November 2024

10 Examples of AI Customer Service Solutions That Could Change Your Business

Posted on November 22, 2024December 3, 2024 Michael Hartsog

From full-bore automation to multilingual speech and more, AI is already changing the way customer service works. We’ve written a lot about this over the past year, drawing on both industry statistics and our insider knowledge.

But it always helps to ground these claims with specific, real-world examples, which will be our focus today. In our judgment, there are ten domains where AI customer service helps businesses better serve their customers and improve their own profits, all of which we’ll walk through below.

Let’s get started!

What are the Ways AI can be used for Customer Service?

The following ten examples of AI in customer service are especially compelling. Read through them to see how you might use AI in your enterprise.

1. Instant, Multilingual, 24/7 Replies

Your customer service agents might require downtime, but an AI agent can operate round-the-clock. Capable of handling inquiries and resolving issues at any time of the day or night, AI agents ensure that matters are either resolved or neatly handed over to your human agents the next morning. This not only means that service is continuous, it also reduces the workload waiting for your agents while they’re getting their coffee.

Additionally, with the evolution of machine translation, it’s now possible to automatically translate between most languages, which is essential for serving a diverse, global customer base. Offering support in a customer’s native language not only improves key performance metrics like average handling time and resolution rates, but also significantly enhances customer satisfaction, demonstrating your commitment to providing a comforting, personalized experience (all this without the need to open up contact centers in each new region you operate in).

2. Automatically Sorting Emails and Tickets

Properly resolving an issue requires many steps, but one big one that must be handled up front is sorting and prioritizing incoming emails or tickets. A person could easily spend most of their day combing through communications with customers and deciding what should be handled first.

But, with AI, this is no longer required! Clever email automation can parse these incoming messages and route them to the relevant agents, boosting retention and improving your bottom line. Even better, AI agents are often good enough to solve problems directly (more on this later). Just make sure you’re following best practices when looking for an email automation for customer service platform.

3. Voice AI

Throughout this piece, we speak in general of AI agents, but one specific kind of AI worth singling out is the multimodal models able to parse voice commands and respond in kind.

On their own voice models aren’t new, but they’re now good enough to form a pillar of your customer experience strategy. Voice AI for customer experience can increase the efficiency of your operation, enhance the customer experience, and facilitate multilingual support.

4. Personalized Responses

Enhancing the customer experience by tailoring your interactions to their circumstances is easy enough to understand, but it’s far from trivial. However, a combination of advances in artificial intelligence and the vast amounts of data generated from our increasingly online lives is starting to change this picture.

However you’re reaching out to customers, people will respond better if you successfully draw on their specific data–purchase history, past interactions with your contact center, or whatever–to make it clear you’re talking to them.

Check out our guide to retrieval-augmented generation for more information.

5. Automating Content Generation

So, AI can help you personalize responses, but this is a special case of the more general fact that they can help you with content generation. Modern generative AI can create product information, summaries of calls or interactions, and even blog posts. These are huge time savers, reducing the workload for customer service teams and enabling them to handle more complex tasks.

6. AI Agents Are Handling Issues on Their Own

AI agents are great at automating routine tasks and asks, such as answering common questions or troubleshooting straightforward technical problems.

You’ll have to experiment with these tools to figure out which ones they can sort out alone, but we recommend maintaining robust checks to ensure they don’t behave in unexpected ways.

7. AI Systems are Letting Customers Solve Issues on Their Own

A related but distinct fact is that modern agents can help customers solve their own issues without the need for much oversight from agents.

AI agents can make your troubleshooting guides conversational, helping customers get their software working and guiding them through key steps while answering whatever questions come up along the way.

8. Natural Language Processing and Sentiment Analysis are Making Customers’ Feelings Clearer

There are different kinds of data, but two of the biggest categories are ‘structured’ and ‘unstructured.’ The former is the kind of thing you might find in clearly labeled columns in an Excel spreadsheet, while the latter consists of things like feedback from social media, product reviews, call transcriptions, etc.

Though unstructured data is profoundly informative, not much could be done with it until natural language processing in general – and sentiment analysis in particular – made significant strides. These techniques allow you to extract insights around how customers feel, at a much larger scale. AI agents can now automatically search for recurring issues or customer sentiment, while customer service teams can quickly respond to emerging problems and adapt in real time.

