Survey: 75% of IT Teams Face Workflow Challenges, AI Not a Panacea

Despite the omnipresent promise of artificial intelligence to accelerate development cycles, a new survey reveals that the path to production remains fraught with challenges for most IT teams. A recent report from Temporal, a microservice orchestration platform, found that a mere one in four participants reported experiencing genuinely smooth workflows within their organizations. For the vast majority – 75% of respondents – the reality is a constant battle against system complexities and operational crises, akin to “fighting fires” on the digital frontlines, as the report grimly notes.

The survey highlighted several pervasive pain points hindering efficiency. A significant 35% of participants cited overly complex workflow processes as a major impediment. Similarly, 35% reported that their teams grapple with high operational overhead, consuming valuable resources and time. Furthermore, nearly a third (34%) of organizations confessed to struggling significantly with recovery from system failures, indicating a lack of resilience in their current setups.

Interestingly, a clear divergence emerged in how different roles within IT perceive these challenges. Decision-makers, such as engineering managers and CTOs, were more likely to pinpoint operational overhead and difficulties with scaling systems as their primary concerns. In contrast, individual contributors – the software engineers and developers on the ground – more frequently cited issues with managing long-running processes and the inherent complexity of recovering from system failures.

These insights stem from Temporal’s 2025 “State of Development” report, which gathered responses from over 220 individual contributors and IT decision-makers globally. The participant base was geographically diverse, with 42% from North America, 27% from Europe, and 15% from the Asia-Pacific region. Nearly half (46%) of the respondents work for organizations employing more than 500 people, providing a broad view across enterprise sizes.

The survey also shed light on differing approaches to tooling selection and priorities. In smaller and mid-sized organizations (those with fewer than 1,000 employees), individual developers typically hold more sway over which tools are adopted. However, in larger enterprises with 1,000 or more employees, CIOs and engineering managers predominantly dictate tooling solutions for their development teams. This hierarchical difference in decision-making extends to the priorities driving tool selection. Management tends to prioritize security and reliability above all else, while individual contributors are more inclined to seek out open-source tools, suggesting a disconnect in strategic vision versus practical implementation.

While the adoption of AI tools, such as coding assistants, is widespread – with 94% of professionals reporting their use in workflows – the organizational integration of AI at scale lags significantly. Only 39% of organizations indicated they are actively building frameworks to leverage AI comprehensively across their operations.

Despite these internal disparities, a few shared priorities emerged. Roughly the same proportion of decision-makers and individual contributors agreed that enhancing system reliability and ensuring security compliance were their organization’s main objectives for new solutions and tools over the next one to two years. However, beyond this common ground, a substantial chasm appeared in other reported priorities. Decision-makers were notably more focused on increasing automation (38% versus 27% of individual contributors), reducing operational costs and technical debt (37% versus 20%), and enhancing developer productivity (34% versus 18%). Conversely, individual contributors expressed greater concern than their managerial counterparts about increasing data processing capabilities (30% versus 19%) and migrating to hybrid and cloud environments (30% versus 11%).

The findings paint a picture of an IT landscape grappling with fundamental workflow inefficiencies and a significant communication gap between different organizational tiers. Even as cutting-edge technologies like AI become commonplace, the core challenges of complexity, operational burden, and system resilience continue to plague most teams, highlighting a persistent need for better alignment and more robust foundational processes.