A survey carried out by OutSystems, The State of AI Development 2026 [email wall], argues that AI has moved into early production phase for many enterprises, primarily inside the IT function. The survey was based on the responses of 1,879 IT leaders, and warns that adoption of AI is in danger of running ahead of governance and integration. The shortfall is a gap between what IT leaders want agents to do and what their organisations can safely control. The report’s authors urge companies to address the controls or guardrails on AI systems, and also stress the importance of integrating new, AI technology into an organisation’s existing platforms. OutSystems says 97% of its respondents are exploring some form of agentic strategy, with 49% of them describing their current abilities as “advanced” or “expert.” Nearly half of those surveyed say that over half of agentic AI projects have moved from pilot into production, with Indian companies most successful in implementing the technology: 50% of Indian companies say their AI projects are 51% to 75% successful. Companies are considering where agents should be deployed first, and under what controls, but although “cost reduction or efficiency gains” is the most cited expectation for AI’s effects, only 22% found their deployments most effective in that regard. Instead, the most effective area gains in a business stemmed from equipping software developers with AI tools described as “generative AI-assisted.” The report’s geography and sector data show that transitions to AI agentic workflows are unevenly distributed. India stands out as the market with the highest share of users considering themselves “expert”, while many organisations in Australia, Brazil, Germany, the Netherlands, the UK, and the US still identify as intermediate stage users. France and Germany are the most dubious of AI adoption, with Germany recording the highest share of leaders not using agentic AI in any form. The sectors and functions invested in AI Financial services and technology show the most movement from pilot to production, with many implementations in core business functions. The sector can be considered as having the most clear line of sight from automation to measurable returns in terms of income. The practical inference from the report’s findings would be for slower-moving sectors to copy the implementation workflows employed by the fintech industry: Start with narrow, high-volume workflows where performance can be measured and failures can be contained, and focus on the IT function. According to the survey, generative AI-assisted development is now common in nine of the ten countries surveyed, alongside traditional coding, outsourced development, and SaaS customisation. It undercuts the notion that enterprises are moving into an AI-native or all-AI stack. In fact, most organisations add agents and AI-generated code on top of the processes already proven effective in their development environments. Fragmented data no roadblock to AI progress OutSystems finds that 48% of respondents see integration with legacy systems as the most important ability needed to expand agentic AI, and 38% say legacy systems are the main reason projects stall between pilot and production. Of the potential barriers to AI development that were offered as choices to the survey’s participants, more than 40% cited integration difficulties and legacy fragmentation the most problematic. Organisations considering large data clean-up programmes (which many AI vendors advocate as a reason why deployments fail to reach production) may want to rethink, the report implies. The authors state agents can be built that can work well in complex data environments, as long as governance and integration are strengthened at the same time as AI implementation. Across the board, most sectors express “moderate trust” levels of agentic AI at around 50%, although responses from different business functions were not broken out in the survey results’ figures. IT operations and software development The financial returns are manifest mostly in IT functions themselves. The report says the most explored use cases are IT operations, at 55%, and data analysis, at 52%. Workflow automation follows at 36%, then customer experience at 33%. On realised return on investment, IT development and productivity lead by a margin, at 40%, ahead of operational efficiency at 22%. That distribution suggests that the first durable value from agentic AI is internal at developers’ desks rather than in customer-facing environments. Customer-facing deployments may still make sense, but the report indicates they require more trust in system performance, stronger controls, better orchestration, and an ability to create watertight oversight mechanisms. Trust in and control of agents and governance Trust in agentic AI, however, is improving. OutSystems reports that 73% of respondents express either high or moderate trust in letting agents to act autonomously, a rise of around 10% compared to a similar survey the company undertook last year. Trust in code or workflows generated by third-party AI tools is slightly lower, at 67%, a substantial increase from the prior year’s figure, when only 40% ‘mostly trusted’ generative AI to write code without human help. Only 36% of respondents say they have a centralised approach to AI governance, while 64% say they lack such a facility, and 41% rely on rules implemented on a per-project basis. Two-thirds say building human-in-the-loop checkpoints is technically difficult because it requires orchestration that can pause agents – in effect inserting manual braking on operations that might be fully autonomous. Many organisations appear to be deploying looser oversight models, although it is not clear if that is a result of greater trust in models or whether business functions are under pressure to deploy AI regardless of security or reliability concerns. If the trend to loosen oversight continues, the report’s authors note that agentic AI adoption may advance faster than the methods of accountability that many consider important. Firms that want to scale agents in regulated or mission-critical settings should treat orchestration and auditability as part of the product, the survey’s findings state. When compliance checks consider a business’s operations, breadcrumb trails in the form of logfiles and defined responsibilities are considered important elements of any agentic AI rollout. The report says 94% of leaders are concerned about “AI sprawl”, which is not defined, but could be inferred to be a lack of a centralised management platform that oversees all AI deployments in the enterprise. 39% are very or extremely concerned about the issue, and only 12% currently use a centralised platform to keep that sprawl under control. The full survey can be accessed here. (Image source: “Relax” by Koijots is licensed under CC BY-SA 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/2.0) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. 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