AI agents are fast becoming a major imperative across industries. Leading companies are fueling agentic adoption, going beyond automation, and pinpointing key areas where agentic AI can make an impact.
Sydney Scott
Editorial Strategist, AI
Workday
Artificial intelligence (AI) has already become embedded into the core of enterprise operations, automating tasks and providing data-driven insights at scale. But a new, even more sophisticated wave of AI is taking hold in 2025 that takes enterprise AI to a new level. AI agents—autonomous systems that can reason and act with limited human intervention—are handling workflows that once relied entirely on human judgement.
As organizations grapple with rising complexity, constrained resources, and relentless change, AI agents offer something rare: scalable intelligence that acts with purpose. And this evolution is underway across industries; Gartner predicts 33% of enterprise software applications will include agentic AI by 2028—up from just 1% in 2024—and at least 15% of business decisions will be made autonomously via agents.
From finance to healthcare to retail and more, intelligent AI agents are fundamentally reshaping how teams operate, how decisions are made, and the customer experiences companies can provide.
Report
By 2028, 33% of enterprise software applications will include agentic AI—up from just 1% in 2024.
Agentic AI is defined by its capacity for purposeful, self-directed behavior. Agentic systems sense their environment, process signals, make informed decisions, and take action—all in pursuit of specific outcomes. What makes them distinct from other AI systems isn’t just what they do, but how they do it: continuously, contextually, and with limited dependence on human direction.
Several core capabilities make an AI system agentic:
Together, these capabilities define what makes AI agentic: the ability to act independently, reason through trade-offs, adapt to change, and pursue outcomes with minimal human direction. But agentic AI behavior isn’t one-size-fits-all; it shows up in different ways depending on the agent’s design and objective.
AI agents vary widely in complexity and capability, from rule-based systems that follow predefined triggers to adaptive agents that learn, reason, and collaborate. Each type is purpose-built for a specific class of tasks and operational goals.
Here are the eight AI agent types most commonly embedded in business workflows:
Reactive agents, also known as simple reflex agents, respond to current inputs based on predefined rules. They’re ideal for automating repetitive, structured decisions, such as filtering spam emails or initiating a default process when a sensor threshold is crossed.
Model-based reflex agents maintain an internal model of the world around them. This allows them to act even when not all information is visible—what’s known as a partially observable environment. For example, in warehouse robotics, they can infer the likely location of an item when some shelf data is missing by using their internal understanding of layout and past patterns.
Goal-based agents are designed to achieve specific goals by selecting actions that move them closer to a desired outcome. Unlike reflex agents that react to inputs moment by moment, goal-based agents evaluate the current state, consider possible future states, and choose the best path toward their objective.
For example, in a logistics scenario, a goal-based agent might reroute deliveries based on traffic and weather conditions to ensure on-time arrival.
Utility-based agents evaluate not just whether an action will meet a goal, but how valuable that outcome is relative to others. A utility-based agent, for instance, might prioritize fulfilling an order for a high-value client over a lower-value one when resources are limited. Their strength lies in making trade-offs that optimize business impact, not just task completion.
Learning agents improve their performance over time by analyzing new data and feedback. Rather than relying solely on static rules, they adjust their behavior based on experience, enabling them to handle unfamiliar situations more effectively.
For example, a fraud detection agent might start with historical patterns but learn to flag new forms of suspicious activity as it encounters evolving attack methods.
Also known as multi-agent systems, collaborative agents coordinate with other agents—or with humans—to solve large-scale or interdependent problems. In supply chain management, for example, a network of collaborative agents can each monitor different logistics points (such as warehousing, shipping, inventory) and collectively adjust operations in real time to minimize bottlenecks.
Commerce AI agents are built for high-volume retail and e-commerce environments. They power dynamic pricing systems that can adjust pricing in real time based on current demand, competitor activity, and available inventory. For example, during a flash sale, a commerce agent might automatically raise prices for fast-selling items while discounting slower-moving stock to maximize revenue.
AI customer support agents use natural language processing to resolve frequent inquiries without requiring human intervention. They can reset passwords, track orders, or handle refunds, freeing up live agents for more complex customer issues. As they interact with more users, these systems become smarter, faster, and better at deflecting volume while maintaining service quality.
