What are AI agents?

AI agents are intelligent autonomous systems that can plan, perform tasks, and make decisions on behalf of humans. They interpret the intent of requests within context, learn from historical data, and adapt dynamically to changing conditions in real time.

When integrated into an enterprise ecosystem, AI agents orchestrate multiple tools and systems, and even collaborate with other agents to complete complex, multistep workflows.

Types of AI agents

There are five main types of enterprise AI agents:

1. Simple reflex agents operate using an “if-this-then-that” logic. In other words, if they perceive a change, they respond.
2. Model-based reflex agents are like simple reflex agents in that they react to change, but different in that they retain memory (a model) of the environment they observe. This memory enables them to react beyond direct stimuli.
3. Goal-based agents use search or planning algorithms to evaluate possible actions, predict outcomes, and choose the optimal sequence of actions to achieve their intended goal.
4. Utility-based agents act based on how well a decision achieves a desired outcome. Utility represents a numerical measure of desirability, so these agents aim to maximize performance while balancing trade-offs.
5. Learning agents continuously improve their performance by observing the outcomes of an action and evaluating whether those outcomes were good or bad.

Enterprises can also combine multiple AI agents into multi-agent systems to handle complex workflows.

For example, a simple reflex agent in an HVAC system might trigger cooling when temperatures rise. If paired with a model-based reflex agent, the system remembers that certain rooms heat faster in sunlight and adjusts cooling priorities accordingly.

Examples of AI agent use cases in the enterprise

AI agents are already making enterprise-wide impacts beyond just productivity gains. SAP’s Joule Agents exemplify this transformation.

Joule Agents are systems of enterprise AI agents that can integrate across business functions to help teams accelerate complex, multistep workflows and realize business value at scale. SAP’s data products harmonize disparate data across silos, enabling Joule Agents to deliver insights and recommendations grounded in the full context of a business. No matter how unique your processes, Joule Agents can be customized to ensure compliance, security, and compatibility with existing systems.

Here are some of the top business use cases where Joule Agents make the most difference.

AI agents in finance and accounting

Finance teams and contract accountants seek to speed up payments and close faster. However, incorrect invoices and missing payments are time-consuming issues that require manual intervention.

Joule’s Dispute Resolution Agent automates the dispute process by analyzing the details of invoices and contracts and then flagging discrepancies or mismatches. It does this proactively instead of reactively, advising finance teams how to proceed with a generated credit memo.

Other AI agent use cases in finance include:

• Late payment prediction to assess customer payment behavior to forecast delays, enabling proactive outreach and strengthening working capital.
• Automatic payment matching to streamline the slow and manual process of matching payments to open invoices to reduce days sales outstanding.
• Financial close resolution that analyzes historical closing cycles, flags anomalies, and guides teams to resolution before they compound into organization-wide delays.

These capabilities allow finance teams to be more efficient and shift from reactive to proactive approaches, especially when managing incoming or late payments.

AI agents in supply chain and procurement

Procurement operations are complex, data-heavy, and time-sensitive. Teams that manage supply chains must make decisions fast to keep up with shifting business conditions and mitigate supply chain disruptions.

Joule’s Sourcing Agent can help. It identifies sourcing opportunities, evaluates suppliers, and initiates RFPs autonomously, streamlining procurement cycles and improving cost efficiency.

This helps keep enterprises relevant and competitive. Other AI agent use cases in supply chain and procurement include:

• Automatic invoice processing to reduce the burden of manual invoice data entry. These AI agents extract, validate, and identify discrepancies fast.
• Lead time analysis for detecting and correcting inaccurate lead time data to prevent stockouts and delays.
• Defect detection by analyzing image data to quickly and accurately identify errors and drive product quality improvement.

AI agents in human resources

Managers are using enterprise AI agents to empower their teams. The Performance and Goals Agent, in particular, automates data collection to ensure leaders have relevant insights about every employee.

Because these AI agents understand the context of business data, they can generate personalized talking points for 1:1 meetings, align employee goals with business objectives, and provide constructive feedback.

Other AI agent use cases in HR include:

• Job description generation to save recruiters time by drafting high-quality job descriptions using natural language generation. It also flags vague or biased phrasings that can turn off potential candidates.
• Applicant screening to enable evaluation of resumes at scale. It highlights top candidates in a way that also reduces unconscious bias.
• Interview preparation to equip managers with interview questions tailored to the role and designed to reveal a candidate’s true potential.

