Your guide to understanding how AI fits into customer support

If you’re in any way responsible for the behind-the-scenes systems that underpin frontline customer support, you’re probably getting a lot of questions about AI.

Your boss (and maybe even your boss’s boss) wants to know how you plan on using AI to stay relevant. SDRs from this and that company are emailing you sensational claims about a new AI product every other day, asking when you’re free for a call. Your colleagues are beginning to wonder how AI will affect their jobs.

If keeping up with the demands of modern customer support teams didn’t have you juggling enough, you’re now expected to tap dance through the rapidly shifting landscape of AI-powered support tools.

Don’t stress, we’ve got you covered. In this guide, you’ll get the full rundown on how AI can superpower customer support operations and, just as importantly, where it falls short. By the time you reach the bottom of this page, you’ll be ready to field the most mission-critical AI-related questions that come your way with confidence.

First, let’s start by making sure you understand the latest generation of AI models and why they’ve got the customer support technology space so excited about the potential of AI.

A tale of two AIs

Generative AI models are the shiny new breakthrough in AI tech, making waves with their ability to churn out human-like text. They’re shaking up customer support by tackling those tricky, long-standing challenges head-on.

Unlike their older, more predictable cousins, generative AI models can answer complex questions, give personalized and context-relevant responses, and keep getting better with every interaction.

Before generative AI, we had discriminative AI, which was more about sorting and categorizing. Imagine a diligent librarian who can tell if a book belongs in the “billing” or “technical issues” section. Helpful? Sure. But ask it to explain a confusing bill or predict your next question and it’s out of its depth.

That’s why generative AI is such a big deal. This new tech doesn’t just sort — it creates. It can take a billing issue and not only recognize it but also craft a detailed, personalized response, explain the bill, and anticipate follow-up questions. Pretty cool, right?

Not so fast. Generative AI isn’t the simple solution to all your operational problems. These models need tons of data and serious computational horsepower. Plus, they can sometimes spit out incorrect or downright weird answers, especially when faced with ambiguous or unfamiliar data.

The takeaway? Generative AI is a game-changer for customer support, but it’s not a magic wand. To truly benefit, you need to know what it excels at and where it could use a little help.

Things AI is really good at

AI is like having a superpowered assistant that can sift through mountains of data to find patterns and insights that would take humans forever to spot. It’s a game-changer for support teams, boosting the capabilities of human agents in several key areas.

But let’s not forget, no matter how advanced AI gets, it still needs human oversight to keep it on track with your business goals and quality standards.

Here’s where you can expect AI to shine in the context of your customer support operations.

Search

One of AI’s biggest strengths is search functionality. Remember when Google’s machine learning made web searches super relevant? Now, with generative AI, you can ask your internal docs questions and get spot-on answers, making information retrieval faster and easier than ever.

Drafting

AI is a lifesaver when it comes to drafting content. Sure, it might not nail it on the first try, but it speeds up the process, helps you dodge writer’s block, and gets you past the daunting blank page.

Analyzing

AI’s knack for analyzing data is a huge asset in customer support:

• Ticket classification: Automatically sorting tickets helps prioritize and route issues more effectively.
• Ticket QA: AI can review every single ticket for quality assurance, way beyond what human agents can typically manage.
• Sentiment analysis: AI offers a quick “temperature check” on customer interactions, helping you gauge satisfaction levels and spot potential issues early.

Repetitive Scenarios

For routine tasks like returns and password resets, AI handles these efficiently and consistently. This means your human agents can focus on more complex and high-value interactions.

Things AI could definitely be better at

Despite its impressive capabilities, AI isn’t without its flaws. Knowing what AI can’t do is just as important as knowing what it can, so you don’t end up with frustrated customers and a chaotic support operation.

“Garbage in, garbage out” problem

AI’s performance is only as good as the data it’s trained on. If your documentation is a mess, the quality of AI responses will take a hit. While AI can flag these data quality issues, you’ll need to roll up your sleeves and fix the root problems to see real improvement.

AI is not a person

Even the fanciest AI can’t replicate human intuition and emotional intelligence. Sure, you can program it to use your brand’s voice and follow certain guidelines, but it’s going to struggle with nuanced emotional interactions.

An upset customer needs empathy and understanding — qualities only a human agent can truly provide. Letting AI handle these delicate situations without proper oversight can lead to bigger headaches down the line.

Complex scenarios

AI is great with patterns, but throw it into a complex, non-routine situation, and it’s like a deer in headlights. Expecting AI to navigate these tricky scenarios can leave your customers feeling frustrated and dissatisfied.

Emotional scenarios

While AI can do a decent job at gauging customer sentiment, it lacks the finesse to adjust its approach based on emotional cues. An artificially cheerful bot dealing with an irate customer? Not a good look. It can make a bad situation worse instead of resolving it.

Logical thinking

Generative AI works by predicting the next word, not by engaging in logical thinking. Even with all the relevant data, it can flounder without examples to draw from, leading to some pretty incoherent responses.

Data freshness

AI models are only as current as the data they’re trained on. If your data is outdated, your AI's responses will be too. Regularly updating your datasets is crucial to ensure your AI remains relevant and accurate. Stale data leads to stale answers, which isn’t going to impress anyone.

Understanding these limitations helps you use AI wisely, enhancing your customer support operations without expecting it to be a miracle worker.

Knowledge is power

AI is revolutionizing customer support with its ability to handle routine tasks, analyze vast amounts of data, and assist with drafting and search. However, it's crucial to understand its limitations. AI needs quality data, human oversight, and regular updates to perform at its best.

By leveraging AI's strengths and acknowledging where it falls short, you can enhance your support operations and keep your customers happy. So, take the leap into AI-powered support, but do it with your eyes wide open.

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