9. Predictive Analytics and Machine Learning are Helping Us Better Serve Customers

A similar idea is to use predictive analytics and machine learning. With these tools, enterprises can craft AI agents to analyze customer data to identify trends, preferences, and pain points. With enough data, for example, they might see cyclical patterns in customer buying behaviors or realize that a product feature consistently causes a certain kind of frustration.

This gives customer service teams and AI agents the ability to proactively address common issues and personalize responses (discussed above), thereby improving CSAT and other metrics by making interactions more helpful.

10. AI is Making Human Agents More Effective

We’ve made a few allusions to this already, but here we’ll make it explicit: AI agents can make your human agents much more effective.

Human agents can now use generative AI tools to help them craft responses, coach them to do their jobs better, and facilitate the automation of parts of their work. For example, Quiq’s Agent Assist offers suggested responses, agent coaching and process automation (see how it works here).

These are just a few examples, of course, but they’re enough to demonstrate the profound ways AI agents can augment (and improve) your workflows!

Leverage AI Customer Service Solutions Today

AI for customer service is among the most promising frontiers for our industry. To learn more about Quiq’s approach to customer-facing AI, click here.

5 Engineering Practices For Your LLM Toolkit

Posted on November 21, 2024December 11, 2024 J.R. Rettenmeyer

Large Language Models play a pivotal role in automating conversations, enhancing customer experiences, and scaling support capabilities. However, delivering on these promises goes beyond simply deploying powerful models; it involves utilizing a comprehensive LLM, or generative AI, toolkit that enables effective integration, orchestration, and monitoring of agentic AI workflows.

In this article, I’ll touch on a few time-tested software practices that have helped me bridge the gap between traditional software development and agentic AI engineering.

1. API Discoverability and Graph-Based RESTful APIs

Data access is crucial for AI agents tasked with understanding and responding to complex customer inquiries. Modern LLM developer tools should facilitate understanding and access through APIs that are well defined with JSON-LD, GraphQL, or OpenAPI spec. These API protocols enable AI agents to dynamically query and interpret interconnected data structures. The more discoverable your APIs, the easier it becomes for your AI to provide personalized and accurate service.
Similar to human agents onboarding to your support team, AI agents need access and understanding of your system data to provide relevant and accurate customer service.

2. Design by Contract with AI Function Calling

Ensuring reliable AI-to-system interactions requires strict compliance with well-defined operational rules. This is where the practice of design by contract proves invaluable. The best LLM tools should establish clear contracts for AI functions, ensuring that each interaction occurs within its designated boundaries and yields the expected outcomes. This structured approach minimizes errors and enhances the reliability of AI agents by mandating validation checks when reading or writing data.
Your LLM toolkit should promote and enforce a defined data schema for your AI agents. For more insights, refer to Quiq’s exploration of this topic in their LLM Function Calling post.

3. Functional and Aspect-Oriented Programming

Functional programming emphasizes pure functions and immutability, and when combined with aspect-oriented programming, which tackles cross-cutting concerns, it establishes robust and scalable frameworks ideal for AI development.

Modern LLM toolkits that embrace these paradigms offer sophisticated tools for constructing more resilient cognitive architectures. These components can be independently developed, tested, and reused, making them ideal for assembling complex AI agents, including agent swarms. Agent swarms, consisting of multiple AI agents working in concert, benefit particularly from an atomic, yet cohesive approach to decision making. Your design choices will become crucial as the demands of customer interactions grow more complex over time.

4. Observability: Ensuring Transparency and Performance

Your LLM toolkit should offer comprehensive monitoring capabilities that allow developers and business operators to track how AI agents make decisions. These tools should enable high level and deep dive analysis that clearly shows how inputs are processed and decisions are formulated. This level of transparency is crucial for troubleshooting and optimizing performance.

By offering detailed insights into AI performance and behavior, modern LLM toolkits play a critical role in helping businesses maintain high service quality and build trust in their AI-driven solutions. The ability to trace how and why a message was delivered or an action taken has never been so important, and top LLM dev tools provide it. Traditional logging and APM software won’t cut it in the era of stochastic AI. Please see Quiq’s LLM Observability post for a deeper discussion on the topic.

