Business Use Cases for AI Agents [Not to Be Confused With AI Assistants]

While many organizations use AI assistants, like chatbots and expense tracking bots, fewer organizations use AI agents. 

An AI agent is an artificial intelligence tool capable of executing complex tasks independently and driving outcomes. While not yet widely adopted, these agents hold significant promise for customer service, finance operations, supply chain optimization, and more.

Today, we’re exploring the business use cases of AI agents with a focus on customer service, supply chain management, and finance operations.

AI Agents vs. AI Assistants​

While the terms AI agents and AI assistants are sometimes used interchangeably, they serve very different purposes.

AI Assistants ​

These are task-specific tools designed to provide information and respond to queries. They typically assist with administrative tasks, reminders, and customer-facing interactions. 

Chatbots integrated into websites are common examples of AI assistants. Their actions are often pre-programmed or rely on narrow, rule-based logic. This makes them well-suited for automating routine tasks that don’t require contextual understanding.

AI Agents

These AI tools operate with greater autonomy and complexity. In addition to making decisions and executing processes, they drive outcomes by leveraging advanced algorithms, reinforcement learning, and predictive analytics. 

AI agents can collaborate with other systems, such as ERP systems, supply chain management systems, and CRM platforms, to manage workflows from start to finish. At the same time, they can draw on knowledge bases to apply relevant information to their decisions.

For instance, an AI agent in a manufacturing setting might consult a knowledge base of equipment manuals and maintenance history to predict maintenance needs.

Unlike AI assistants, agents are proactive—they initiate actions, optimize processes, and adapt based on evolving inputs. 

Business Use Cases for AI Agents

1. AI Agents for Customer Service

Customer service is one of the most impactful areas for deploying AI agents, especially in large-scale businesses. 

AI agents go beyond the traditional chatbot functions by autonomously managing entire customer journeys through dynamic, context-aware interactions.

Consider the following use cases:

Personalized and Proactive Customer Support

AI agents can anticipate customer needs based on behavioral data. They provide personalized suggestions and support at key moments in the customer journey.

For example, an AI agent in a telecommunications company might monitor customer data usage and proactively offer a data plan upgrade when it detects an increase in usage.

Automated Issue Resolution Across Channels

AI agents can manage multi-channel support autonomously, ensuring a seamless experience whether customers are engaging via email, social media, or chat. They can answer routine queries, intelligently route complex issues to human agents, and resolve certain problems end-to-end without human intervention.

For example, in an eCommerce environment, an AI agent might be used to resolve refund requests without manual processing. The AI could directly interface with logistics systems to manage returns, refunds, and inventory updates.

Reducing Operational Costs Through Efficiency

By automating responses to the most common queries, AI agents dramatically reduce call center workload and operational costs. This frees up human agents to focus on high-value interactions, improving both efficiency and overall customer experience.

Keep in mind that the integration of AI agents in customer service entails continuous model training on industry-specific language and behavior patterns. This means regularly updating models with new customer interaction data to refine their understanding of evolving terminology.

2. AI Agents in Supply Chain and Operations​

AI agents are becoming critical to supply chain and operational management, where their ability to make real-time, autonomous decisions allows businesses to be more agile and responsive. AI agents reduce the need for human intervention, leading to streamlined processes and enhanced decision-making across the supply chain.

Consider the following use cases:

Inventory Management and Demand Forecasting

AI agents can analyze historical sales data, market trends, and supply chain conditions to predict future inventory needs and ensure optimal stock levels.

For example, in retail, an AI agent might be used to analyze seasonal trends and regional sales data to forecast inventory needs. By anticipating high demand during peak seasons, the agent can ensure sufficient stock levels.

Real-time Supply Chain Optimization

AI agents in the supply chain use real-time data to detect supply chain disruptions and automatically trigger contingency plans, such as selecting alternative suppliers or adjusting production schedules. This level of agility is crucial in mitigating the impact of unexpected events, like weather disruptions or geopolitical risks.

For example, an AI agent monitoring a global supply chain for an automotive manufacturer might detect delays in the delivery of critical parts and autonomously source the components from an alternate supplier.

Predictive Maintenance for Operational Equipment

In industries reliant on heavy machinery, AI agents can enable predictive maintenance by monitoring equipment and scheduling repairs before issues cause downtime.

Keep in mind that the deployment of AI agents in supply chain operations often requires integration with IoT devices and advanced analytics tools. This means taking the time to configure data streams and ensure seamless communication between systems.

