What are AI agents?

AI agents are intelligent autonomous systems that can plan, carry out 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 coordinate multiple tools and systems, and even collaborate with other agents to complete complex, multi-step 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 similar to simple reflex agents in that they react to change, but differ in that they retain memory (a model) of the environment they observe. This memory enables them to respond 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 according to how well a decision achieves a desired outcome. Utility represents a numerical measure of desirability, so these agents aim to maximise 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 activate cooling when temperatures increase. If paired with a model-based reflex agent, the system remembers that certain rooms heat up more quickly in sunlight and adjusts cooling priorities accordingly.

Examples of AI agent use cases in the enterprise

AI agents are already having enterprise-wide impacts beyond merely 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 realise business value at scale. SAP’s data products harmonise 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 are, Joule Agents can be customised to ensure compliance, security, and compatibility with existing systems.

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

AI agents in finance and accountancy

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

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

Other AI agent use cases in finance include:

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

These capabilities enable finance teams to be more efficient and move from reactive to proactive approaches, especially when managing incoming or overdue payments.

AI agents in supply chain and procurement

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

Joule’s Sourcing Agent can assist. 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 the supply chain and procurement include:

• Automatic invoice processing to reduce the burden of manual invoice data input. These AI agents extract, validate, and identify discrepancies quickly.
• Lead time analysis for detecting and correcting inaccurate lead time data to prevent stock shortages and delays.
• Defect detection by analysing 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 personalised talking points for one-to-one meetings, align employee goals with business objectives, and provide constructive feedback.

Other AI agent use cases in HR include:

• Job description creation to save recruiters time by drafting high-quality job descriptions using natural language generation. It also highlights vague or biased phrasing that can discourage potential candidates.
• Applicant screening to enable evaluation of CVs 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 enhance 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 include:

• 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 faults. With machine learning techniques, processes can adjust autonomously to maintain manufacturing standards.

AI agents in marketing and commerce

Marketing teams use AI agents to prioritise high-quality leads, personalise 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 analyse intent signals such as purchase history to identify leads ready to buy. Then, they can prompt human marketers or account teams to engage these leads with personalised campaigns or direct outreach, connecting with them when their level of interest is at its highest.

Complementing this is the Catalogue Optimisation 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 between enterprise AI agents and humans can lead to significant outcomes. Other AI agent use cases in marketing include:

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

AI agents in IT and governance

AI agents are increasingly supporting IT teams in defending against threats and remaining 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 behaviour, helping IT teams respond to potential threats before they escalate.

AI agents in customer support

Customers expect prompt and personalised 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 support. For existing customers, the Q&A Agent is also available to assess the intent of an enquiry 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 queries. Other use cases that demonstrate this include:

• Ticket triage and routing to automatically classify incoming support requests and direct them to the appropriate team based on urgency, topic, and sentiment.
• Service case summarisation: for condensing lengthy service threads into concise summaries, improving handovers and reducing resolution time.
• Agent assistance that provides 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 objectives and has buy-in from stakeholders across the organisation.

To begin, identify use cases where AI agents can deliver measurable value. These typically include repetitive, error-prone, and time-consuming processes, such as 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 rely on high-quality, harmonised data, so assessing 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 vital. AI integration is inherently cross-functional, so involve business leaders, IT teams, and end users from the very beginning. Clearly communicate the benefits of agentic AI and address concerns related to change management, data privacy, and the impact on employment.

Organisations 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 maintain 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 organisation's unique needs.

Lay the foundations for business transformation

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

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