5. Continuous Integration

Continuous integration (CI) systems within LLM toolkits play an important role in development, testing, integration, and deployment of AI agents. Your toolkit should ensure agents adapt correctly to changes in models, data, logic or your system at large. LLM toolkits that oversee the lifecycle of AI agents will need to be resilient to updates and iterative improvements based on real-world scenarios and emerging capabilities of the models.

Additionally, modern LLM toolkits, such as those highlighted in Quiq’s AI Studio Debug Workbench, should provide an environment for running a wide range of scenarios. This includes allowing developers to closely inspect, recreate and replay AI behavior on-demand or test-time. You will need to be well informed and react quickly and confidently across the lifecycle of your project.

Remaining Skeptical in the Era of AI

As a software developer with 20 years of experience, I’ve found that a healthy dose of skepticism and reliance on time-tested practices have helped me remain focused on building robust solutions. Not only has this experience proven effective over the years, but it has also laid a strong foundation for my journey as an Applied AI Engineer.

However, LLMs present new challenges that traditional tools and techniques alone can’t fully address. To unlock the potential of these models, we must remain adaptable and open to integrating new tools, techniques and tactics. While I still often use Emacs for editing, I’ve also come to fully embrace the LLM toolkit equipped with a visual procode interface that promotes solid engineering practices. An LLM toolkit will not erase the need for your software engineering practices, but it does provide me, my team and our customers with the tools necessary to unlock the power of AI in an enterprise environment.

Finally, tools like AI Studio offer a surface where we can collaborate with our counterparts across the business to help grow AI that is well understood, reliable, and impactful. Without collaboration, an AI initiative will likely grind to a halt. You will need some new tools to help you bridge the gap.

To learn more about how Quiq is helping software engineers, operational teams and business leaders find the intersection of AI in 2025, learn more about AI Studio.

How the AI Chatbot for Customer Service Became the AI Agent (And How It Actually Works)

Posted on November 15, 2024December 12, 2024 Max Fortis

Chatbots have become a staple in customer service for brands across the world. This is why eight out of ten businesses have some kind of chatbot on their website to help customers in their journey.

And it’s not hard to see why, as there are myriad benefits to using such AI support chatbots. They’re available 24/7, without having to take breaks or sick days; they’re able to handle multiple conversations simultaneously; they’re cost efficient and scalable; they can personalize queries to each individual (more on this shortly); and they boost customer satisfaction.

Perhaps this is why the chatbot market was thought to be worth nearly $5 billion in 2022, a figure estimated to triple before the end of this decade.

But having said that, there’s a lot of diversity hidden under the ‘chatbot’ label. There are many techniques for building chatbots, these techniques have changed over time, and today, there are ‘AI agents’ which need to be distinguished from the older chatbots they replaced.

This is what we’re here to discuss today. We’ll first define AI chatbots in the context of customer service, provide an overview of their history, and how they’re different from the agents rapidly changing the contact center industry.

What is an AI Chatbot for Customer Service?

An AI chatbot for customer service refers to a program, platform, or machine-learning model that can perform some fraction of the work done by customer service agents.

Chatbots vary widely in complexity. First, there were the simple rule-based systems of yesteryear that attempted to understand the intent of a query and match it to an appropriate, pre-defined response. Over time, advances in machine learning, natural language processing, and data storage led to the billion-parameter large language models we use now, which can respond flexibly and dynamically under a range of circumstances, even to questions that are ambiguous or contradictory.

These are incredibly different offerings, but we won’t do more than point that out for now, as this will be our focus for most of the rest of the piece.

Regardless, an AI customer service chatbot generally lives on a company’s website, where it can answer questions. It has also become common to integrate them into various communication channels, such as Apple Messages for Business, WhatsApp, Voice, and email.

