AI agents are forcing companies to completely overhaul their operations
Artificial Intelligence is no longer some distant promise of the future — it has become a force that is literally reshaping how companies operate right now. And we are not talking about small tweaks or incremental improvements. The current landscape points to a deep restructuring that reaches everything from revenue models to the way each professional contributes within an organization.
The numbers back this up pretty directly: a Gartner survey, released in April 2026, gathered responses from 469 CEOs and senior executives around the world at the end of 2025, and 80% of them said that AI-driven automation will require deep changes in their companies’ operational capabilities. This is not just another data point from a survey. It represents a fundamental shift in global corporate thinking about the role of AI in business.
This is no longer a conversation about digitizing processes or dropping a chatbot into customer service. We are talking about something much bigger: the transition from digital businesses to autonomous businesses, where AI systems make decisions, manage entire workflows, and operate with very little human intervention. And this shift is happening now, not five or ten years from now.
And the most interesting part? Some of these very CEOs admit that this change could hurt their current profit models. AI agents have the potential to disrupt intermediated systems and traditional pricing negotiation processes, which forces a complete reassessment of how value is generated and captured. 🤯 So what exactly is going on, what did this survey reveal, and where are companies heading with all of this? That is exactly what we are going to explore here.
What the Gartner survey actually revealed
The survey did not deliver soft surprises. It delivered a pretty honest portrait of where business leaders stand mentally when it comes to artificial intelligence and what it means for the years ahead. Of the 469 executives surveyed, most acknowledge that automation is no longer a trend to watch from a safe distance. It is already inside the house, rearranging the furniture and changing the floor plan of operations without asking permission.
The 80% figure predicting deep changes in operational capabilities is especially telling because it does not come from analysts or consultants. It comes from the people running the organizations. These are CEOs and senior executives saying, in their own words, that the way their companies work today will not survive intact as autonomous AI agents are adopted at scale.
Another standout point in the survey is the acknowledgment, from a significant share of these leaders, that the accelerated adoption of autonomous systems could paradoxically erode the very business models that sustain their companies today. That is a powerful admission. It means these executives see disruption coming through the front door and still understand that not adopting this technology would be even riskier than adopting it with all the uncertainties it carries.
The survey also shows that the concern is not just technological — it is structural. Companies built on revenue models based on human labor volume, billable hours, or highly specialized repetitive tasks need to completely rethink their value proposition. When an artificial intelligence system can execute in minutes what an entire team would take days to finish, the equation changes radically, and pricing models, delivery methods, and customer relationships all need to keep up with that pace.
Most companies are still in the early stages, but that is about to change fast
One of the most relevant findings from the Gartner survey relates to the current stage of autonomous AI adoption within companies. More than half of CEOs said that automation in their organizations is still limited to specific tasks. In other words, AI is being used to solve isolated problems, automate individual steps within larger processes, and assist teams with well-defined activities.
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However, the picture changes dramatically when we look at short-term expectations. Only 13% of CEOs expect to remain at this basic level of automation by the end of 2028. That means the overwhelming majority plan to scale their AI usage significantly over the next two to three years. The ambition is clear: move beyond spot automation and reach operations where artificial intelligence permeates multiple layers of decision-making and execution.
More specifically, one-third of CEOs said their organizations will use adaptive, self-learning AI tools to assist in human decision-making. And more than a quarter of respondents plan to use AI operating primarily without human intervention. These numbers show that the conversation is no longer about whether companies will adopt autonomous agents, but about how deeply those agents will be woven into the operational fabric of the business.
Jennifer Carter, Senior Principal Analyst at Gartner, summed up this shift in perspective well when she explained that companies are looking at the entire end-to-end operations chain and asking themselves how to create entire workflows that fundamentally run on their own. These workflows can be supervised by humans, but the central goal is to maximize the automation opportunity at every possible step.
From digital businesses to autonomous businesses
There is a fundamental difference between a digital business and an autonomous business, and that distinction is at the heart of everything happening right now. A digital business uses technology to execute processes that were once analog. An autonomous business uses artificial intelligence to create autonomous workflows that operate, make decisions, and adapt without depending on human approval at every step. It is the difference between having a GPS that guides you and having a car that drives itself while you do something else.
This transition is happening across very different industries. In retail, AI systems already manage inventory, adjust prices in real time, and personalize offers for each individual customer — all in an automated and continuous way. In financial services, algorithms make credit decisions, detect fraud in milliseconds, and rebalance investment portfolios without any analyst needing to step in. In healthcare, language models and computer vision assist with diagnoses and organize patient flows with a precision that would be impossible to maintain manually at scale. These are not future scenarios. They are realities already in production at companies around the world.
What makes this movement even more significant is that autonomous workflows do not just eliminate simple operational tasks. They are starting to take on functions that previously required judgment, context, and accumulated experience. Generative AI systems are being integrated into decision-making pipelines in marketing, legal, logistics, and human resources. This redesigns not only what people do inside a company, but also how the company is organized, which roles make sense, which skills need to be developed, and how value is generated and distributed internally.
How business models need to reinvent themselves
The business transformation that AI is driving is not just a matter of operational efficiency. It directly impacts the core logic of how a company generates and captures value. Models based on labor-intensive work, information brokering, or execution of standardized tasks are coming under increasing pressure. Accounting firms, law offices, marketing agencies, IT consultancies — all of these categories are revisiting their value propositions to understand what remains relevant when automation takes over the more operational layers of work.
According to the Gartner survey, one possible response to this scenario involves migrating to recurring, outcome-based revenue models. Instead of charging for hours worked or volume of tasks completed, companies deliver value based on measurable outcomes. This is a shift that was already gaining traction in sectors like software and managed services, but it is now expanding into areas that historically operated very differently.
