Customer service is your silent business hero, and AI is its modern-day armor. With AI, not only can you turbocharge efficiency and elevate customer interactions but also usher in a new era where over 70% of engagements are expected to be AI-driven by 2023. Dive below to explore the vast realms of AI-enhanced service, its undeniable advantages, and the transformative ways it's redefining the support landscape
Customer service is the backbone of your business. It doesn’t get the most attention, but it’s vital.
AI customer service can completely redefine how you serve your customers, taking that vital area of your business and revamping it entirely.
Not only can it save you time and radically improve your team’s efficiency in terms of both workflow and information management, but it can also transform the way your customers are served by you.
But it goes way beyond just chatbots.
By utilizing AI in your customer service, customers can get answers to their queries that are:
Plus, your team can get easier access to information, drastically reduce the effort it takes to handle basic tasks (or fully automate them altogether) and get more time to focus on high-importance requests.
Below, we’ll talk about everything from the advantages and challenges of using AI for customer service to the specific tasks it can help with and how.
Read on to dive in or jump to the section most relevant to you.
The term “AI customer service” doesn’t refer to one thing, but rather is a blanket term for any form of AI tech used to improve the customer service experience.
Examples of that include:
Each of these AI tools– and others– serve to do two things:
Both your amazing CS team and valued customers benefit from AI in multiple ways.
Pretty neat, right?
Now, let’s jump into the advantages– and some challenges– of using AI in your customer service.
After that, we’ll cover some specific examples of how you can use AI to optimize your customer service.
AI can provide tons of amazing advantages for your customer service.
Here are a few of the most notable:
First, let’s talk about the agent side. These are some of the business-facing benefits you can expect by implementing AI into your customer service operation:
One of the standout features of AI is its ability to make multitasking a possibility without being overwhelmed.
Unlike us humans, who can generally attend to only one customer at a time, AI can handle multiple inquiries simultaneously.
This ability translates to significantly shorter response times and allows agents to focus on more complex tickets.
Humans need rest, breaks, and vacations. AI doesn’t.
Implementing AI in customer service allows customers to receive support any time of day or night, regardless of holidays or weekends.
This round-the-clock availability boosts the brand's image via greater perceived dependability and trustworthiness in the eyes of customers.
We’ve touched on this several times already, but one of the greatest benefits of AI is its ability to take a complex task that typically takes a human X amount of minutes or hours and cut that down significantly.
While the initial investment in AI might seem substantial, in the long run, AI is a cost-effective addition when compared to managing large human teams.
That’s especially true for handling basic inquiries and tasks that don't require emotional intelligence or a nuanced understanding.
AI allows you to either handle more requests than before or cut your team down while handling the same number of requests with a smaller budget.
Unexpected spikes in customer service traffic are likely no surprise to you.
They can happen due to a variety of reasons:
A significant portion of customer service inquiries often revolve around the same set of questions. Think "How do I reset my password?" or “How do I update my account?”.
Addressing your most common support questions tends to be time-consuming and monotonous for human agents as they’re your most common requests.
However, AI can effortlessly manage simple, repetitive queries.
By doing so, you not only ensure that customers receive immediate and consistent answers but also free up your agents for more important things.
This becomes time they can focus on more nuanced, complex escalations that require a human touch, ensuring optimal use of resources and expertise.
The diverse nature of customer inquiries requires a system that can quickly categorize and route them to the right place. AI excels in this.
With advanced algorithms and machine learning, AI can analyze the content and context of a customer's query, triaging it to determine its priority and nature.
Beyond that, it can escalate the issue to the most appropriate agent or department, ensuring that a customer's concern is addressed by someone with the right expertise. This not only speeds up resolution times but also enhances the accuracy and effectiveness of the responses.
A major challenge in customer service is the high turnover rates of agents. Repetitive tasks and the constant pressure of handling customer complaints can lead to burnout.
By automating the more monotonous tasks, AI allows agents to engage in more meaningful, varied tasks.
This not only reduces the risk of burnout but also offers agents opportunities to develop and utilize a broader skill set.
Engaging in complex problem-solving and having the time to build deeper customer relationships can lead to increased job satisfaction. And, in turn, reduced turnover.
Traditionally, onboarding new customer service agents involves extensive training.
They need to be familiarized with FAQs, company policies, product details, sit in on live calls, and more.
AI can streamline this process. With systems that can instantly provide answers to a wide array of questions and guide agents in real time, the need for exhaustive memorization and hands-on guidance is greatly reduced or eliminated.
New team members can rely on AI to assist them in their initial onboarding, reducing the learning curve.
Furthermore, as the product or service evolves or your policies change, AI can be updated centrally, ensuring that all agents have access to the latest information without the need for continuous retraining sessions.
Next, let’s talk about the benefits your customers can expect:
In the era of social media and 1-click ordering, modern customers are used to instant gratification, so meeting that– or something close to it– is an expectation.
They expect immediate answers and any delay can lead to frustration.
AI, with its ability to process information incredibly fast, can provide the lightning-fast responses that customers expect, ensuring that customers walk away feeling good about the experience.
Human beings are prone to, well… let’s just say inconsistencies. AI on the other hand is incredibly consistent and never gets tired.
This consistency in customer service ensures that the brand's image remains untarnished and trustworthy and reduces the chance for issues to arise due to a mistake made by an agent.
One size doesn't fit all, especially in customer service. With AI's ability to quickly analyze a customer's history and behavior, responses can be tailored to suit individual needs.
Whether it's product recommendations or handling grievances, AI can offer a more personalized and relevant interaction, making customers feel valued and taken care of.
Global businesses often grapple with the challenge of serving a multilingual customer base.
