Having grasped AI agents' transformative potential, the next step is moving from understanding to action. To prove the value of this technology quickly and set the stage for scaling, it's crucial to start with the right use case and build a solid foundation for your AI Agent operating model. This blog post will help you navigate these critical first steps, ensuring your organization is positioned to leverage AI Agents effectively and competitively.
If you have a background in automation, one promising area to explore is automating previously impossible tasks. These typically involve complex, unstructured documents or flexible workflows that are not easily codified and address excessive error-handling rules. Focus on tasks that occur before or after traditional RPA workflows, which may not have been previously addressable.
Selecting the Right Use Case for AI Agents
The success of your AI Agent journey hinges on choosing the right initial use case—a clearly defined, end-to-end process. Here’s how to approach this:
Focus on High-Impact Areas:
Begin by identifying critical processes in your operations where inefficiencies or errors have a significant impact. Look for repetitive, data-intensive tasks that require decision-making—these are ideal candidates for AI Agents.
Evaluate Feasibility:
Not all processes are suitable for immediate AI Agent deployment. Assess the technical feasibility, data availability, and process complexity. Start with use cases where AI Agents can deliver quick wins, demonstrating tangible value.
Align with Strategic Goals:
Ensure that the selected use case aligns with broader business objectives and strategic goals like improving customer satisfaction, reducing operational costs, or accelerating time-to-market.
The success of your AI Agent journey hinges on choosing the right initial use case—a clearly defined, end-to-end process. Here’s how to approach this:Strategic use cases often require AI Agents to access data across multiple systems or apps. For example, tasks that involve cross-referencing multiple systems, such as CRM, finance and customer support, are prime candidates for automation. Tasks that are limited to a single app, like a CRM system, are less likely to benefit from AI Agent automation—these systems typically have built-in automation tools.
One way to look at potential use cases is to evaluate through desired outcomes:
Saving Money:
Finding efficiencies through tasks like invoice reconciliation, employee onboarding, or AP/AR processing.
Risk Mitigation:
Compliance and fraud detection, such as anti-money laundering or insurance claim validation.
Revenue Growth:
AI Agents help businesses respond faster to market changes, identify upsell opportunities, and innovate new products and services.
Contact us to help you find the ideal use case for AI Agents in your organization.
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