New adoption data from Perplexity reveals how AI agents are driving workflow efficiency gains by taking over complex enterprise tasks.
For the past year, the technology sector has operated under the assumption that the next evolution of generative AI would advance beyond conversation into action. While Large Language Models (LLMs) serve as a reasoning engine, “agents” act as the hands, capable of executing complex, multi-step workflows with minimal supervision.
Until now, however, visibility into how these tools are actually being utilised in the wild has been opaque, relying largely on speculative frameworks or limited surveys.
New data released by Perplexity, analysing hundreds of millions of interactions with its Comet browser and assistant, provides a first large-scale field study of general-purpose AI agents. The data indicates that agentic AI is already being deployed by high-value knowledge workers to streamline productivity and research tasks.
Understanding who is using these tools is essential for forecasting internal demand and identifying potential shadow IT vectors. The study reveals marked heterogeneity in adoption. Users in nations with higher GDP per capita and educational attainment are far more likely to engage with agentic tools.
More telling for corporate planning is the occupational breakdown. Adoption is heavily concentrated in digital and knowledge-intensive sectors. The ‘Digital Technology’ cluster represents the largest share, accounting for 28 percent of adopters and 30 percent of queries. This is followed closely by academia, finance, marketing, and entrepreneurship.
Collectively, these clusters account for over 70 percent of total adopters. This suggests that the individuals most likely to leverage agentic workflows are the most expensive assets within an organisation: software engineers, financial analysts, and market strategists. These early adopters are not dabbling; the data shows that “power users” (those with earlier access) make nine times as many agentic queries as average users, indicating that once integrated into a workflow, the technology becomes indispensable.
AI agents: Partners for enterprise tasks, not butlers
To advance beyond marketing narratives, enterprises must understand the utility these agents provide. A common view suggests agents will primarily function as “digital concierges” for rote administrative chores. However, the data challenges this view: 57 percent of all agent activity focuses on cognitive work.
Perplexity’s researchers developed a “hierarchical agentic taxonomy” to classify user intent, revealing the usage of AI agents is practical rather than experimental. The dominant use case is ‘Productivity & Workflow,’ which accounts for 36 percent of all agentic queries. This is followed by ‘Learning & Research’ at 21 percent.
Specific anecdotes from the study illustrate how this translates to enterprise value. A procurement professional, for instance, used the assistant to scan customer case studies and identify relevant use cases before engaging with a vendor. Similarly, a finance worker delegated the tasks of filtering stock options and analysing investment information. In these scenarios, the agent handles the information gathering and initial synthesis autonomously to allow the human to focus on final judgment.
This distribution provides a definite indication to operational leaders: the immediate ROI for agentic AI lies in scaling human capability rather than simply automating low-level friction. The study defines these agents as systems that “cycle automatically between three iterative phases to achieve the end goal: thinking, acting, and observing.” This capability allows them to support “deep cognitive work,” acting as a thinking partner rather than a simple butler.
Stickiness and the cognitive migration
A key insight for IT leaders is the “stickiness” of AI agents for enterprise workflows. The data shows that in the short term, users exhibit strong within-topic persistence. If a user engages an agent for a productivity task, their subsequent queries are highly likely to remain in that domain.
However, the user journey often evolves. New users frequently “test the waters” with low-stakes queries, such as asking for movie recommendations or general trivia. Over time, a transition occurs. The study notes that while users may enter via various use cases, query shares tend to migrate toward cognitively oriented domains like productivity, learning, and career development.
Once a user employs an agent to debug code or summarise a financial report, they rarely revert to lower-value tasks. The ‘Productivity’ and ‘Workflow’ categories demonstrate the highest retention rates. This behaviour implies that early pilot programmes should anticipate a learning curve where usage matures from simple information retrieval to complex task delegation.
The “where” of agentic AI is just as important as the “what”. Perplexity’s study tracked the environments – specific websites and platforms – where these AI agents operate. The concentration of activity varies by task, but the top environments are staples of the modern enterprise stack.
Google Docs is a primary environment for document and spreadsheet editing, while LinkedIn dominates professional networking tasks. For ‘Learning & Research,’ the activity is split between course platforms like Coursera and research repositories.
For CISOs and compliance officers, this presents a new risk profile. AI agents are not just reading data; they are actively manipulating it within core enterprise applications. The study explicitly defines agentic queries as those involving “browser control” or actions on external applications via APIs. When an employee tasks an agent to “summarise these customer case studies,” the agent is interacting directly with proprietary data.
The concentration of environments also highlights the potential for platform-specific optimisations. For instance, the top five environments account for 96 percent of queries in professional networking, primarily on LinkedIn. This high concentration suggests that businesses could see immediate efficiency gains by developing specific governance policies or API connectors for these high-traffic platforms.
Business planning for agentic AI following Perplexity’s data
The diffusion of capable AI agents invites new lines of inquiry for business planning. The data from Perplexity confirms that we have passed the speculative phase. Agents are currently being used to plan and execute multi-step actions, modifying their environments rather than just exchanging information.
Operational leaders should consider three immediate actions:
- Audit the productivity and workflow friction points within high-value teams: The data shows this is where agents are naturally finding their foothold. If software engineers and financial analysts are already using these tools to edit documents or manage accounts, formalising these workflows could standardise efficiency gains.
- Prepare for the augmentation reality: The researchers note that while agents have autonomy, users often break tasks into smaller pieces, delegating only subtasks. This suggests that the immediate future of work is collaborative, requiring employees to be upskilled in how to effectively “manage” their AI counterparts.
- Address the infrastructure and security layer: With agents operating in “open-world web environments” and interacting with sites like GitHub and corporate email, the perimeter for data loss prevention expands. Policies must distinguish between a chatbot offering advice and an agent executing code or sending messages.
As the market for agentic AI is projected to grow from $8 billion in 2025 to $199 billion by 2034, the early evidence from Perplexity serves as a bellwether. The transition to enterprise workflows led by AI agents is underway, driven by the most digitally capable segments of the workforce. The challenge for the enterprise is to harness this momentum without losing control of the governance required to scale it safely.
See also: Accenture and Anthropic partner to boost enterprise AI integration
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