Cost of inefficiencies vs. cost of AI agents

If you haven’t heard the term “AI agent” yet, you will. AI agents represent the next wave of digital transformation in distribution. But what are they? And how do they differ from other popular AI and automation technologies?

Let’s get one thing out of the way: AI agents aren’t robots waiting to step in and do your team’s work. The term “agent” comes from “agency,” which means the ability to do things with minimal human intervention.

They are autonomous, goal-driven systems that take care of repetitive tasks that might slow your business down:

• You tell an AI agent what to do;
• You train it on that task using examples and rules;
• Once it understands, it starts working; and
• Before finishing, the AI agent confirms the work was accurate.

On the surface, AI agents may not sound much different from other AI and automation tools out there today. But not all AI-driven solutions are created equal.

Recognizing the strengths and limitations of each type of AI or automation technology will ensure you find the right tool for your business and get the most from it.

Here are some ways AI agents differ:

They require less human intervention
Many automation tools require human intervention at decision points. AI agents, however, autonomously handle exceptions, adapt to new inputs and ensure processes run smoothly. For example, OCR-based automation may extract order data from an email but still require human verification before putting it in the ERP. AI agents understand the context, check inventory, apply business rules and complete the order without any human touch.

They are not bound by rules
Traditional automation tools operate on more rigid, rule-based structures, which means that they may struggle with unstructured data or unexpected variability. But AI agents can interpret complex, unstructured data from various sources (for example: email, PDFs, voice); cross-check to identify inconsistencies; adapt to new scenarios instead of failing when rules aren’t followed.

They scale more easily
Because AI agents learn and improve over time, distributors can grow workloads without adding people. For example, instead of hiring more reps to process orders, distributors can deploy AI agents to handle RFPs, and validate and process orders. Traditional automation does not scale as well without additional customization.

They can make decisions, not just execute tasks
Traditional automation tools follow strict workflows. AI agents can evaluate options, weigh trade-offs and take action based on real-time conditions.

They can work across systems
Traditional automation tools often operate within a single platform such as ERP, CRM or your WMS. They require integrations. But AI agents can move between platforms. For example, an AI agent processing an order could access pricing data in a CRM, check stock levels in an ERP and coordinate shipping in a WMS.

They are proactive
Traditional automation reacts to predefined inputs. AI agents can anticipate issues and act before problems occur. For example, an AI agent that is monitoring inventory could predict stockouts based on order patterns and suggest alternatives.

Are AI agents right for your business?

When you are evaluating AI and automation technologies, you need to determine whether they align with your needs and business goals. AI agents are designed to streamline complex, high-volume tasks, but they aren’t one-size-fits-all. Consider the following questions:

• What repetitive, high-volume tasks are slowing down operations?
• What is your error rate in frequent, high-volume tasks such as order entry or invoice matching?
• How complex and rigid are your current automation efforts? Are your existing solutions struggling to handle variability or exceptions?
• Are you dealing with highly variable data, such as orders arriving through multiple channels in multiple formats?
• Does your current technology require frequent human intervention for manual data entry or decision-making?

And, most importantly, what’s the cost vs. ROI of the technology you are evaluating? If inefficiencies are costing you more than the perceived benefit of AI agents, it may be worth serious consideration. Look at both quantitative and qualitative factors, such as:

• Faster order processing, which leads to increased revenue and higher customer satisfaction.
• Fewer errors, which lower costs from returns, incorrect shipments and chargebacks.
• Improved rep efficiency, which means they can spend more time strengthening customer relationships instead of admin tasks.
• Scalability without increasing headcount, giving you the ability to grow without adding excessive costs.

The right choice depends on your business processes, existing automation infrastructure and long-term growth strategy.