, like validation, review, and architectural reasoning, largely unchanged. Time is saved, but not uniformly.
The problem appears when teams try to measure that change. Most productivity metrics assume a direct relationship between effort and output. In AI-assisted development, output is co-produced. A task may be completed faster, but the source of that speed becomes difficult to isolate. Was it developer experience, model suggestions, or the interaction between both?
This is why many teams report feeling more productive without being able to prove it cleanly. Velocity improves, but attribution breaks down.
The issue isn’t that productivity gains are imaginary. It’s that the way work is produced changed faster than the way we learned to measure it.
Autonomy Sounds Efficient Until You Own the Outcome
As AI tools become more capable,autonomy feels like a natural next step. On paper, it promises cleaner workflows and faster execution.
McKinsey’s State of AI research shows that many organizations experimenting with generative AI are already exploring systems that operate with less direct human input across workflows. That shift has very concrete implications for developers.
In practice, autonomy changes the balance in a few key ways. Tasks move through the system with less manual coordination, and when failures happen, accountability snaps back to the human immediately.
Autonomy doesn’t eliminate ownership. It concentrates it into fewer, higher-stakes moments.
As AI tools spread across development workflows, fragmentation is inevitable. DevActivity’s analysis of the algorithmic shift in software engineering shows that new AI tools enter teams faster than standards can keep up. Adoption happens bottom-up, driven by individual productivity, not coordination.
That pattern shows up quickly when developers solve the same problems in different ways or what counts as “done” quietly diverges, because of different expectations from the AI output.
Would you like to learn more
about talent solutions
Fragmentation itself isa natural phase of exploration.The risk appears when teams never stop to align on what actually changed.
At Intersog, we help technology leaders make deliberate decisions about AI adoption and engineering infrastructure. Whether teams are integrating AI into daily development work or preparing systems to scale responsibly, we focus on aligning technical ambition with clear ownership and real-world constraints.
