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According to Gartner Inc., 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% today.

As organizations accelerate digital transformation, agentic AI in enterprise applications will move beyond individual productivity, setting new standards for teamwork and workflow through smarter human-agent interactions.

Gartner's best case scenario projection predicts that agentic AI could drive approximately 30% of enterprise application software revenue by 2035, surpassing $450 billion, up from 2% in 2025.

"AI agents will evolve rapidly, progressing from task and application specific agents to agentic ecosystems," said Anushree Verma, Sr Director Analyst at Gartner. "This shift will transform enterprise applications from tools supporting individual productivity into platforms enabling seamless autonomous collaboration and dynamic workflow orchestration."

C-level executives at software organizations have a crucial three- to six-month window to define their agentic AI product strategy, as the industry is at an inflection point. Organizations that do not plan to develop agentic capabilities risk falling behind their peers. Managing strategic priorities requires a focused approach across the five stages of agentic AI evolution.

Stage 1: AI Assistants for Every Application

Gartner predicts that by the end of 2025 most enterprise applications will have embedded assistants. AI assistants are the precursor to agentic AI. They simplify tasks and interactions for users but depend on human input and do not operate independently. The most common misconception is referring to these AI assistants as agents, a misunderstanding known as "agentwashing."

"C-level leaders at software organizations need to offer suitable AI assistants today that can be seamlessly integrated with their enterprise apps to improve user productivity, initializing the shift away from traditional keyboard-centric interfaces," said Verma.

Stage 2: Task-Specific Agent Applications

Gartner predicts up to 40% of enterprise applications will include integrated task-specific agents by 2026, up from less than 5% today.

Adding task specialization capabilities evolves AI assistants into AI agents. These AI agents have the capacity to operate and perform complex, end-to-end tasks. An example is an AI-driven cybersecurity threat response agent that scans network traffic, system logs and user behavior patterns in real time. The agent then assesses and initiates a response as appropriate.

Stage 3: Collaborative AI Agents Within an Application

Collaboration among AI agents will redefine the boundaries of enterprise applications. By 2027, Gartner predicts one-third of agentic AI implementations will combine agents with different skills to manage complex tasks within application and data environments.

Today's AI agents often focus on individual, task-specific functions, which can limit their overall versatility and thus business impact. Collaborative agents will offer more adaptable and scalable solutions by learning from real-time data and adjusting to new conditions. At this stage, C-level leaders at software organizations will need to invest in and develop communication and interoperability standards to enable agent-to-agent communication.

Stage 4: AI Agent Ecosystems Across Applications

By 2028, AI agent ecosystems will enable networks of specialized agents to dynamically collaborate across multiple applications and multiple business functions, allowing users to achieve goals without interacting with each application individually.

Gartner estimates that by 2028, a third of user experiences will shift from native applications to agentic front ends, driving new business models and pricing structures.

Stage 5: The "New Normal" for Democratized Enterprise Apps

Gartner predicts that by 2029, at least 50% of knowledge workers will develop new skills to work with, govern or create AI agents on demand for complex tasks.

"As agentic AI matures, standardized protocols and frameworks will enable seamless interoperability, allowing agents to sense their environments, orchestrate projects and support a wide range of business scenarios," said Verma.