Key Takeaways

• To successfully implement AI agents, select use cases that go beyond rule-based tasks, involving decision-making, context awareness, and collaboration. 
• Start by reviewing RPA pipelines, analyzing upstream and downstream activities, and focusing on well-documented internal processes. 
• Proven use cases include customer service, invoice reconciliation, leads generation, recruitment, remittance matching, RFP bid management, and sales order intake. 
• Begin internally to balance risks and rewards, then scale to customer-facing tasks.
  

In the fast-evolving world of enterprise automation, AI agents are at the forefront of accelerating and transforming businesses everywhere. Unlike traditional RPA bots that follow rigid, rule-based workflows, AI agents embody an agentic AI approach. (See 8 Differences between AI Agents and RPA Bots for more comparison between AI agents and RPA bots.)

AI agents can reason, make decisions, collaborate with humans, and autonomously execute tasks to achieve defined goals. These advanced capabilities open the door to a new era of agentic automation, redefining how businesses manage processes and deliver value.

However, selecting the right use cases for AI agents is crucial for a successful implementation and achieving the desired business outcomes. After all, the use of AI agents comes with its own set of benefits as well as challenges. Choosing the right use cases therefore maximizes the impact of these AI agents and secures organizational buy-in for future scaling of your agentic workforce.

Here are a few practical strategies for uncovering opportunities to deploy AI agents within your enterprise.

1. Evaluate your Existing RPA Pipeline

If your organization already uses Robotic Process Automation (RPA), you have a treasure trove of data to start with. Review processes in your RPA pipeline to identify tasks that go beyond simple rule-based, repetitive processes. Processes that involve decision-making, context awareness, or collaboration are excellent candidates for AI agents. For instance, upgrading an RPA-driven invoice processing workflow to an agentic workflow could enable better exception handling and faster reconciliation.

Take invoice reconciliation for example.

• Task automation with RPA: Read structured data, perform 2/3-way matching, and post invoice into ERP
• Process automation with RPA & OCR/IDP: Retrieve submitted invoice, extract required data, perform 2/3-way matching, and post invoice into ERP
• Intelligent automation with AI agents: All of the above, including automatic validation of the extracted data, invoice/payment approval based on evolving company policy (i.e. not static rules), handling of rejected invoices, fraud detection, etc.

2. Analyze Upstream and Downstream Activities

Review your existing RPA processes, in particular the upstream and downstream activities, to identify opportunities to increase the automation scope with AI agents. For example, consider a sales order intake process automated by RPA. While the bot handles data entry, an AI agent could evaluate sales trends, flag anomalies, or suggest upselling opportunities, thereby increasing the value creation.

3. Focus on Well-Documented Processes

Business processes that are well-documented and have available Standard Operating Procedures (SOPs) are ideal use cases to train the AI agents on. SOPs provide the foundational knowledge needed for AI agents to reason and make decisions. A customer service AI agent, for example, can be trained to resolve queries using predefined escalation procedures while also learning from past interactions to improve over time.

4. Adopt an Inside-Out Approach

Focus on internal back-office processes first before looking at the client-facing, front-office tasks. Internal processes, such as invoice reconciliation, remittance matching, or sales order intake, are typically easier to manage and less exposed to variability. Once your agentic process automation strategy demonstrates success internally, expanding to customer-facing use cases becomes less daunting. Doing so will enable you to achieve a good balance between risks and rewards.

5. Leverage Proven Use Cases

Some applications of AI agents are already delivering demonstrable results across myriad industries. Begin with these proven use cases to minimize uncertainty and accelerate your path to success:

• A/R Collections Agent: Cut through lengthy payment cycles and get paid faster with an autonomous agent that streamlines your accounts receivables process
• Customer Service Agent: Automates customer interactions, resolves queries, and offers 24/7 support.
• Invoice Reconciliation Agent: Matches invoices with payments, identifies discrepancies, and ensures accurate record-keeping.
• Leads Generation Agent: Transform casual website visitors into engaged prospects, collecting valuable contact details while providing instant, helpful responses to their inquiries.
• Recruitment: Streamline your talent acquisition process, save time and resources, and find top talent faster with agentic AI
• Remittance Matching Agent: Matches incoming payments with outstanding invoices, improving cash flow management.
• RFP Bid Management: Streamline your proposal process, accelerate submissions, and win more deals with agentic AI
• Sales Order Intake Agent: Streamlines order processing, reduces errors, and integrates seamlessly with inventory systems.

The Future of Agentic AI in Enterprises

As AI technology continues to advance, the potential for agentic AI in enterprises is limitless. By automating processes that require reasoning, decision-making, and collaboration, AI agents are not just tools—they are strategic assets that redefine the concept of a workforce. And the journey begins with selecting the right use cases for your AI agents.