Though there are critical aspects of human interactions that are still not outsourceable to algorithms, customers have gradually become more willing to talk directly to AI chatbots to resolve their customer service issues. Surveys have shown that almost everyone has heard of chatbots and understands in general terms what they are. And nearly three-quarters of respondents prefer chatbots over humans to quickly get simple questions answered. When asked whether they were satisfied with their last interaction with a chatbot, 69% said ‘yes,’ and a little over half cited long wait times as one of their chief frustrations.

How AI Chatbots Have Evolved

As promised, let’s now discuss some of the ways in which the customer service AI chatbot has changed over time.

Here’s a broad overview, taken from TechTarget:

What are the Kinds of AI Chatbots?

You’ll notice the chart above tracks three broad types of chatbots, which is a categorization we more or less agree with—though we think there’s an important distinction between chatbots and agents, which isn’t reflected here. (We discuss that more in the next section).

The first kind of chatbot to be developed was by far the simplest, and it emerged from research done in the 1960s. These were based on a primitive model known as a ‘decision tree,’ and were only suitable for basic, formulaic interactions where there were clear rules and virtually no room for either ambiguity or creativity. In contact centers, robust AI agents are replacing these, but you might still see them answering the most common questions.

Although there was a lot of research into methods like neural networks, these ‘scripted chatbots’ were more or less the standard for the next four decades, until the field of natural language processing made enough progress to power a different approach.

Once it became possible to use sentiment analysis to detect emotional tones in writing, and entity extraction to automatically detect information like product names and formal titles (to pick just two examples), the road was paved to create more powerful ‘conversational chatbots.’

Unlike their predecessors, conversational chatbots could carry on much longer-range, multi-turn interactions, and help in a much broader variety of circumstances. Common examples of these tools are Siri and Alexa, both of which can process voice commands, look things up, fetch information, and even perform simple tasks (like scheduling a meeting or adding a reminder to your calendar).

Then, we come to the modern crop of AI chatbots, which are so much more powerful and far-reaching it’s better to call them ‘agents’ instead of ‘chatbots.’ These ‘generative AI agents’ are built around large language models, made famous by the release of ChatGPT in November of 2022.

For the most part, AI agents aren’t actually a new kind of technology, as neural networks have been around for a while and have been used in chatbots for a while, too. No, the single biggest distinguishing feature is that the networks are so big and are trained on such a bewildering variety of data that they can do things prior iterations couldn’t do.

No doubt you’ve spent some time playing with these models, or you’ve seen demonstrations of them, and you know what we mean. They can write code and poems, translate near-instantaneously between dozens of languages, describe (and generate) images (and videos), and take on all sorts of subtle postures in their interactions with humans. They can be instructed to act like a kindly grandma, for example, a stern teacher from fifth-grade, or an exceptionally polite and deferential friend.

This is precisely the reason that generative AI is having such a profound impact on contact centers. It can do so many things, and there are so many ways to fine-tune and tweak it, that people are finding dozens of places to use it. It’s not so much replacing human agents as it is dramatically simplifying and accelerating their workflows in hundreds of little ways.

AI Chatbots vs. AI Agents

Okay, now let’s get to the main distinction we want to draw out in this piece, the one between ‘AI chatbots’ and ‘AI agents’. In doing so, we’ll provide CX leaders with the valuable context they need to make the best decisions about the technological tools they deploy and invest in.

First, we’ve already written a lot about chatbots, so let’s define an ‘agent.’ Broadly speaking, an agent is an entity that can take one or more actions in pursuit of an overarching goal. Some agents are very simple, like single-celled organisms that just sort of float around looking for food, while some are very complex, such as the humans working out ways to terraform Mars.

But what they all have in common is a goal.

An AI agent is the same thing, it’s an artificial entity that can usually achieve a goal, like ‘download these data files and create a line chart with them’ or ‘check these six sources on quantum computing and summarize your findings.’

As with chatbots more generally, agents aren’t exactly new. We’ve been working with reinforcement learning agents for years, for example. But generative AI has opened up a whole new frontier, and the agent projects being built on top of it are really exciting.

A full discussion of this frontier is outside the scope of this article, but you can check out our piece on the future of generative AI for a discussion of specific agent projects.

7 Best Practices for Using AI Agents

What does concern us here is the impact this will have on CX leaders and the contact centers they manage, which is why we’ll cover some of the best practices of successfully using AI agents in this section.

