How to evaluate AI copilot tools for customer support teams without getting burned

The promise of AI copilots for customer support is compelling. We hear about significant reductions in average handle time (AHT), improvements in first contact resolution (FCR), and enhanced agent satisfaction. These tools can surface relevant knowledge, draft responses, summarize interactions, and automate routine tasks, fundamentally changing how our customer-facing teams operate.

However, the rapid proliferation of these tools also presents a substantial challenge: selecting the right solution without incurring significant costs, integration headaches, or ultimately, failing to deliver on the promised value. A haphazard approach can lead to wasted resources, agent frustration, and a damaged perception of AI's potential within the organization. This brief outlines a structured approach to evaluate AI copilot tools, minimizing risk and maximizing strategic impact.

01. Defining Your Problem and Goals

Before exploring any AI copilot, it is critical to precisely articulate the specific business problems you intend to solve. Vague objectives like "improve support" are insufficient; we need measurable pain points that can be directly addressed and quantified. This initial step grounds the entire evaluation process in tangible outcomes.

Consider key operational metrics currently underperforming. Are we struggling with high average handle time due to agents searching multiple systems for information? Is our first contact resolution rate low because agents lack real-time guidance? Are agent onboarding times lengthy, or is turnover high due to repetitive, high-stress tasks?

Once identified, these problems must be associated with specific, measurable goals for the AI copilot. For instance, a goal might be to "reduce AHT by 15% for tier 1 inquiries within six months post-implementation" or "increase FCR for product-related questions by 10%." These quantifiable targets will serve as benchmarks for evaluating prospective solutions and measuring success.

This foundational understanding ensures that any chosen technology aligns directly with strategic business objectives, preventing a solution in search of a problem. It also provides the necessary baseline data against which the copilot's eventual impact can be accurately assessed, justifying the investment.

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