New survey suggests humans aren’t ready to support autonomous AI agents

The increasing adoption of autonomous AI agents by enterprises is creating both opportunities and infrastructure challenges as companies race to implement this transformative technology.

Investment momentum: Organizations are demonstrating substantial financial commitment to AI agent development and deployment, with market indicators showing aggressive adoption plans.

• More than 50% of enterprises are allocating annual budgets of $500,000 or higher for AI agent initiatives
• 42% of surveyed technology professionals anticipate building or prototyping over 100 AI agents within the next year
• 36% of respondents plan to move more than 100 AI agents into production environments

Implementation timeline and scope: Companies are setting ambitious targets for AI agent integration into their core business operations over the next two years.

• By the end of 2025, a quarter of organizations expect AI agents to manage most of their core business processes
• 41% of businesses project that AI agents will handle between 26-50% of their fundamental operations
• The rapid timeline suggests a dramatic shift in how businesses plan to automate and augment their workflows

Technical barriers and requirements: Despite strong interest, significant infrastructure gaps must be addressed before widespread AI agent deployment becomes feasible.

• 86% of professionals indicate their current technology stack requires upgrading to support AI agents
• Access to multiple data sources is crucial, with 42% of respondents requiring integration with 8 or more distinct data sources
• The fragmentation of SaaS applications creates additional complexity for seamless agent integration

Infrastructure prerequisites: Success with AI agents demands substantial improvements in several key technical areas.

• Companies need to develop robust API ecosystems to enable agent interactions across systems
• Enhanced security and control frameworks are essential for managing autonomous AI operations
• Improved data management capabilities are required to handle increased data processing demands
• Storage infrastructure must evolve to accommodate growing model and training data requirements

Looking ahead: While enterprise enthusiasm for AI agents is high, the path to successful implementation remains complex.

• Specialized AI models trained for specific tasks are likely to emerge as the technology matures
• Organizations must balance their ambitious deployment goals with the reality of their current technical readiness
• The gap between interest and infrastructure suggests a potentially challenging transition period as companies work to build the necessary technical foundation