There is a growing narrative that AI in customer service is being trained to “say no,” prioritizing containment and deflection to avoid passing customers over to human agents rather than solving problems to their satisfaction.
Enterprises have long measured the success of automation in their customer service operations through “containment” or “deflection” rates that focus on how many customer interactions were handled by an AI chatbot or agent rather than escalated to a person.
But, as Steve Blood, VP for Market Intelligence at Five9, told CX Today, that mindset defines AI success through outdated metrics, creating distorted incentives and fragmented customer experiences.
“Ultimately, it's because it's the easiest thing to measure. If I can say I can deflect or contain a hundred thousand inquiries a week, and each one costs five euros, that adds up to half a million a week, 26 million a year. It is simple mathematics and people buy it, unfortunately.”
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Why “70% Deflection” Is a Failed Metric
Vendors and enterprises routinely boast that their AI-driven service operations are achieving 70-80 percent containment, which means that most customer inquiries never reach a human agent. The savings are easy to calculate, which makes containment attractive to executives trying to justify their investments in automation.
“The main issue is that there are still too many discrete siloed departments touching the customer,” Blood said.
Each department focuses on protecting its project and uses the containment number to justify its existence. “It’s just a departmental metric, not a view of the customer. That's how they build their business case, so they don't even think about what it means for the customer,” Blood added.
Those numbers often reveal little about whether the customer’s problems were solved.
Instead, Five9 argues that automation can significantly improve the customer experience when it is designed around outcomes such as first-contact resolution, customer satisfaction, and customer effort, rather than raw containment rates and operational savings.
“Metrics and core outcomes are actually related,” Blood said. “The outcome is a real-world result, something that's achievable, and then the metric is going to be the business’s way of measuring whether the outcome was achieved.”
“You have to start with, why are you doing this? You want to save money? No, you're doing this to improve customer experience. If you get it right, you will save money.”
An example of a good customer outcome would be if 70 percent of interactions were resolved to the customer’s satisfaction without repeat contact or escalation, Blood said. “That's very different from 70% of calls were deflected.”
From a customer's perspective, the problem is solved, which the business can determine from the resolution rate metric. To claim real containment, the business would need to collect enough data to understand whether it has truly resolved the customer’s problem, Blood explained.
That could be picked up by a self-service agent asking the customer whether their problem has been solved to their satisfaction. First contact resolution also comes into play here as a more relevant metric than containment, Blood said.
“But again, we need to know, has the customer switched channels? Did they start on the website and find they couldn't deal with the issue and then moved to the phone channel? If you're measuring FCR across these things separately, then you're never going to know if you truly achieve first contact resolution.”
Without full visibility into the customer journey, departments end up measuring success in isolation, whereas in reality customers frequently switch channels after failed interactions.
A chatbot may technically “contain” an inquiry, but the customer goes on to phone support, email the company, or post publicly on social media. So while the website team that created the bot might boast of 80 percent containment, the contact center team gets the follow-up call from the disgruntled customer, and the connection between the two interactions is not captured.
If the customer at this stage is not satisfied with the service response, they are likely to turn to social media platforms to vent their frustration, which can result in reputational damage to the brand.
Customer Satisfaction Score (CSAT) is a useful metric in determining how the customer feels about the interaction. While CX leaders often use CSAT to identify employee soft skills, they could extend it to AI agents to track whether a change to a prompt for a large language model (LLM) affects customer satisfaction positively or negatively. However, businesses should avoid overreliance on CSAT, Blood said.
“The other desirable customer outcome is ‘that was easy.’ That is the customer effort score. And that doesn't get used enough. We use CSAT and NPS way too much.”
Gartner found that 96 percent of customers who have high effort experiences are more likely to become disloyal, which has a direct effect on revenue from repeat repurchases, Blood noted.
Reducing customer frustration matters more than trying to create exceptional or “delightful” moments. “It's actually not necessary and neither is it profitable,” Blood said.