The technology landscape for customer experience has advanced rapidly over the last few years. This evolution, combined with customer expectations of faster, more personalized service means that the appetite to augment customer-centric support agents with AI tools is growing quickly.
As you’d expect, this means CX leadership teams are talking a lot about their approach to AI. How can AI best support what they’re already doing? What are the challenges and benefits? How will their customers feel about it?
Finding the right AI software is difficult enough, but many customer support leaders have a different, more foundational question: Is it even worth it?
Let’s explore some of the most important AI customer support challenges and why they are worth taking on.
The challenges of implementing AI in support
With most emerging technologies, challenges often arise in the purchasing, implementing, and maintenance phases when getting started. AI tools in customer support are no different. As you start your buying journey for AI support tools, here are five things to be aware of that may be seen as roadblocks.
1. Finding the right use cases
Identifying the right use cases for AI in customer support can be daunting.
Not every customer query or issue is suitable for AI intervention, and understanding where AI can add the most value requires a deep dive into customer interactions and pain points. In addition, many tools in the market handle similar concepts differently. For example, two generative AI chatbots may be trained differently, allow for different settings to be configured, or have underlying AI models better suited for one industry over another.
When starting your buying journey, it’s critical to recognize what is most important to your customers and your business so that you are solving the right problems in the most impactful ways. The big challenge lies in distinguishing where AI can enhance customer service efficiency without sacrificing the quality of service.
If AI attempts to handle queries it isn’t equipped for, it can result in inaccurate responses and diminished customer satisfaction. Conversely, not leveraging AI where it could be effective means missing out on efficiency gains and cost savings. As a customer support leader, you need to understand your use cases in depth to ensure you are going to market for the right solution.
Misapplying AI can lead to frustrated customers and wasted resources.
2. Navigating the AI hype cycle
Navigating the hype cycle surrounding AI technology can be tricky for several reasons:
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There is often a gap between the expectations set by marketing and the reality of what the technology can deliver.
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There are new solutions entering the market every week.
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The technology seems “magic” when demonstrated, but it isn’t obvious how it will behave in your environment and with your data.
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The market is ripe for faulty business plans or even vaporware which could put your business at risk.
A 2023 Gartner study showed that “more than half of the innovation profiles included in the hype cycle fall into what Gartner describes as the ‘Trough of Disillusionment’ section” — meaning many of these new AI support tools won’t be fully mature for two to five years.
The fallout from unmet expectations can be damaging, both in terms of wasted investment and the potential erosion of trust in AI initiatives. Organizations may become reluctant to pursue further AI projects, slowing innovation and the adoption of beneficial technologies.
Choosing a reliable vendor and a product that has proven technology is a major AI customer support challenge. With a hectic market, media hyperbole, and the potential customer and internal impacts tied to the decision, overcoming this challenge with a reasoned set of questions to potential vendors and a robust testing plan is paramount.
3. Customer perception of AI customer service varies
Customer perception of AI in support interactions varies widely.
Some customers appreciate the efficiency and 24/7 availability while others may feel frustrated by the lack of human touch and the inability of AI to understand certain contexts. PwC found that “71% of Americans would rather interact with a human than a chatbot or some other automated process.”
But a lot of this stems from the fact that automated solutions have historically been pretty bad, and customers are now conditioned to view automation negatively. Examples include:
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Bots repeating robotic responses that either do not answer the problem at all or put the customer into a loop.
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Voice recognition not understanding intentions or different accents.
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Having no way to get to a human agent.
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Using AI to manipulate or sell to us versus make our lives easier.
Overreliance on AI can alienate customers who prefer human interaction, whereas underutilizing AI can lead to inefficiencies and higher operational costs. It’s a balancing act to ensure that when you deploy automation, your customers are accepting of it.
Starting with a smaller set of use cases or a gradual rollout which you test thoroughly at each launch and maintain consistently will help to overcome this challenge.
4. Employee concerns around job insecurity
Integrating AI into customer support can lead to concerns among employees about job security and changes to their roles.
These concerns can lead to resistance to AI adoption, low morale, and reduced productivity. Employees may be less willing to engage with and support AI initiatives if they feel their jobs are at risk. That’s a reasonable apprehension, and it’s one of the reasons Help Scout’s AI features focus on making support agents better, not replacing them.
When planning an AI strategy, the human impact and messaging must be taken into account and clearly articulated to avoid this challenge. Ensuring that employees see AI as a tool to assist them rather than replace them is crucial for smooth adoption and maximizing the benefits of AI.
