Enterprise applications are entering a new phase of automation, with Gartner forecasting that 40 percent of them will include task-specific AI agents by 2026 – up from less than 5 percent today.
The research firm says the rise of agentic AI will mark one of the fastest transformations in enterprise technology since the adoption of the public cloud.
Gartner analysts warn that CIOs have just three to six months to define their AI agent strategies or risk ceding ground to faster-moving competitors.
By 2035, the firm predicts, agentic AI will account for nearly $450 billion in enterprise software revenue, or 30 percent of the market.
“AI agents are evolving rapidly, progressing from basic assistants embedded in enterprise applications today to task-specific agents by 2026 and ultimately multiagent ecosystems by 2029,” said Anushree Verma, Sr Director Analyst at Gartner.
Why This Matters
Agentic AI is not simply about efficiency. It represents a rethinking of how enterprise software delivers value. Traditional applications have long been designed around user input. With AI agents, the software itself begins to anticipate, decide, and act.
For business leaders, the implications extend across the buying committee:
CIOs and CTOs will need to modernise infrastructure, ensure interoperability, and manage the risks of autonomous decision-making.
CFOs will ask whether productivity gains justify the costs of new platforms – and whether suppliers are guilty of “agentwashing,” selling old automation with a new label.
COOs and line-of-business leaders must identify where AI agents can accelerate decision cycles, reduce error rates, or improve customer experience.
CISOs will be concerned about governance, data sovereignty, and liability if an AI agent makes a misstep.
Enterprises that act decisively could gain a competitive advantage in speed, service quality, and operating efficiency. Those that hesitate risk higher costs, slower processes, and reduced customer loyalty.
From AI Assistants to Autonomous Agents
Gartner outlines five stages in the evolution of enterprise AI:
-
Assistants for Every Application (2025): By year-end, nearly every enterprise application will include some form of AI assistant. These systems simplify tasks but still depend on human input.
-
Task-Specific Agents (2026): 40 percent of enterprise applications will integrate agents that act independently – automating development, managing incidents, or resolving support cases.
-
Collaborative Agents Within Apps (2027): AI agents will begin working together inside applications, combining complementary skills to tackle more complex tasks.
-
Ecosystems Across Apps (2028): Networks of agents will collaborate across platforms, shifting user experience away from app interfaces toward agentic front ends.
-
The New Normal (2029): At least half of knowledge workers will be expected to create, govern, and deploy agents on demand.
This staged progression is intended as a roadmap, but the pace may vary. I
Industries with complex processes and heavy regulation – such as healthcare or financial services – are likely to move cautiously.
Retailers, telecoms, and technology firms may accelerate faster, seeing agents as a route to cost reduction and customer engagement at scale.
Early Use Cases Show Promise
The most immediate applications are already emerging.
Customer Service: Gartner forecasts that by 2027, self-service and live chat will surpass phone and email as the top customer service channels. “Agent assist” technologies are on track to be adopted by 73 percent of organisations by year-end, giving frontline staff real-time insights and suggested responses.
Collaboration Platforms: Unified communications providers are embedding AI agents to automate meeting notes, assign follow-up actions, and track project progress. For distributed workforces, this could ease the burden of coordination and reduce meeting fatigue.
IT Operations: In DevOps, AI agents are beginning to detect anomalies, resolve incidents automatically, and escalate only where human oversight is essential – cutting downtime and improving resilience.
These examples are narrow compared with the multiagent ecosystems Gartner anticipates later in the decade, but they demonstrate clear gains in productivity and service quality.
Risks and Challenges
Despite the enthusiasm, obstacles remain. Security leaders worry about agents taking unmonitored actions or exposing sensitive data.
HR executives must prepare employees for a future where agents are colleagues as much as tools. Regulators will demand transparency about how agents make decisions, particularly in financial services, healthcare, and government sectors.
The cultural challenge is also significant. Employees may resist working with autonomous systems, especially if organisations fail to communicate clearly about benefits, accountability, and career impact.
For IT leaders, change management could ultimately be as important as technology selection.