3. AI Agents in Finance Operations​

AI agents in finance go far beyond automating transactions. Their advanced decision-making capabilities allow them to play a pivotal role in financial planning, risk management, and investment strategies.

Consider the following use cases:

Automated Financial Forecasting and Budgeting

AI agents excel at forecasting by analyzing transactional data, market trends, and internal financial metrics to generate highly accurate financial projections. These agents enable businesses to anticipate cash flow needs, identify spending inefficiencies, and recommend budgeting adjustments.

For example, in a retail chain, an AI agent might analyze daily sales data, seasonal patterns, and promotional impacts across multiple store locations. Based on this analysis, it could forecast cash flow for each location and suggest adjusting staffing levels or inventory purchases to align with expected demand.

Risk Management and Compliance

AI agents can monitor financial transactions for anomalies and fraud, ensuring compliance with regulatory frameworks. By analyzing large datasets, agents can detect patterns that may indicate risk exposure and alert finance teams to potential issues before they escalate.

For instance, in a multinational corporation, an AI agent might track transactions across borders to identify any potential violations of local tax laws. This would help the organization stay compliant with international regulations.

Cash Flow Optimization and Liquidity Management

AI agents can analyze cash flow patterns, expenses, and revenue cycles to provide real-time insights that optimize liquidity and ensure sufficient cash reserves. They can analyze accounts payable and receivable, assess working capital needs, and forecast cash flow under different scenarios.

For example, a distribution company might use AI to analyze daily sales data and vendor payment schedules, suggesting changes to payment terms to prevent cash shortfalls. By forecasting when cash reserves might drop too low, the agent could recommend delaying non-essential payments or securing short-term credit to ensure uninterrupted distribution of goods.

Strategic Recommendations for AI Agent Deployment​

1. Align Your AI Strategy with Business Objectives

Before implementing AI agents, it’s essential to understand what strategic goals they should support—be it enhancing operational efficiency, improving customer experience, or driving financial decision-making.

If the goal is to streamline operations, AI agents should focus on automating back-office tasks, optimizing workflows within ERP systems, and reducing manual data entry. 

Alternatively, if the priority is customer satisfaction, then AI agents should focus on managing multi-channel support and providing proactive service. 

Our ERP selection consultants always tell clients to involve key stakeholders early in the planning phase to ensure alignment between IT deployments and business goals. This means collaborating with various departments to identify the areas where AI can have the most immediate impact. 

2. Begin Change Management Early

Employees may perceive AI as a threat to their jobs, fearing that automation could replace their roles. To address these concerns, it’s crucial to identify change barriers upfront and develop a change management strategy to address them.

One of the most important aspects of change management is training. AI-related training should focus on upskilling employees so they can work alongside AI agents. This means regularly scheduling training sessions tailored to specific roles and focusing on skill sets like interpreting model outputs and troubleshooting basic issues.

Another equally important aspect of change management is communication. We recommend developing a comprehensive plan for communicating project details and the goals of AI adoption. In addition, you should demonstrate how AI agents can augment rather than replace human roles. 

3. Leverage Independent Consultants

Independent ERP consultants can play a vital role in helping you select AI solutions that align with your technology infrastructure and business needs. 

Independent of vendor affiliation, these consultants can provide unbiased insights into the best AI tools for your unique needs. They can also provide project oversight when it comes to managing all human, operational, technical, and data aspects of your project. 

In addition, consultants can provide a roadmap for scaling AI deployments, ensuring that the initial integration is able to adapt as business needs evolve.

4. Prioritize Data Quality

AI agents rely on accurate data to generate reliable insights. Poor data quality can lead to flawed decision-making and ineffective automation.

Establishing a data governance framework ensures consistency in how data is collected, processed, and utilized across the organization. This means implementing data validation processes to catch errors before they enter AI systems, and regularly auditing data to identify inconsistencies. 

Learn More About AI Agent Use Cases in Business

There are many other business use cases for AI that we didn’t discuss. In fact, AI agents are being used across almost every functional area, from marketing to human resources. 

In all these scenarios, AI agents are changing the way businesses operate by streamlining routine processes, reducing manual workloads, and driving operational efficiency. 

Deploying AI strategically requires careful planning, robust change management, and a focus on data governance. We can help. Contact our business software consultants to learn about AI-powered ERP systems and AI tools in general.