The path many companies are taking involves a reorientation toward what we often call high-complexity intangible value: strategy, relationships, contextual creativity, applied ethics, and risk management in uncertain environments. These are dimensions that AI still cannot replicate consistently, especially when the human, cultural, and emotional context is what determines the outcome. That does not mean these areas are immune to automation. It means the balance point between what the machine does and what the human adds is still being calibrated in real time.
Another important movement is the creation of new business models built directly on top of AI infrastructure. Startups and technology companies are building entire products where artificial intelligence is not an added feature — it is the product itself. Platforms for automating legal processes, tools for generating personalized content at scale, fully autonomous customer service systems, AI agents that manage paid media campaigns from start to finish. These models have cost structures, scalability, and delivery methods that are completely different from traditional models, and they are growing at a speed that few established industries can match.
Machine customers and the new frontier of relationships
A particularly interesting aspect that the Gartner survey addressed is the expectation that companies will use AI to deepen relationships with existing customers and, at the same time, serve an entirely new type of customer: machine customers. The concept might sound strange at first, but it makes perfect sense in the context of increasingly automated businesses.
Machine customers are AI systems or autonomous agents that make purchasing, contracting, or subscription decisions on behalf of organizations or individuals. Gartner predicts that companies will expand the use of dedicated business units focused exclusively on this type of customer. This fundamentally changes the dynamics of sales, marketing, and customer service, because the counterpart is no longer a person with emotions, subjective preferences, and cognitive biases — it is an algorithm that evaluates proposals based on data, efficiency, and cost-effectiveness.
For companies that rely heavily on human negotiation, sales charm, or personal relationships as a competitive advantage, this transition represents a considerable challenge. The decision criteria change, the communication channels change, and the speed of negotiation changes. Adapting to this new landscape requires rethinking not just the commercial strategy, but the very architecture of how a company presents itself and delivers value to the market.
Trust, accuracy, and data hygiene as a foundational pillar
As companies increase their reliance on AI agents and autonomous workflows, one issue becomes absolutely central: trust, accuracy, and data integrity. The Gartner survey is emphatic on this point. The foundation on which companies deploy their AI becomes an essential business requirement, and poor data hygiene will have lasting effects.
This is not a minor technical detail. When an AI system operates autonomously, the decisions it makes are only as good as the data feeding its models. Inconsistent, outdated, biased, or poorly structured data does not just produce bad results. It erodes the trust of customers, partners, and regulators in the entire operation. And rebuilding that trust once it is lost is a far more expensive and time-consuming process than building it right from the start.
Jennifer Carter reinforced this point by emphasizing that as adoption accelerates, data and AI governance needs to keep pace. Companies that neglect this dimension in the name of implementation speed are creating risks that will materialize painfully in the medium term. Data integrity is not just a compliance issue — it is a matter of operational survival in an environment where automated decisions impact real customers, real revenue, and real reputation.
The expanded role of CIOs in this new reality
With artificial intelligence taking an increasingly central role in operations, CIOs are gaining responsibilities that go far beyond managing technology infrastructure. They are becoming key players in defining business strategy, governing AI, and orchestrating business transformation as a whole.
Carter noted that CIOs need to look simultaneously inward and outward from their organizations. Internally, it is necessary to assess operations, identify automation opportunities, and ensure that AI systems are performing with the accuracy and reliability expected. Externally, it is essential to monitor the market, understand customer needs, and track how the autonomous ecosystem is evolving in each industry.
This dual focus is a significant challenge. Many CIOs were trained in a context where technology was a support function for the business. Now, technology is the business. And leading this transition requires a combination of strategic vision, deep technical knowledge, and the political skill to align different areas of the company around a shared vision for the autonomous future.
The human challenge within the transformation
Talking about business transformation driven by artificial intelligence without talking about the people who live within these organizations would be telling only half the story. The impact on teams is real, deep, and in many cases still poorly understood by leadership itself. The Gartner survey captures this tension well: while CEOs acknowledge the inevitability of change, many still lack clarity on how to prepare their teams to operate in environments where autonomous workflows are the norm rather than the exception.
A recent study by Writer brought a data point that adds to this picture in a striking way: 61% of technology leaders reported fear of losing their jobs if they fail to successfully guide their organizations through AI adoption. Some of these professionals said their own skills could become obsolete in the age of artificial intelligence. This shows that the pressure is not just at the operational base of companies. It is at the top.
The issue is not simply training people to use new tools. It is a much deeper shift in the relationship that professionals have with their own work. When an AI system takes over the execution of a task, the human role shifts to supervision, curation, interpretation, and decision-making in situations of high ambiguity. That requires a different profile — not necessarily smarter or more experienced in the traditional sense, but more adaptable, more comfortable with uncertainty, and more capable of working in close collaboration with automated systems. This transition in competencies is one of the biggest challenges companies face right now.
Carter emphasized that managing AI agents will become an integral part of workflows across the entire company. But at the same time, this change frees up time for teams to focus on the uniquely human parts of the business. As she put it quite directly, automation allows companies to get rid of some of the tedious, repetitive work, opening up space for people to focus on what is specialized and genuinely human.
Beyond that, there is a cultural dimension that cannot be ignored. Companies with highly hierarchical cultures, rigid approval processes, and a natural resistance to experimentation tend to encounter more friction in adopting automation at scale. Not because the technology does not work, but because the human organization around it is still calibrated for a different pace and a different logic. Real business transformation, therefore, starts well before any technology implementation. It starts with the willingness of leadership and teams to fundamentally rethink how work gets done, how decisions are made, and how success is measured within the new reality that artificial intelligence is building. 🚀
The next few years will be decisive in determining which companies managed to navigate this transition smartly and which ones got stuck in models the market already left behind. The message from the Gartner survey is clear: the time to act is not tomorrow — it is now.
