AI agents dominate the conversation right now, and most businesses have already seen vendors promoting AI-enhanced versions of their software. But these upgrades typically focus on making each system more efficient on its own. The real work of running the process, connecting systems, reconciling information, and coordinating steps, still depends on people.
What’s often missing from the discussion is how to use AI to run the process itself rather than improving each platform in isolation. An AI agent works across the software you already have, using each system as input to manage coordination and streamline the process. This shifts the burden away from people, allowing teams to focus on the parts of the process that require expertise, negotiation, or judgment.
The natural question, then, is how to put this approach into practice.
The best place to begin is with a single process that spans multiple systems and involves enough variability that traditional automation struggles. These are the processes where people spend their time pulling reports from one system, cross-checking data in another, reconciling numbers, coordinating handoffs, and resolving exceptions. An AI agent can be deployed to handle the coordination, cutting down delays, reducing handoffs, and giving your teams the capacity to move faster on the decisions that matter.
Consider contract review and negotiation preparation. Legal and procurement teams often manage contracts across document management systems, compliance databases, and financial platforms. Reviewing terms, checking for policy alignment, verifying supplier data, and preparing negotiation points takes time and coordination. An AI agent can handle the initial review. It pulls contract details, flags deviations from standard terms, confirms financial thresholds, and compiles key issues. That allows legal and procurement teams to focus their time on negotiation strategy, risk evaluation, and approvals.
Or consider managing orders. Sales, operations, and finance teams typically rely on multiple systems to process and fulfill orders. They spend time confirming product availability, checking credit terms, reconciling pricing, coordinating shipment logistics, and updating customer records, all while managing exceptions and changes. An AI agent moves between these systems, gathers details, flags potential issues such as stock shortages or payment discrepancies, and keeps orders progressing. Teams can then focus on complex cases, customer relationships, and decisions that require human judgment.
The important step is to pick one process, design the AI agent to handle the coordination across systems, and remove the manual friction. Once that first agent is in place, expanding to the next process becomes far easier.
Skip to content
Skip to footer
