UiPath Reports How Agentic AI is Driving Business Investment

As the evolution of AI in enterprises has moved beyond basic automation and chatbots towards systems capable of autonomous decision-making, a shift is required for the transition from tools that need continuous human oversight to those that can navigate complex business processes independently.
The market for enterprise AI has traditionally focused on specific task automation and data analysis up until now. However, the emergence of agentic AI represents a step change in capability.
The financial implications of this transition are substantial, with technology leaders evaluating both the potential returns and risks of deploying more autonomous systems within critical business operations.
Therefore, questions of governance, security and integration with existing infrastructure are shaping investment decisions, leading enterprise technology leaders to prepare to increase investment in agentic AI according to UiPath.
UiPath research highlights integration challenges
UiPath identifies integration between systems as a priority, with 87% of respondents stating that interoperability between AI technologies is essential for their organisations whilst current limitations centre on connecting AI tools with existing business applications.
Daniel Dines, Chief Executive Officer and Founder of UiPath, says: "Agentic AI is a transformative approach that greatly expands and enhances the ability to automate larger, more complex business processes.
“The most powerful use cases for agents will be those that can orchestrate across business systems."
- 93% are extremely or very interested in agentic AI
- 32% note they are planning to invest in the next six months or less
- 37% of IT executives claim they are already using agentic AI
Organisations are reporting challenges with their current automation and AI deployments, citing security concerns, development complexity, integration issues and data quality as primary obstacles to implementation.
Security and automation concerns shape deployment
According to the survey, IT security emerges as the primary concern for 56% of respondents, followed by implementation costs at 37% and integration challenges at 35%. The findings suggest executives seek autonomous capabilities but require governance frameworks and security measures.
Max Ioffe, Director of the Global Intelligent Automation Center of Excellence at Wesco Distribution, emphasises the role of existing automation technology: "I expect that robotic process automation will orchestrate the agents.
"For larger scale processes, you need clear orchestration and governance and that means a deterministic technology like RPA."
The research also indicates that 58% of respondents anticipate improvements in business workflow oversight through agentic AI deployment, while 53% expect enhanced application integration.
A further 52% identify potential for automating complex business workflows.
Enterprise adoption requires governance framework
Companies face challenges in implementing AI transformation across complex workflows and multiple systems and UiPath highlights concerns about productivity gains and trust in AI for critical enterprise processes.
The survey reveals that safety and privacy rank as the most critical capabilities for effective implementation of agentic AI workflows, followed by seamless integration with existing systems and this prioritisation reflects growing awareness of compliance requirements and data protection obligations.
Daniel adds: "For agentic AI to have meaningful impact, organisations need to provide agents with the needed foundation to intelligently plan and synchronize actions across robots, agents, people and systems, all within enterprise-grade governance and security.”
The implementation of safety measures and system integration capabilities rank as priority requirements for successful agentic AI deployment and the research suggests acceptance of autonomous operation provided governance and security frameworks exist.
UiPath also defines AI agents as software entities that employ AI techniques to perceive their environment, process information and execute actions to achieve specific objectives.
These agents operate either autonomously or semi-autonomously, replicating human decision-making processes within defined parameters.
Daniel says: "As AI systems become more autonomous, enterprises must strike a balance between autonomy and human oversight to prevent unintended consequences and guarantee that AI-driven actions align with ethical, compliance and legal standards."
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