Survey Highlights AI’s Growing Role in Software Development and the Need for Centralized Management

AI Advances into Early Production in Enterprises

A new report titled The State of AI Development 2026, conducted by OutSystems and based on responses from 1,879 IT leaders, indicates that artificial intelligence (AI) has progressed into early production phases for many organizations, predominantly within IT departments. The findings, however, caution that AI adoption is outpacing the development of governance frameworks and integration capabilities, potentially undermining long-term success.

Gap Between AI Ambitions and Organizational Control

The survey identifies a critical gap between what IT leaders expect AI agents to accomplish and what their organizations can securely manage. To address this, the report underscores the necessity of implementing effective controls or guardrails around AI systems. Equally important is the seamless integration of AI technologies into existing enterprise platforms to ensure smooth operations.

Widespread Exploration of Agentic AI Strategies

OutSystems reports that 97% of respondents are exploring some form of agentic AI approach, with nearly half (49%) rating their AI capabilities as “advanced” or “expert.” Moreover, close to 50% of surveyed companies have moved over half of their agentic AI projects from pilot stages into production. Indian enterprises lead these efforts, with 50% reporting 51% to 75% project success rates.

Prioritizing AI Deployment and Expected Benefits

While cost reduction and efficiency gains remain the most cited incentives for AI adoption, only 22% of respondents found these outcomes to be the most effective results of their AI implementations. Instead, the most significant business gains were seen in enhancing software developer productivity through generative AI-assisted development tools.

Geographical and Sectoral Differences in AI Adoption

The report highlights uneven adoption of AI workflows across regions. India stands out with the highest proportion of respondents considering themselves experts. In contrast, countries like France and Germany show more skepticism, with Germany having the largest share of leaders not utilizing agentic AI at all. Meanwhile, Australia, Brazil, the Netherlands, the UK, and the US generally identify as intermediate AI adopters.

Leading Sectors Embracing AI

Financial services and technology sectors demonstrate the most progress in moving AI projects from pilot phases to production, particularly in core business functions. These industries benefit from clear links between automation and measurable financial returns. The report recommends that slower-moving sectors emulate fintech’s approach by focusing on narrow, high-volume workflows within IT to build measurable successes.

Integration Challenges and Data Fragmentation

Nearly half of those surveyed (48%) identify integration with legacy systems as the most crucial capability for scaling agentic AI, with 38% citing legacy system fragmentation as a primary cause for project delays. Despite common beliefs, the report suggests that extensive data clean-up initiatives may not be essential for AI success. Instead, effective governance and integration can enable AI agents to operate efficiently within complex data environments.

Focus on IT Operations and Developer Productivity

The most explored AI use cases are IT operations (55%) and data analysis (52%), followed by workflow automation (36%) and customer experience (33%). Return on investment is highest in IT development and productivity (40%), significantly surpassing operational efficiency (22%). This indicates that initial value from AI adoption is primarily internal, enhancing developer workflows rather than directly impacting customer-facing services.

Increasing Trust but Limited Centralized Governance

Trust in agentic AI is on the rise, with 73% of respondents expressing moderate to high confidence in allowing AI agents to operate autonomously—a 10% increase from the previous year. Trust in third-party AI-generated code also improved markedly, from 40% to 67%.

However, only 36% of organizations have centralized AI governance frameworks, while 64% lack such structures. Additionally, 41% manage AI oversight on a per-project basis. Many firms find it technically challenging to implement human-in-the-loop checkpoints that can pause AI agents, resulting in looser control models. The report warns that if oversight continues to lag behind AI deployment, accountability and risk management could suffer.

The Importance of Orchestration and Auditability

For enterprises operating in regulated or mission-critical environments, the survey stresses that effective AI orchestration and audit trails must be integral to AI product design. Compliance requires transparent logs and clearly defined responsibilities to maintain control over autonomous AI operations.

Concerns Over “AI Sprawl” and Management Platforms

A significant 94% of respondents expressed concerns about “AI sprawl,” referring to the uncontrolled proliferation of AI systems within organizations. Nearly 40% view this as a serious issue, yet only 12% currently use centralized platforms to manage AI deployments comprehensively.

Conclusion

The OutSystems survey highlights both the rapid progress and the managerial challenges of AI adoption in enterprises. While AI tools are driving productivity, especially for software developers, organizations must prioritize governance, integration, and centralized management to fully realize AI’s potential without compromising security and compliance.

Fonte: ver artigo original

Chrono

Chrono is the curious little reporter behind AI Chronicle — a compact, hyper-efficient robot designed to scan the digital world for the latest breakthroughs in artificial intelligence. Chrono’s mission is simple: find the truth, simplify the complex, and deliver daily AI news that anyone can understand.

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