AI is one of the most exciting new developments in customer service. But how does customer service AI work and what it makes possible? In this piece, we’ll offer the context you need to make good decisions about this groundbreaking technology. Let’s dive in!
What is AI in Customer Service?
AI in customer service means deploying innovative technology–generative AI, custom predictive models, etc.–to foster support interactions that are quick, effective, and tailored to the individual needs of your customers. When organizations utilize AI-based tools, they can automate processes, optimize self-service options, and support their agents, all of which lead to significant time and cost savings.
What are the Benefits of Using AI in Customer Service?
There are myriad advantages to using customer support AI, including (but not limited to):
1. AI will automate routine work.
As with so many jobs, a lot of what customer service agents do day-to-day is fairly repetitive, as little imagination is required to do things like order tracking, balance checking, or password resetting. These days, customers are obsessed with quick and convenient service, so utilizing customer service AI to automate and speed up routine tasks benefits both customers (who want answers now) and agents (who don’t have to do the same thing all the time).
2. Scalability and Cost Savings
A related point concerns the fact that AI automating routine tasks and supporting agents with data-driven insights allows businesses to scale customer service without a proportional increase in costs. This lowers operating expenses, increases capacity to handle peak volumes, and frees-up human agents for high-value tasks.
3. Customer service AI can help make ‘smart’ documentation.
Many customers will begin by checking out your documentation to see if they can’t solve a problem first, so it’s important for yours to be top-notch. You can use large language models (LLMs) to draft or update documentation, of course, or you can go a step further. Modern AI agents can use documentation to answer questions directly, and can also guide customers to use the documentation themselves.
4. Customer Support AI Supercharges Chat.
Customer service leaders have long recognized that AI-powered agents for chat support are a cost-effective (and often preferred) alternative to traditional phone or email support. AI agents for chat are rapidly becoming a mainstay for contact centers because they can deliver personalized, round-the-clock support across any channel while seamlessly integrating with other tools in the CX, eCommerce, and marketing tech stacks.
5. AI contributes to a better customer experience.
All of the above ultimately adds up to a much better customer experience. When customers can immediately get their questions answered (whether at 2 p.m. or 2 a.m.), with details relevant to their specific situation, or even in their native language, that’s going to leave an impression!
6. Use customer service AI to learn about your customers.
Before moving on, let’s discuss the amazing capacity customer service AI has to help you discover trends in your customers preferences, predict customer needs, identify patterns, and proactively address issues before they become major problems. This can significantly speed up your response time, reduce churn, improve resource allocation, and establish your reputation for anticipating customer desires.
Where to Get Started with AI in Customer Service
If you’re looking to get started with customer support AI, this section will contain some pointers on where to begin.
Deploy AI Agents for Maximum Efficiency
The next frontier in customer service AI is ‘agents,’ which have evolved from the AI chatbot and are capable of much more flexible and open-ended behavior. Whereas a baseline large language model can generate a wide variety of outputs, agents are built to be able to pull information, hit APIs, and complete various tasks from start to finish.
Use Customer Service AI to Guide Humans and Optimize your Business Processes
AI-powered tools in customer service are changing how support teams operate by enhancing the productivity of human agents, as well as the efficiency of workflows. By providing agents with response suggestions specifically tailored to each customer’s unique needs, for instance, these tools enable agents to burn through issues more swiftly and confidently. This can be especially helpful during onboarding, where agents benefit a great deal from additional guidance as they learn the ropes.
More broadly, AI can automate many aspects of customer service and thereby streamline the support process. To take just one example, intelligent, AI-powered ticket routing can use sentiment analysis and customer intent to direct inquiries to the agent best able to resolve them.
As mentioned above, AI can also participate more directly by suggesting changes to responses and summarizing long conversations, all of which saves time. In addition to speeding up the overall support process, in other words, these optimizations make agents more efficient.
Use Voice AI for Customer Calls
Another exciting development is the rise of ‘multimodal models’ able to adroitly carry on voice-based interactions. For a long time now there have been very simple models able to generate speech, but they were tinny and artificial. No longer.
Today, these voice AI applications can quickly answer questions, are available 24/7, and are almost infinitely scalable. They have the added advantage of being able to translate between different natural languages on the fly.
Effectively use AI in Emails
In customer service, email automation involves leveraging technologies such as generative AI to automate and customize email interactions. This enhances your agents’ response speeds, increases customer satisfaction, and improves overall business efficiency. It enables businesses to handle a large number of inquiries while maintaining a high quality of customer interactions.
Given the email channel’s enduring importance, this is a prime spot to be looking at deploying AI.
Make the Most Out of Digital Channels
For a while now, people have been moving to communicating over digital channels like Facebook messenger, WhatsApp, and Apple Messages for Business, to name a few.
As with email, AI can help you automate and personalize the communications you have with customers over these digital channels, fully leveraging rich messaging and text messaging to meet your customers where they’re at.
AI can Transform your E-Commerce Operations
When you integrate AI with backend systems – like CRM or e-commerce platforms – it becomes easier to enhance upsells and cross-sells during customer support sessions (an AI agent might suggest products tailored to a customer’s previous purchases, items currently in their shopping cart, or aspects of the current conversation, for instance).