1. Be clear about your intended use of AI agents.

As we’ve already mentioned, generative AI is great at many tasks, but the way to get the most out of it is to identify which KPIs you’re trying to drive and what changes you want to see, then implementing an agent that can help get you there. In other words, don’t be overwhelmed by its possibilities; start by drilling down into a few promising use cases and expand as appropriate.

2. Focus on design and access.

You want to be sure that the interface customers use to interact with your chatbot is sleek, intuitive, and easy to find. You can have the most powerful AI agent in the world, but it won’t do you much good if people hate using it or they can’t locate it in the first place.

3. Use full personalization.

One of the reasons modern chatbots are so powerful is that they can use retrieval-augmented generation to ‘ground’ their generations in sources of information–knowledge bases, product feeds, CRMs, Notion pages, etc. This makes replies more useful, while also making your customers feel more heard. So make sure your AI agent has access to the systems or data it needs to take action and be helpful (as you would with a new employee).

4. Gather feedback and improve.

AI agents are capable of being improved in a bevy of different ways. You should implement systems to gather your users’ impressions and use them to update your systems.

5. Let AI and humans play their strongest roles.

AI agents are great at many tasks, but others need a human’s superior flexibility and insight. The key here is to craft a system that can seamlessly switch between humans and agents.

6. Have your AI agents be proactive.

AI agents can be configured to reach out on their own if a user engages in certain behaviors or otherwise seems confused. For example, one well-known furniture brand and Quiq customer implemented Proactive AI and a Product Recommendation engine, which led to the largest sales day in the company’s history through increased chat sales.

7. Ensure transparency.

Most of us are really excited about the promise of generative AI, but one thing that has many concerned is the way data is used by these models, and their broader implications for privacy. Make your policies clear, and make sure you are being responsible with the data your customers trust you with.

You can use these best practices when designing your own AI agent system, but the easier way forward is to treat them as a checklist when you’re shopping around for third-party platforms.

AI Agents and You

Large language models, and the AI agents they make possible, will be a key part of the future of contact centers. If you want to learn more about this technology and the ways to harness it to redefine CX success, check out our latest guide.

Highlights from My Build vs. Buy Discussion with TTEC: How to Make the Right Strategic Choice for Your Organization

Posted on November 14, 2024November 15, 2024 Mike Myer

As the founder of Quiq and a veteran in the CX technology space, I recently had the pleasure of joining TTEC Digital‘s Experience Exchange Series over on LinkedIn Live to discuss one of the most pressing questions facing enterprises today:

Should organizations build their own AI solutions or buy existing ones?

In my conversation with Tom Lewis, SVP of Consulting at TTEC Digital, we explored this complex decision-making process and its implications for customer experience success. Here’s an overview of our discussion —and the highlights of our conversation if you missed it.

My key takeaways:

Assess your organization’s capabilities and resources honestly before deciding to build or buy
Ensure strong collaboration between CX and IT teams
Prioritize knowledge and data quality and governance for both building and buying
Consider a hybrid build and buy approach when appropriate
Maintain focus on risk management and compliance
Stay adaptable as technology evolves and keep your eye on prize AKA CX outcomes

Understanding the AI build vs. buy dilemma.

The rapid advancement of AI technology has created both opportunities and challenges for enterprises. While the promise of AI to transform the customer experience is clear, the path to implementation isn’t always straightforward. Organizations must carefully evaluate their resources, capabilities, and objectives when deciding between building custom AI solutions or purchasing existing platforms.

When considering the build approach, organizations gain complete control over their AI solution and can tailor it precisely to their needs. However, this comes with significant investments in time, talent, and resources. During our discussion, I emphasized that building in-house requires not just initial development capabilities, but ongoing maintenance and governance of the system.

On the buy side, organizations can benefit from immediate deployment, proven solutions, and regular updates from vendors who specialize in AI technology. The trade-off here might be less customization and potential dependency on third-party providers.

Bridging the gap with IT.

One crucial aspect we explored was the importance of alignment between CX leaders and IT departments. Success in AI implementation requires a collaborative approach where both teams understand:

Technical requirements and limitations
Integration capabilities with existing systems
Data security protocols
Scalability needs

I shared that the most successful implementations often occur when CX and IT teams establish clear communication channels and shared objectives early in the process.