AI agents in manufacturing

AI agents enable factories to be more productive by anticipating and mitigating delays. Joule’s Shop Floor Supervisor Agent, for example, assists supervisors by first identifying potential disruptions and then recommending schedule adjustments to address them. By providing visibility into the severity of these issues and the dependencies involved, these agents proactively prevent unplanned downtime and boost overall operational efficiency.

AI agents provide contingencies in the event of bottlenecks and delays, enabling operations to adjust dynamically. Other AI agent use cases that support this are:

• Predictive maintenance AI agents use sensor data to determine when equipment needs to be serviced or if parts need to be replaced—before they fail.
• Quality control helps improve consistency and reduce waste by identifying defects. With machine learning techniques, processes can adjust autonomously to maintain manufacturing standards.

AI agents in marketing and commerce

Marketing teams use AI agents to prioritize high-quality leads, personalize customer interactions, and drive conversions. By automating these basic tasks, marketers can shift their focus to the aspects of their jobs that require a human touch.

For example, AI agents can analyze intent signals like purchase history to identify leads ready to buy. Then, they can prompt human marketers or account teams to engage these leads with personalized campaigns or direct outreach, connecting with them when their interest level is highest.

Complementing this is the Catalog Optimization Agent, which continuously updates prices and product entries. It dynamically restructures content to align with changes in search intent to improve ranking in search engine results.

The collaboration of enterprise AI agents and humans can result in impactful outcomes. Other AI agent use cases in marketing include:

• Customer segmentation that dynamically groups customers based on behavior, preferences, and intent, enabling more targeted and effective campaigns.
• Product recommendations that analyzes real-time interactions to suggest relevant products, increasing conversion rates and customer satisfaction.
• Content generation for creating product descriptions, campaign copy, and promotional messaging tailored to audience segments and SEO goals.

AI agents in IT and governance

AI agents increasingly support IT teams in defending against threats and staying compliant by automating compliance tasks, monitoring system health, and enforcing policies.

AI agent use cases in IT include:

• Policy enforcement to monitor user activity and system configurations to ensure compliance with internal and external regulations.
• Data governance to maintain data integrity by detecting inconsistencies, enforcing standards, and managing access controls.
• Security monitoring to identify unusual patterns in system behavior, helping IT teams respond to potential threats before they escalate.

AI agents in customer support

Customers expect fast and personalized support. AI agents help service teams meet these demands efficiently and at scale.

The Shopping Agent, for example, provides new customers with product details, comparable options, and order assistance. For existing customers, the Q&A Agent is also on hand to assess the intent of a query and provide accurate answers to them.

The capabilities of these AI agents reduce response times, improve customer satisfaction, and free up human agents for more complex tickets. Other use cases that demonstrate this include:

• Ticket triage and routing to automatically classify incoming support requests and route them to the appropriate team based on urgency, topic, and sentiment.
• Service case summarization: for condensing long service threads into concise summaries, improving handoffs and reducing resolution time.
• Agent assistance that delivers real-time suggestions, next-best actions, and knowledge resources during live interactions.

Practical guidance: agentic AI integration

Implementing AI agent solutions requires a strategic approach that supports business goals and has buy-in from stakeholders across the organization.

To begin, identify use cases where AI agents can deliver measurable value. These typically include repetitive, error-prone, and time-consuming processes, like invoice processing or dispute resolution. Workflows that are data-intensive, complex, and cross-functional, or critical to compliance, can also be streamlined.

Next, assess your data readiness. Enterprise AI agents depend on high-quality, harmonized data, so evaluating current infrastructure for real-time access, integration capabilities, and governance standards is essential. SAP’s data cloud and analytics tools can help prepare for successful AI integration by establishing a single source of truth.

Launch a pilot project focused on a manageable use case, such as dispute resolution. Define clear, quantifiable success metrics, such as time saved, errors reduced, or customers satisfied. Set a baseline and monitor performance closely to validate impact.

Engaging stakeholders throughout the implementation process is crucial. AI integration is inherently cross-functional, so involve business leaders, IT teams, and end users from the outset. Convey the benefits of agentic AI clearly and address concerns related to change management, data privacy, and job impact.

Organizations that have deployed AI into their workflows have seen significant increases in productivity and reductions in operational costs. Not to mention higher customer satisfaction rates. To sustain the business value of AI over time, it is vital to create feedback loops for further improvement.

As AI agents learn from new data, their insights can increasingly inform other use cases that address an organization's unique needs.

Lay the foundation for business transformation

As enterprise AI agents mature, they become digital partners that enhance human judgment in ways that accelerate innovation. The organizations that take the step of AI integration today will be ready for the next era of breakthrough performance—one where better decisions are made faster, processes are more efficient, and results are more within reach.

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