Stop wasting AI on personal productivity: 60% of leaders pivot to agentic automation for real enterprise value

Automation Anywhere
, the leader in
Agentic Process Automation
(APA), today released a new proprietary research
report
developed in collaboration with Forrester Consulting, revealing key barriers and breakthroughs shaping enterprise adoption of
AI agents
. The findings highlight the increasing momentum of AI agents across industries, as well as the implementation challenges organizations must address to realize their full potential.
The study, based on a survey of global decision-makers overseeing enterprise-wide AI strategies, found that 60% of respondents believe automation platforms—especially those from RPA leaders like Automation Anywhere—are the most valuable foundation for managing
AI-driven processes
. This preference outpaces general-purpose AI providers such as OpenAI (ChatGPT) and Anthropic (Claude), as well as broader enterprise platforms like Microsoft Power Automate and Salesforce Einstein, highlighting the need for automation-native solutions purpose-built for process orchestration and scale. Additionally, 71% of respondents agreed that automation solutions should augment human capabilities rather than replace them—reinforcing the importance of keeping strategic decision-making in human hands.
'This research highlights a critical inflection point for enterprises,' said Mihir Shukla, CEO of Automation Anywhere. 'Leaders are clearly prioritizing AI-augmented workflows, recognizing the undeniable value of Agentic AI. The fact that a significant majority are specifically seeking these solutions from traditional RPA and task automation vendors underscores that deep process automation expertise is critical to scale adoption and unlock meaningful impact, accelerating the journey to the autonomous enterprise and paving the path to artificial general intelligence for work.'
Key Insights from the Study:High interest meets practical hurdles
With deep roots in automation and RPA, Automation Anywhere's Agentic Process Automation (APA) is purpose-built to overcome the key hurdles slowing AI agent adoption. While 74% of respondents recognize the promise of AI agents to surface insights from vast data sets, concerns around data privacy (66%), skillset gaps (63%), and integration complexity (61%) persist. APA is designed to balance autonomous execution with enterprise-grade governance and human oversight—making it possible to scale safely and effectively.
Transformational opportunities across business functions
Organizations are already piloting or implementing AI agents for internal employee support (53%) and customer service (48%). Many plan to extend these capabilities to broader business functions, to
enterprise automation
and organizational stewardship in the next two years. The potential value of AI agents for areas such as customer service, sales automation, and compliance received transformational value ratings exceeding eight out of ten on average.
Businesses demand enterprise-grade AI automation platforms
When evaluating platforms for building and deploying AI agents, 60% of respondents found intelligent automation platforms from RPA (Robotic Process Automation) and task automation vendors to be highly valuable for long-running processes. Organizations strongly prefer solutions capable of enterprise-grade integration, end-to-end process orchestration, and mature data security.
Early adoption & transformational value
Nearly 75% of leaders plan to pilot AI agents for customer support within the next year, with 71% eyeing research applications. Across all potential use cases, respondents expect transformational levels of value, underscoring strong confidence in AI agents' impact.
Navigating the road ahead
While challenges remain, enterprise leaders are clear-eyed and confident about the transformational potential of AI agents. By proactively addressing hurdles around security, cost, and talent, organizations can move beyond experimentation and begin scaling Agentic AI to drive measurable business outcomes.

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