AI Moves Into Production, But Governance Gaps Raise Enterprise Risks – OutSystems Report
AI Moves Into Production, But Governance Gaps Raise Enterprise Risks – OutSystems Report
Artificial intelligence is rapidly moving from experimentation to real-world deployment across enterprises, but new research suggests governance and integration challenges are struggling to keep pace.
According to a 2026 survey by OutSystems, based on responses from 1,879 IT leaders, AI adoption has entered an early production phase—particularly within IT departments. However, the report warns that many organizations are scaling AI faster than they can properly manage it.
Nearly all respondents (97%) are exploring agentic AI strategies, with almost half describing their capabilities as advanced. In addition, close to 50% say that more than half of their AI projects have already transitioned from pilot to production. India leads in implementation success, with many companies reporting strong results.
Despite high expectations around cost reduction and efficiency, only 22% of organizations say those benefits have materialized. Instead, the most immediate gains are being seen in software development, where generative AI tools are boosting developer productivity.
Integration and governance remain key barriers
The report highlights a growing gap between AI ambition and operational control. Almost half of respondents (48%) say integrating AI with legacy systems is the biggest requirement for scaling, while 38% identify outdated infrastructure as the main reason projects stall.
At the same time, governance frameworks are still underdeveloped. Only 36% of organizations report having centralized AI governance, while many rely on project-level rules. Concerns about “AI sprawl”—uncontrolled and fragmented AI deployments—are widespread, with 94% of leaders expressing worry.
IT leads adoption, while other sectors lag
AI deployment is most advanced in financial services and technology sectors, where automation has clearer links to measurable returns. IT operations (55%) and data analysis (52%) are the most common use cases, followed by workflow automation and customer experience.
The findings suggest that early returns from AI are largely internal, improving developer workflows and operational efficiency rather than transforming customer-facing services.
Trust is rising—but oversight is still evolving
Trust in AI systems is improving, with 73% of respondents expressing moderate to high confidence in autonomous agents. However, building proper oversight remains a challenge. Two-thirds of IT leaders say implementing human-in-the-loop controls is technically difficult, especially in complex, automated workflows.
The report concludes that for AI to scale safely—especially in regulated industries—organizations must treat governance, orchestration, and auditability as core components of their AI strategy, not afterthoughts.
Without stronger controls, the rapid expansion of agentic AI could outpace the systems designed to keep it accountable.