Moreover, AI can proactively deliver notifications featuring customized messages based on user activity and historical interactions, which can also increase sales and conversion rates. All of this allows you to boost profits while helping customers–everyone wins!
Things to Consider When Using AI in Customer Service
Now that we’ve covered some necessary ground about what customer support AI is and why it’s awesome, let’s talk about a few things you should be aware of when weighing different solutions and deciding on how to proceed.
Augmenting Human Agents
Against the backdrop of concerns over technological unemployment, it’s worth stressing that generative AI, AI agents, and everything else we’ve discussed are ways to supplement your human workforce.
So far, the evidence from studies done on the adoption of generative AI in contact centers have demonstrated unalloyed benefits for everyone involved, including both senior and junior agents. We believe that for a long time yet, the human touch will be a requirement for running a good contact center operation.
CX Expertise
Though a major benefit of customer service AI service is its proficiency in accurately grasping customer inquiries and requirements, obviously, not all AI systems are equally adept at this. It’s crucial to choose AI specifically trained on customer experience (CX) dialogues. It’s possible to do this yourself or fine-tune an existing model, but this will prove as expensive as it is time-intensive.
When selecting a partner for AI implementation, ensure they are not just experts in AI technology, but also have deep knowledge of and experience in the customer service and CX domains.
Time to Value
When integrating AI into your customer experience (CX) strategy, adopt a “crawl, walk, run” approach. This method not only clarifies your direction but also allows you to quickly realize value by first applying AI to high-leverage, low-risk repetitive tasks, before tackling more complex challenges that require deeper integration and more resources. Choosing the right partner is an important part of finding a strategy that is effective and will enable you to move swiftly.
Channel Enablement
These days, there’s a big focus on cultivating ‘omnichannel’ support, and it’s not hard to see why. There are tons of different channels, many boasting billions of users each. From email automation for customer service and Voice AI to digital business messaging channels, you need to think through which customer communication channels you’ll apply AI to first. You might eventually want to have AI integrated into all of them, but it’s best to start with a few that are especially important to your business, master them, and branch out from there.
Security and Privacy
Data security and customer privacy have always been important, but as breaches and ransomware attacks have grown in scope and power, people have become much more concerned with these issues.
That’s why LLM security and privacy are so important. You should look for a platform that prioritizes transparency in their AI systems—meaning there is clear documentation of these systems’ purpose, capabilities, and limitations. Ideally, you’d also want the ability to view and customize AI behaviors, so you can tweak it to work well in your particular context.
Then, you want to work with a vendor that is as committed to high ethical standards and the protection of user privacy as you are; this means, at minimum, only collecting the data necessary to facilitate conversations.
Finally, there are the ‘nuts and bolts’ to look out for. Your preferred platform should have strong encryption to protect all data (both in transit and at rest), regular vulnerability scans, and penetration testing safeguard against cyber threats.
Observability
Related to the transparency point discussed above, there’s also the issue of LLM observability. When deploying Large Language Models (LLMs) into applications, it’s crucial not to regard them as opaque “black boxes.” As your LLM deployment grows in complexity, it becomes all the more important to monitor, troubleshoot, and comprehend the LLM’s influence on your application.
There’s a lot to be said about this, but here are some basic insights you should bear in mind:
- Do what you can to incentivize users to participate in testing and refining the application.
- Try to simplify the process of exploring the application across a variety of contexts and scenarios.
- Be sure you transparently display how the model functions within your application, by elucidating decision-making pathways, system integrations, and validation of outputs. This makes it easier to model how it functions and catch any errors.
- Speaking of errors, put systems in place to actively detect and address deviations or mistakes.
- Display key performance metrics such as response times, token consumption, and error rates.
Brands that do this correctly will have the advantage of being established as genuine leaders, with everyone else relegated to status as followers. Large language models are going to become a clear differentiator for CX enterprises, but they can’t fulfill that promise if they’re seen as mysterious and inscrutable. Observability is the solution.
Risk Mitigation
You should look for a platform that adopts a thorough risk management strategy. A great way to do this is by setting up guardrails that operate both before and after an answer has been generated, ensuring that the AI sticks to delivering answers from verified sources.
Another thing to check is whether the platform is filtering both inbound and outbound messages, so as to block harmful content that might otherwise taint a reply. These precautions enable brands to implement AI solutions confidently, while also effectively managing concomitant risks.
AI Model Flexibility
Finally, in the interest of maintaining your ability to adapt, we suggest looking at a vendor that is model-agnostic, facilitating integration with a range of different AI offerings. Quiq’s AI Studio, for example, is compatible with leading-edge models like OpenAI’s GPT3.5 and GPT4, as well as Anthropic’s Claude models, in addition to supporting bespoke AI models. This is the kind of versatility you should be on the look out for.
What is the Future of AI in Customer Service?
This has been a high-level overview of the ways in which customer support AI can make you stand out in a crowded market. AI can help you automate routine tasks and free up agent time, personalize responses, gather insights about customers, and thoroughly optimize your internal procedures. However, you must also make sure your models are observable, your offering is flexible and dynamic, and you’re being careful with sensitive customer data.
For more context, check out our in-depth Guide to Evaluating AI for Customer Service Leaders.
