Intro to implementing customer support AI assistants

Learn the difference between AI agents, copilots and analysts - and the benefits and challenges to be aware of.

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Implementing AI in customer support

In this series of tutorials, you’re going to learn how to implement customer support AI assistants into your customer service workflows.

Introducing AI into customer service workflows can drastically reduce response times, elevate human agents to focus on strategic work, provide more accurate answers, efficiently categorize and route issues, and supercharge many more support workflows.

If you haven’t already gone through Strategies for AI Customer Support Systems, make sure to read through those tutorials first to understand the broader context of this space. You’ll walk away with a clearly defined implementation strategy, a better sense of your team’s readiness, and a comprehensive understanding of the benefits and challenges of deploying customer support AI assistants, which will make this series of tutorials even more impactful.

For these tutorials, you’ll need:

Tutorials:

  1. Overview, benefits, and challenges of implementing customer support AI assistants
  2. Setting up a customer support AI copilot
  3. Document and knowledge management
  4. Developing an AI analyst to automate insights
  5. AI agents and customer support automation workflows

Before we get started building, we’re going to provide a brief overview of what customer support AI assistants are, how you can think of them, and the various benefits and challenges in deploying them into your customer support workflows.

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Overview of customer support AI assistants

There are three main types of customer support AI assistants we will be covering in this set of tutorials, and broadly speaking, we see these as the three clearest implementation buckets for AI assistants in this space. This includes AI Agents, AI Copilots, and AI Analysts.

  1. AI Agents provide instant and accurate answers to customers directly in chat and other channel interfaces. These tools act as the first line of support triage, directly interfacing with customers to answer inquiries, before looping in human agents.
  2. AI Copilots are support tools for human agents. Agents can use these tools to query knowledge sources, internal documentation, and public-facing data to get quick answers to customer queries, generate accurate responses, and provide advice across long context windows.
  3. AI Analysts are tools for customer support managers and leaders. These tools let you mine your support tickets, conversations, and responses to get back insights like query volume by category, support ticket sentiment, average response times, and more vital business insights.

All three of these customer support AI assistants sit on top of your knowledge bases of internal and external facing documentation, previous customer interactions, and custom instructions (e.g. brand voice, routing logic, etc.)

💡 Tip: In this series of tutorials, we will be covering all three AI assistant types, teaching you how to build your own AI Copilots and AI Analysts with OpenAI’s Custom GPTs, and providing you recommendations on how to take these internal implementations a step further, deploying AI Agents in customer-facing chat experiences with a tool like Intercom.

Benefits and challenges of implementing customer support AI assistants

The benefits of implementing customer support AI assistants are swift and broadly sweeping, which is why this technology has been revolutionizing this space, even before the LLM boom.

Benefits

  • Reduce response times: You can have 24/7 availability, and greatly reduce human agent response times by making information access and response generation much faster.
  • Provide more accurate answers: You can iteratively improve the frequency and quality of your knowledge source maintenance. Gone are the days of stale and out-of-date documentation, ensuring more accurate answers to customer queries.
  • Save time: AI assistants can draft initial responses in your brand voice and from your knowledge sources, as well as provide updates directly to your knowledge sources, saving significant time for both agents and managers.
  • Elevate human agents’ focus: With more time freed up from searching for answers, drafting responses, and updating documentation, human agents can focus on more strategic work across your customer support stack.
  • Efficiently route issues: Not only can AI help with knowledge retrieval and generation, but it can smartly categorize and route queries to the right teams, automating several workflows at once.
  • Get better insights: Customer support AI tools can crunch the numbers across an archive of customer conversations, providing insights across response times, CSAT, sentiment, and other valuable metrics instantly.

It isn’t all rainbows and sunshine though. You can’t just set it and forget it when it comes to implementing AI into your customer support workflows. Deploying customer support AI assistants comes with its own specific set of challenges. It takes diligent planning and maintenance to ensure high-quality support and legitimate increased efficiency.

Challenges

  • Knowledge management: Integrating an AI assistant into customer support requires the creation and continuous update of a comprehensive knowledge base. Ensuring the AI has access to accurate, up-to-date information is crucial but can be challenging due to the dynamic nature of many businesses.
  • Personalization: While AI can handle a high volume of queries, providing personalized responses that reflect understanding and empathy towards individual customer situations can be difficult. Tailoring interactions to effectively meet varied customer preferences and histories is a significant challenge.
  • Hallucinations: AI models, including GPTs, sometimes generate incorrect or misleading information—a phenomenon known as "hallucination." This can erode trust with agents and customers, potentially complicating customer issues rather than resolving them.
  • Complexity: The issues customers face can be highly complex and interconnected. AI can struggle with understanding the nuances and context necessary to provide accurate solutions, especially for cases that deviate from more common or straightforward inquiries.
  • Maintenance: Deploying an AI assistant is not a one-time task. Continuous monitoring and maintenance are required to ensure AI tools perform well. This involves regular training updates, handling unexpected AI behavior, and adjusting to new customer service protocols or information, which can be resource-intensive.

Without further ado, let’s head to the second tutorial of this series, where you’ll learn how to set up the foundational components of a customer support AI copilot.

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