For the past year, the world has been obsessed with chatbots. We’ve seen them write poems, generate images, and answer trivia questions. It’s been entertaining, but let’s be frank, it often feels like a clever parlour trick. The real revolution, the one that’s genuinely starting to reshape how businesses operate, is happening far more quietly. It’s happening inside company workflows, and it’s being driven by a far more sophisticated form of intelligence: agentic AI. This isn’t about asking an AI for a fun fact; it’s about giving it a job to do, and trusting it to get it done.
Fresh data from the AI search company Perplexity is giving us the first real look under the bonnet at this shift. By analysing how people use its AI-powered browser and assistant, it’s clear that we are moving from simple queries to complex, multi-step tasks. This signals a move towards genuine workflow automation and what can only be described as cognitive delegation.
Understanding This New Breed of AI
So, what exactly makes an AI ‘agentic’? It’s a question worth asking because the term gets thrown around quite a bit.
Not Your Grandfather’s AI
Think of it this way: a traditional AI, like the chatbots we’ve grown accustomed to, is like a super-powered calculator. You give it a specific instruction—”add these numbers,” “write a sentence about cats”—and it executes that single task. It’s powerful, but it’s passive. It waits for your command.
An agentic AI is more like a junior associate or a research assistant. You don’t give it a command; you give it a goal. You might say, “Analyse our top five competitors’ latest financial reports and create a summary of their R&D spending.” The agent then autonomously breaks that goal down into smaller tasks:
– It finds the financial reports.
– It reads and extracts the relevant data.
– It synthesises the information.
– It presents you with a finished summary.
This ability to plan, execute, and adapt across multiple steps without constant human intervention is the defining characteristic. It’s a move from instruction-following to problem-solving.
The Quiet Takeover in the Enterprise
This shift is not just theoretical. According to an analysis published in Artificial Intelligence News, based on Perplexity’s data, the adoption of these tools is accelerating in high-value sectors. The digital technology industry leads the pack, accounting for 28% of adopters and 30% of all queries. Finance and academia are not far behind. This isn’t a coincidence. These are fields where knowledge workers are drowning in data and where sifting through information to find a critical insight is the core of the job.
Redefining ‘Work’ with Intelligent Automation
The real story here is about how this changes the very nature of professional work. We are seeing a practical application of AI that goes beyond simple efficiency gains and starts to augment human intellect.
Automation That Thinks
Workflow automation isn’t a new concept. For years, businesses have used software to automate repetitive, rule-based tasks like sending invoices or updating databases. But agentic AI takes it to a new level.
The Perplexity data shows that the top use cases are productivity and workflow (36% of queries) and learning and research (21%). This tells us that users aren’t just offloading mindless clicks. A remarkable 57% of agent activity is focused on cognitive work, according to the report. They are asking agents to synthesise information from multiple sources, conduct research within platforms like Google Docs, or gather intelligence from professional networks like LinkedIn.
Welcome to the Era of Cognitive Delegation
This brings us to the core concept of cognitive delegation. Most of us are happy to delegate manual tasks—we use a dishwasher instead of washing by hand. But we have historically been reluctant to delegate thinking tasks. That is changing.
Professionals are now using agentic AI to handle the initial, often gruelling, phases of cognitive work. An analyst no longer has to spend four hours gathering data; they can delegate that to an agent and spend those four hours interrogating the data, finding the narrative, and formulating a strategy. This dramatically scales productivity and, more importantly, allows humans to focus on the highest-value work: critical thinking, creativity, and decision-making. We’re seeing this with ‘power users’, who are making 9 times more agentic queries than the average user, pointing towards deep integration into daily routines.
The Impact on Business Performance
So what does this mean for the bottom line? The evidence points towards a significant boost in enterprise efficiency, but the benefits are not distributed evenly.
Scaling Productivity and Finding the Power Users
The most obvious impact is on productivity. By automating the time-consuming, preparatory stages of knowledge work, companies can dramatically increase the output of their most valuable employees. Engineers, financial analysts, and market researchers can get to insights faster, accelerating project timelines and decision-making cycles. The Perplexity adoption data shows a strong correlation with GDP and education levels, suggesting that the most advanced economies and highly skilled workforces are the first to harness this advantage.
Not Everyone is on Board (Yet)
Adoption is far from uniform. Some sectors and companies are leaping ahead, while others are still standing on the sidelines. This “adoption heterogeneity” creates a real risk of a productivity gap opening up between the early adopters and the laggards. Businesses that successfully integrate agentic AI into the workflows of their high-value teams will likely build a formidable competitive advantage.
A Word of Caution: Governance is Not Optional
Let’s not get carried away. Handing over complex tasks to an autonomous AI system is not without its risks. What happens if an agent misunderstands a prompt and accesses confidential data? Or if it synthesises information from unreliable sources, leading to a flawed business decision?
These governance challenges are real and must be addressed proactively. Companies need a clear framework for:
– Data Security: Defining what data agents can and cannot access.
– Accuracy and Verification: Establishing processes to check the outputs of AI agents, especially for critical decisions.
– Accountability: Knowing who is responsible when an AI-driven process goes wrong.
Ignoring these issues is not an option. Building trust in these systems is paramount for widespread and responsible adoption.
The Future is Agent-Driven
If the current trends are anything to go by, we are at the very beginning of a massive wave. Market projections cited in the Artificial Intelligence News article estimate the agentic AI market will grow from around $8 billion in 2025 to a staggering $199 billion by 2034.
This growth will be fuelled by agents becoming more capable and more deeply integrated into the software we use every day. Imagine agents that can not only research a topic but also book the necessary travel, schedule the meetings, and prepare the presentation deck. This is the future of enterprise efficiency.
This isn’t science fiction anymore. The data shows it’s happening now. The question for businesses is no longer if they should explore agentic AI, but how they can integrate it into their core operations to unlock the next level of productivity.
How is your organisation thinking about moving beyond simple chatbots to true agent-driven automation? I’d be keen to hear your thoughts in the comments below.