Data and knowledge are the foundations of AI success.

Regardless of the build or buy decision, data preparation and having the right knowledge in your knowledge base to train the AI is crucial. Organizations need to:

Audit existing data quality and accessibility
Establish data governance frameworks
Ensure compliance with privacy regulations
Create clear data management protocols

During our conversation, I stressed that the quality of AI outputs directly correlates with the quality of input data. We recently released a guide on 3 Simple Steps to Get Your CX Data Ready for Quiq — I highly recommend you check that out for more actionable tips on data readiness.

Don’t let perfect be the enemy of good.

Many CIOs are concerned that it’ll take years to prepare their knowledge and data for AI. To that, my advice is: ‘Don’t let perfect be the enemy of good.’ You’ve got to start somewhere, and there is sure to be a crawl-walk-run framework you can devise with the data available to you now. It’s all about identifying and isolating a first use case.

My other piece of advice to CIOs who may be inundated with AI data concerns is to get your hands dirty and start using AI. Get started with an implementation that you don’t expect to last a whole five years, but are rather expecting to learn, iterate, and fail forward from. Now is the time to lean in, not sit back—even if things are not perfect to start.

Managing risk and ensuring compliance.

One thing I highlighted to Tom was that AI is not super valuable to your business all by itself. What makes it so is combining it with your company data. And that means risk management is paramount. Key considerations include:

Data privacy and security
Regulatory compliance
Transparency in AI decision-making

When planning for risk management and compliance, organizations can build trust by:

Implementing robust security measures
Maintaining clear communication about AI use
Regular auditing and monitoring of AI systems
Establishing clear governance frameworks

What happens when AI creates delightful experiences that customers want to interact with even more?

Tom’s theory is that if you make communicating with a brand effortless, consumers will interact with that brand more, not less. I not only agree, but I think it’s a goal brands should strive for.

Customers are more likely to self-service via AI-powered conversations than on the phone or in person, especially when it comes to the minutia of decision-making. For example, a customer is more likely to ask “What’s the sofa frame made out of?” when evaluating a furniture purchase over chat or their digital messaging channel of their choice. These types of questions are not usually the ones people pick up the phone or march into a physical store to ask, but they are the kind of conversations that lead to more purchases.

Similarly to how retail clerks are ever-present for customers to ask questions, AI that understands and responds to natural language can create even more delightful experiences that build relationships and brand loyalty while driving more revenue.

Final thoughts and looking forward.

The path to AI implementation in CX isn’t one-size-fits-all. Success lies in making informed decisions based on your organization’s unique needs, capabilities, and objectives. Whether building or buying, the focus should remain on delivering value to customers while managing risks and resources effectively.

That said, this technology is exciting, moving fast, and stands to deliver on its promises when done correctly. In fact, I think in the next five years, there’s going to be a shift in customer perception that AI provides even better service than human agents.

Want to listen to my whole conversation with Tom? Check out the replay here.

These are the Most Essential Customer Service Chat Platform Types

Posted on November 6, 2024November 15, 2024 Mark Kowal

Customer service has come a long way since the first contact centers. These days, most complaints are dealt with through a customer service platform, and those platforms are often focused on using chat to communicate.

In this piece, we’ll discuss customer service chat platforms, comparing various features you should be looking out for and how they’ll help your business. Let’s get going!

Key takeaways:

Customer service remains incredibly important, and there’s a ton of data to back that up.
More and more companies are turning to customer service platforms because they help you fit better into busy customers’ schedules, leverage advanced tools like generative AI, and provide a world-class experience.
When evaluating a potential platform, there are a few things you should keep in mind. It should support many different channels (from email and voice to Facebook Messenger and Wechat), it should allow you to have humans do what they’re best at and AI systems do what they’re best at, and you should be able to build a CRM, helpdesk, knowledge base, or agent workspace on top of it without too much difficulty.

Why You Should Be Using a Customer Service Chat Platform

As professionals in the customer service arena, you probably already understand why it’s important to have a dedicated customer service platform–especially when it enables chat.

