5 Ways an AI Implementation Can Go Wrong
Implementing AI in customer service is full of potential, but it's not without its challenges. While many organizations rush to integrate the latest technologies, they often overlook details that can lead to a less-than-successful implementation. Here are five common ways AI implementations can go wrong, and how to avoid them.
1. Not Understanding Your Customer
One of the biggest missteps companies make is not fully understanding why customers are reaching out and which issues should be handled by AI. AI isn't a magic wand that will handle all customer inquiries perfectly. Instead, you need to be deliberate about which issues are right for automation.
To get this right, start by developing an experience taxonomy to categorize customer contact reasons. Once you have this taxonomy, bucket each topic based on its volume and suitability for AI handling. Topics that have high contact volume and are relatively simple for an AI to handle should be prioritized. This is not only crucial for chatbot implementations but also for internal AI assistants and routing AIs. Understanding your contact reasons is the first step to ensuring AI success.
For more details on how to create an experience taxonomy, click here.
2. Neglecting Content Quality
AI is only as good as the information it has access to. Another common issue is failing to build out quality content. Especially for the topics you've identified as prime for AI automation, content needs to be up-to-date, accurate, and comprehensive.
Think of your AI as a student: if you feed it incomplete or outdated information, it will produce subpar results. Ensure that content is being consistently reviewed and updated, particularly the help center articles, knowledge bases, and internal guides that the AI draws from. The better your source content, the better your AI will perform.
3. MISSING Integrations
Modern AI is capable of taking actions for customers, but only if it has access to the necessary integrations and databases. One major obstacle to effective AI implementation is a disjointed tech stack that makes accessing relevant data a nightmare. The more fragmented your tech stack is, the more difficult it will be for the AI to perform well.
For topics that you are targeting for AI, it's essential to create a data and integration plan. Ensure the AI has seamless access to customer information and can take the actions needed to resolve issues efficiently. This means simplifying your tech stack and integrating key tools wherever possible, allowing the AI to draw on a centralized repository of knowledge.
4. Not Preparing Employees for the Shift
AI will eventually take over routine inquiries, leaving human agents to handle the more complex and emotionally charged customer issues. This shift requires proper training, coaching, and support, areas where many companies fall short.
Your agents need to be equipped with a new set of skills to meet customer expectations for complex cases. Beyond training, these skills need to be reinforced through coaching to ensure agents are prepared to provide high-quality service. Creating a culture that prioritizes coaching is crucial to help agents grow into their evolving roles.
Also, leadership needs to have a clear change management plan to support agents through this transition. Many agents will be worried about losing their jobs, so transparent communication is essential. Address their concerns empathetically, emphasizing how AI is meant to support their roles rather than replace them entirely. If staff reductions are in the plan, outline an empathetic and fair plan to handle this difficult decision.
5. Ignoring Human-Centered Design Principles
The last, more subtle mistake companies make is neglecting human-centered design when designing AI-driven customer journeys. The best digital customer experiences are carefully crafted with product design principles, and service experiences should be no different.
Use human-centered design practices like customer interviews, journey mapping, and prototyping to optimize how customers will interact with your AI, find answers, and escalate issues if necessary. Launching the AI is only the first step; continuous optimization using these design principles is important for success. A thoughtful, empathetic approach ensures that both the customer and employee experience are seamless and satisfying.
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