Leading industries are putting AI agents to work in ways that go well beyond process automation. With each passing month, the real world examples of AI agents are increasing exponentially.
Agentic AI systems are helping organizations make strategic decisions, tackle complex tasks, improve resilience, and elevate experiences for employees and customers alike. Let's take a closer look at how AI agents are already driving meaningful progress in key sectors:
Human resources is increasingly tasked with balancing operational efficiency and personalized employee support. AI agents are helping HR teams scale critical processes—like onboarding and internal mobility—while enhancing the employee experience.
Finance teams operate in a high-stakes environment that demands accuracy, speed, and control.
From daily transactions to strategic planning, the volume and complexity of financial data continues to rise—leaving little room for inefficiency. AI agents in finance are stepping in to help teams move faster while staying compliant and precise.
In healthcare and life sciences, operations must meet the dual challenge of compliance and compassion. AI agents for healthcare offer new ways to reduce administrative burden, improve scheduling, and ensure the accuracy of credentialing and audit trails while maintaining human focus on what matters most: Patient outcomes.
Higher education institutions face growing complexity across enrollment, curriculum management, and student services. AI agents in education help colleges and universities operate more efficiently, delivering a better experience for students and faculty.
In service-focused industries like retail and hospitality, responsiveness and efficiency are vital. AI agents in retail help frontline teams manage everything from staffing to supply chain in real time, while maintaining a strong customer experience.
Leaders should map potential AI agent use cases using two key factors: strategic value and readiness for automation.
While agentic AI systems are increasingly powerful, their impact depends on how thoughtfully they are applied. Choosing the right use cases is one of the most important steps in any agent deployment strategy.
Identifying the right AI agent functionality for your business is not just about what can be automated—it’s about where agents can deliver meaningful business value with clarity and control.
The best use cases tend to share three characteristics:
To help prioritize, leaders can use a simple matrix that maps potential use cases by their strategic value and readiness for automation:
This approach helps organizations stay focused and avoid overengineering for use cases that are too complex or too marginal. Starting with high-impact, high-readiness workflows builds momentum, confidence, and clarity.
Choosing the right use cases also means aligning stakeholders around success metrics. That might mean reduced cycle time in finance, faster resolution in HR, or improved compliance in healthcare. With these metrics in place, organizations can scale with purpose—and make agentic AI a sustained part of how business gets done.
Eighty-three percent of workers believe AI can boost their careers, help them develop skills, and allow them to focus on more meaningful work.
It may seem that agents are poised to replace human employees in many areas but agents (and other AI technologies) actually bring new opportunities for employees to be more strategic, innovative, and impactful.
According to the Workday research, 83% of workers believe that AI can boost their careers by helping them develop new skills in their roles and focus on more meaningful work. These findings reflect a growing shift: AI agents are not just tools of efficiency, but enablers of deeper human contribution.
Rather than displace jobs, agentic AI changes the nature of work. Agents take on repeatable, decision-bound tasks that follow clear logic or structured workflows. In doing so, they free up people to focus on work centered on creativity, intuition, innovation, and the ability to adapt amidst change and uncertainty.
This shift raises new requirements for how organizations design work.
Human workers need access to transparent agent behavior, opportunities to intervene, and training to develop new digital fluency. Organizations, in turn, must invest in skills strategies that reflect the changing nature of work, not just the technologies driving it.
Workday research highlights that the most valuable business skills are not narrowly technical, but human. Relationship building, emotional intelligence, ethical decision making, and adaptability rank among the top capabilities believed to be irreplaceable by new AI technologies. These are not soft skills—they are durable ones, even as AI agents take on a bigger role.
As agentic AI becomes a more embedded part of how business operates, the question is not whether human work will remain relevant. It’s how organizations will empower it with AI to grow its impact and value.
The future of agentic AI is not a linear extension of what we see today. AI agents will introduce new frameworks for enterprise systems, workforce design, and decision-making models. The most important developments to watch include:
The role of agentic AI is expanding—and so is the scope of decisions that must be made around it. Leaders must be intentional when shaping that transformation to unlock long-term advantage.
Want to go deeper on how agentic AI is transforming work at scale? Discover how Workday is building enterprise-grade AI agents designed for trust, impact, and action.
Report
A gap between effort and impact is the real ROI problem. Discover the solution.