Still, it’s worth pointing out how compelling the data supporting this conclusion is. High-quality customer service is a huge selling point, as 4 out of 10 customers indicate they will split with a business over bad service (this figure has been considerably higher in prior years). All told, it’s been estimated that U.S. businesses have lost as much as $1.6 trillion when customers switch after having a bad encounter with customer service.

On the other hand, the data also paints a clear picture of how valuable your reputation can be, as 68% of survey respondents have said they will select your company if you are well-known for being helpful and courteous.

How Can a Chat-Based Customer Service Platform Help?

Having said that, here are some of the high-level benefits that accrue to companies that invest in good customer service chat platforms:

Meeting your customers where they’re at: A good platform will give you many ways of talking to your customers, which means they can choose the option that makes the most sense given their schedules and preferences. Consult the next section for more information on customer service chat channels.
You can integrate with generative AI: The big story of the past few years has been the rise of large language models, with concomitant changes in how contact centers run. You’ll want to vet your platforms to make sure they’ll allow you to leverage this technology–as well as related abilities like advanced natural language processing and sentiment analysis–as it’s rapidly becoming tablestakes.
Personalization: One of the other significant applications of modern machine learning is tailoring content to each person’s tastes (think of how Netflix or Spotify learn what shows and music you like and recommend more of it). Using something like retrieval-augmented generation (RAG), you can draw on a customer’s prior interactions or purchases to answer their questions in a way that speaks to their unique circumstances.
You can gather better data for KPIs: It’s hard to know whether your strategies are working unless you have the right data. Quality customer service platforms make gathering and analyzing customer data a breeze.
Your agents can be more productive: Between generative AI, automation features, self-serve, and live chat, a customer service chat platform can make your agents far more effective. This acts to boost customer satisfaction and agent satisfaction at the same time.

The Main Customer Service Chat Channels–And How to Use Them

Okay, now let’s discuss some available options. There’s a lot to cover here, so we’ve chosen to break things up like this:

Customer service channels are the actual applications you use to talk to your customers, like voice chat, email, SMS, Apple Messages for Business, or WhatsApp.
Customer service modes are what we call the ways you use your channels, with the three big categories being “speaking to a human agent,” “speaking with an AI agent,” and “speaking with a mix of both.”
Customer service platforms are what we call products like Quiq, Zendesk, etc., that serve these capabilities through a user interface.

In the sections below, we’ll discuss all three.

The Major Customer Service Channels

Web chat has become a standard-issue channel for many websites. Everyone from energy companies to car dealerships has a portal on their web pages that allows you to speak with either a human or AI agent, and this is something you should offer as well.
SMS and rich chat are both crucial channels, given how prevalent phones have become. Many customers find they prefer the convenience of dealing with agents through text messages, as this enables asynchronous conversations that fit better into busy lives. Through multimedia messaging service (MMS), SMS can send more than text–it can handle pictures, videos, emojis, audio, buttons, carousel cards, quick replies, and more. Rich messaging supports this natively, so you have multiple options for creating engaging interactions with your customers.
Voice (over the phone) was once one of the dominant ways of handling customer service issues. Though it has lost a lot of ground to texting, email, and chat, it’s still worth having a phone number people can call if they need to.
Email is, of course, thoroughly embedded into modern life, and is a common way of communicating. You’re almost certainly already supporting email, and if you’re not, you should be.
WhatsApp for Business–a messaging, voice, and video-calling application managed by Meta–boasts over two billion users worldwide. Given its widespread use for texting, it has become a favored platform for businesses looking to utilize instant messaging for commercial purposes.
Facebook Messenger is another service Meta provides that customer-centric businesses should consider, especially due to its large user base. Furthermore, companies implementing effective automation solutions on Facebook Messenger can respond to 80% of incoming customer inquiries and attain customer satisfaction rates up to 95%.
Instagram Messenger is important because many members of your audience are already engaging with businesses on the widely-used Instagram platform. Additionally, its appealing API facilitates the setup of automation, enhancing the efficiency of your business.
Apple Messages for Business greatly enhances your customers’ access to your business by providing a “message” icon on Maps, Siri, Safari, Spotlight, or your company website, along with other contact options such as QR codes. Furthermore, the widespread popularity of Apple and its product ecosystem underscores why you should be investing in its instant messaging capabilities.
WeChat is an extremely popular platform in China, boasting over a billion users. Similar to the other platforms we’ve discussed, WeChat offers a Business version that enables you to interact with customers, market your services, and process payments. However, setting up a WeChat Business account requires verification of your business license. If you have a substantial customer base in China, this could be the optimal choice.

