.AI Agents Are Revolutionizing Complex Enterprise Tasks

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 serve as reasoning engines, “agents” act as the hands, capable of executing complex, multi‑step workflows with minimal supervision.

Until now, visibility into how these tools are actually being used in the wild has been opaque, relying largely on speculative frameworks or limited surveys.

New data released by Perplexity, analyzing hundreds of millions of interactions with its Comet browser and assistant, provides the first large‑scale field study of general‑purpose AI agents. The findings indicate 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 higher 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. It is followed closely by academia, finance, marketing and entrepreneurship. Together these clusters account for over 70 percent of total adopters, suggesting that the individuals most likely to leverage agentic workflows are the most expensive assets within an organization: software engineers, financial analysts and market strategists. Power users—those with early 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 move beyond marketing narratives, enterprises must understand the utility these agents provide. A common view posits agents as “digital concierges” for rote administrative chores. The data challenges this notion: 57 percent of all agent activity focuses on cognitive work.

Perplexity’s researchers developed a hierarchical agentic taxonomy to classify user intent, revealing that usage is practical rather than experimental. The dominant use case is “Productivity & Workflow,” which accounts for 36 percent of all agentic queries, followed by “Learning & Research” at 21 percent.

Specific anecdotes illustrate how this translates to enterprise value. A procurement professional used the assistant to scan customer case studies and surface relevant use cases before engaging a vendor. A finance analyst delegated the filtering of stock options and analysis of investment information. In these scenarios the agent handles information gathering and initial synthesis autonomously, freeing the human to focus on final judgment.

This distribution sends a clear signal to operational leaders: the immediate ROI for agentic AI lies in scaling human capability rather than merely automating low‑level friction. The study defines these agents as systems that cycle automatically between three iterative phases—thinking, acting and observing—to achieve an end goal. This capability positions them as “thinking partners” rather than simple butlers.

Stickiness and the cognitive migration

A key insight for IT leaders is the “stickiness” of AI agents within enterprise workflows. In the short term, users exhibit strong within‑topic persistence: if a user engages an agent for a productivity task, 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—movie recommendations or general trivia. Over time, a transition occurs. While users may enter via various use cases, query shares tend to migrate toward cognitively oriented domains such as productivity, learning and career development.

Once an agent is employed to debug code or summarize a financial report, users rarely revert to lower‑value tasks. The “Productivity” and “Workflow” categories demonstrate the highest retention rates, implying that early pilot programs should anticipate a learning curve where usage matures from simple information retrieval to complex task delegation.

The “where” of agentic AI is as important as the “what.” Perplexity tracked the environments—specific websites and platforms—where these agents operate. Activity concentrates in staple enterprise applications. Google Docs emerges as a primary environment for document and spreadsheet editing, while LinkedIn dominates professional networking tasks. For learning and research, activity is split between online course platforms and research repositories.

For security officers, this presents a new risk profile. Agents are not merely 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 asks an agent to “summarize these customer case studies,” the agent interacts directly with proprietary data.

The concentration of activity also highlights opportunities for platform‑specific optimization. The top five environments account for 96 percent of queries in professional networking, primarily on LinkedIn. This suggests businesses could achieve immediate efficiency gains by developing tailored 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 Perplexity data confirms we have moved beyond speculation; agents are now planning and executing multi‑step actions, modifying their environments rather than simply exchanging information.

Operational leaders should consider three immediate actions:

1. Audit productivity and workflow friction points within high‑value teams. The data shows this is where agents naturally find footholds. If software engineers and financial analysts are already using these tools to edit documents or manage accounts, formalizing these workflows could standardize efficiency gains.
2. Prepare for an augmentation reality. Researchers note that while agents have autonomy, users often break tasks into smaller pieces, delegating only subtasks. This suggests the immediate future of work is collaborative, requiring employees to be upskilled in how to effectively manage their AI counterparts.
3. Address infrastructure and security layers. With agents operating in open‑world web environments and interacting with sites such as GitHub and corporate email, the perimeter for data‑loss‑prevention expands. Policies must differentiate between a chatbot offering advice and an agent executing code or sending messages.

Analyst forecasts project the market for agentic AI to expand from roughly $8 billion in 2025 to $199 billion by 2034. 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 enterprises is to harness this momentum while maintaining the governance needed to scale it safely.