OutSystems: AI is already in production for IT teams, but the business lacks unified governance

AI is no longer an experiment within corporate development. According to OutSystems, many companies have moved AI tools into early production phases, but the pace of adoption is beginning to outpace management processes.

AI has taken root in IT

The State of AI Development 2026 study is based on responses from 1,879 IT leaders. The main conclusion sounds unsurprising, but now as a working fact: for a significant portion of companies, artificial intelligence no longer lives in a sandbox. It has reached early production, and this is happening primarily within the IT function.

This is where it's easiest to connect new tools to existing processes, see results faster, and more clearly measure the effect in terms of money, speed, and team workload. This is an important shift. Until recently, most corporate AI projects looked like a series of pilots with unclear outcomes: a small experiment, a presentation to management, and a long pause before the next attempt.

Now the picture is changing. Developers, architects, and IT managers increasingly use AI not as a demonstration of capabilities, but as a working layer in their daily activities. For business, this signals that AI in the corporate environment is transitioning from a discussion phase to an operational implementation phase.

Where results are visible

Logically, development was the first testing ground. In IT, it's easier to standardize tasks, verify hypotheses faster, and integrate AI into existing processes more readily. If a team releases code, tests releases, and maintains internal products, they almost immediately see where automation actually saves hours and where it remains a beautiful idea. The following scenarios scale fastest:

• drafting code and accelerating typical development tasks;
• assistance with testing, finding bugs, and reviewing changes;
• preparing documentation, descriptions, and internal technical artifacts;
• supporting planning, task coordination, and related processes around engineering teams.

The point is not that AI completely replaces developers. Rather, it covers repetitive and time-consuming areas where employees previously spent hours on routine work. This is why success in software development looks logical: it's easier to measure productivity here, compare results before and after implementation, and make scaling decisions based on team metrics rather than gut feeling.

Why a control center is needed

Against this background, OutSystems warns of another problem: AI implementation risks beginning to outpace centralized management. Typically, it looks like this: one team buys one tool, another connects a different one, a third builds its own set of prompts and integrations, and there are almost no common rules. As a result, the company gets not a unified AI strategy, but a set of parallel initiatives that don't work well together.

The risk here is not just chaos. When AI grows without a unified management framework, it becomes harder for the business to control security, costs, access, result quality, and implementation priorities. License duplication appears, standards diverge, and successful cases are difficult to transfer from one team to another.

Centralized management of projects and AI tools is needed not to slow down experiments, but to help best practices spread quickly and weak solutions be eliminated just as quickly. For large companies, this is no longer a matter of fashion, but a matter of operational discipline. If AI truly becomes part of production processes, it needs the same level of coordination that has long existed for development, infrastructure, and information security.

Otherwise, the organization scales not efficiency, but fragmentation.

What this means

The OutSystems survey shows a fairly mature market picture: corporate AI thrives best where there are clear processes and measurable results, that is, in IT and development. The next stage is not simply to expand AI use, but to establish unified management so that the growth in the number of tools doesn't turn into growth in disorder.