	Convenient	Asynchronous	Scalable	AI integrations
Web chat	Limited	Limited	Yes	Yes
SMS, MMS, and Rich Messaging	Yes	Yes	Yes	Yes
Voice	Limited	No	No (but will become more scalable as AI voice models improve, see next column)	Kind of (voice models are getting good, and the best vendors already have multimodal voice support.)
Email	Limited	Yes	Yes	Yes
Messaging Application (Facebook, WhatsApp, etc.)	Yes	Yes	Yes	Yes

The Major Customer Service Modes

Now, let’s discuss the basic ways of using a customer service communication channel.

Human agents
Obviously, the original way of solving issues was to have a human manually walk through all the steps. The customer service industry remains large in part because there are many aspects of the “human touch” that cannot be automated or replaced.

This is one huge advantage of using human agents, and it’s why we’ll still need humans for a long time. The downside, of course, is that people need breaks, time off, and to sleep, or else they’ll burn out.

AI agents
Until just a few years ago, the best we could do with AI systems was build brittle, rule-based chatbots that were of limited use. That’s no longer the case, and today’s language models can handle many queries directly, or else route to the most qualified human being.

The advantages of AI agents are that they can scale almost infinitely, can work all day and night, and can almost instantly handle complex tasks like translating between different natural languages. The disadvantage of AI agents is that there are still many issues that they simply can’t handle.

A mix of both
But the real power lies in what are commonly called “centaurs,” i.e., systems combining human ingenuity and flexibility with the scalability of AI.

This will look different for different situations, but a common centaur setup is to have routine issues solved by an AI agent, and everything else sent to a human based on its content and priority. Even then, however, AI can be of enormous use. Quiq’s Human-Agent Assistants, for example, use AI to suggest replies to humans, making both the AI component and the human component of the system more effective.

This means you can allow humans and machines to play to their respective strengths, but it can take time and effort to get everything configured and running smoothly.

	Human agents	AI agents	Centaurs
Adaptable	Yes	Limited	Yes
Infinitely scalable	No	Yes	No
Needs breaks	Yes	No	Yes (the human part)

How This Figures Into Picking the Right Customer Service Chat Platform

Now, we’ll close the section by discussing customer service chat platforms like Quiq.

Today, most of these platforms offer functionality like data tracking, CRMs, various communication channels, and native support for generative AI. If you choose a good one, you’ll be able to create reports about the effectiveness of different agents, how happy your customers are on average, how long it takes you to resolve issues, etc. They should also make it easy for you to incorporate your own data sources–a knowledge base, a product catalog, an API–so that you can personalize replies across channels.

Another thing to look out for is how easy a given platform makes it to set up a helpdesk or knowledge base (or to incorporate an external one). The former allows you to track tickets and manage your agent workflows (another thing that AI agents can help with), while the latter offers resources customers can use to resolve their own problems. Both are important.

	CRM	Help Desk	Knowledge Base	Agent workspace
Supports customer segmentation and bespoke communication	Yes	Yes	No	Yes
Gives granular data insights into customers	Yes	No	No	Yes (but only if the vendor has robust data integrations)
Facilitates multi-channel communication	Yes	No	No	Yes
Supports templates	Yes	Yes	Yes	Yes
Enables automation	Yes	Yes	Yes	Yes
Can integrate with other tools (including AI)	Yes	Yes	Yes	Yes

Getting Customer-Centric Chat Right

We’ve offered a lot of context about how to think about customer service chat platforms, but one thing we haven’t discussed is the difference between rolling your own solution vs. going with a third-party platform.

It’s a big topic, involving non-trivial tradeoffs on either side. To get up to speed as quickly as possible, check out our whitepaper!

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