AI, however, is equipped with advanced translation algorithms that can understand and respond to queries in 90+ languages (and, currently, do a pretty darn good job at it), ensuring that language is never a barrier to exceptional customer service.
AI has been an amazing addition to teams everywhere, and few areas have felt that benefit more than customer service.
But with those benefits also come some challenges, challenges you should be aware of as you work to incorporate AI-driven tools into your customer service arsenal.
Understanding these challenges is key to maximizing the benefits of your AI tools.
Fortunately, they’re all solvable. However, they’re no less important and you’ll need a game plan based on current knowledge to make sure you can correctly navigate them. Those challenges include:
In an age where data is considered the new oil, AI thrives on vast amounts of it. From purchasing behavior to interaction histories, AI uses data to make informed decisions.
As a result, potential issues with data leaks and privacy are a concern.
The good news is that with the proper AI systems in place, this isn’t an issue.
However, think twice before just jumping on ChatGPT and running all your data through it.
Here are a few sub-challenges to take into consideration within the realm of data privacy:
For AI systems to operate effectively and provide tailored customer interactions, they rely heavily on vast amounts of user data.
However, the methods businesses use to gather this data can often be a significant point of contention. Without clear communication, transparency, and obtaining explicit consent from users, companies can inadvertently breach established privacy norms.
This can lead to suspicions and apprehensions among customers about how their personal information is being used, potentially causing them to disengage from or distrust the brand altogether.
And while this isn’t necessarily an issue solely to do with AI, it is one in which AI is included.
The digital landscape is fraught with threats, from hackers seeking unauthorized access to internal misuse of data.
For businesses, ensuring that the customer data they collect is stored securely and is accessed only for legitimate purposes becomes vital. Any lapse in this regard can have catastrophic consequences.
Data breaches or misuse can quickly tarnish a brand's reputation, eroding years of trust and goodwill built with its customer base. It's not just about keeping the data safe; it's also about using it in a manner that respects customer privacy and aligns with their expectations.
In today's regulatory environment, where data protection and privacy laws like the General Data Protection Regulation (GDPR) are relevant, businesses can face severe legal consequences for non-compliance. These can range from hefty fines to operational restrictions.
The financial damages aside, the reputational damage a company can suffer due to perceived or actual negligence in data privacy can be significant.
In the age of information, where news travels fast, ensuring strict adherence to data privacy norms is not just a legal necessity but a critical business imperative if you want to keep your customers happy.
Hand in hand with privacy is the challenge of security. Challenges include:
While bringing about unprecedented conveniences and efficiencies, the digital age has also ushered in a new set of challenges.
Foremost among them is the alarming frequency of data breaches, even within colossal and seemingly well-protected corporations.
Every breach event exposes vast amounts of sensitive customer data, from personal details to financial information.
For businesses employing AI in customer service, where data is the lifeblood fueling interactions, such breaches can be catastrophic.
Not only can they result in immediate financial ramifications and legal entanglements, but they also erode hard-earned customer trust, potentially decimating a brand's image in the marketplace.
While this complexity enables AI to perform advanced tasks and deliver personalized customer experiences, it can also be its Achilles' heel.
Sophisticated cyber-attackers, armed with evolving techniques, often seek out vulnerabilities within these complexities that go unnoticed by their users.
They exploit potential oversights in system design or implementation, making AI-driven customer service systems a potential target.
Therefore, it's crucial for businesses to understand the intricacies of their AI systems fully (or hire a team that does) and be ever-vigilant about potential security weaknesses.
A robust defense against cyber threats doesn't come cheap.
The rapidly evolving nature of cyber threats requires businesses to constantly update and fortify their defenses, investing in the latest security protocols, technologies, and expertise.
For most businesses, the capital required to ensure top-tier security can be daunting.
However, given the risks associated with data breaches and the potential fallout from compromised customer data, investing in security isn't just a line item on the budget– it's an absolute imperative.
When weighed against the potential financial, reputational, and legal consequences of lax security, the cost of ensuring a good digital defense is small.
AI is only as good as the data it's trained on. If this training data carries inherent biases, the AI's functioning can be skewed.There are a few ways this can manifest:
If an AI is trained on historical records that inherently carry biases– whether they be cultural, racial, gender-based, or any other form– there's a substantial risk that the AI system will internalize and perpetuate these biases in its operations.
Instead of being an unbiased tool, the AI becomes a mirror, reflecting and magnifying societal prejudices and imperfections that have persisted over time.
What does this have to do with customer service?
Certain individuals might consistently receive less optimal service, simply because of the skewed data the AI has been trained on.
The practical implications of bias in AI-driven customer service can be severe.
Customers might find themselves on the receiving end of unfavorable or even inappropriate responses due to the AI's biased decision-making.
For instance, credit applications could be denied, service requests might be deprioritized, or marketing offers could be skewed based on inherently biased factors.
This kind of unfair treatment can lead to significant customer dissatisfaction.
Worse still, if these biases become public knowledge, they can ignite PR crises that can severely damage a company's reputation, leading to a loss of trust and potential fallout.
Combating bias in AI is not a one-off task that can be handled in the training phase.
As AI systems evolve and learn, there's a need for continuous monitoring to ensure that they aren't developing or perpetuating biases over time.
This demands a concerted effort from you and your team (unless, again, you hire a team to do it for you), involving regular audits of AI decisions, retraining the AI with more balanced data sets, and even potentially incorporating feedback loops where human agents can flag and correct biased decisions.
That’s all well and good, but this continuous monitoring can be resource-intensive, requiring both tech investments and human oversight.
A lesser-known but significant challenge is when AI "hallucinates" or produces outputs based on patterns it believes to exist but don't.
The result is that the information that you and your customers receive is false, which can lead to all kinds of issues.
While AI excels at addressing issues they've been trained for, there are inevitable instances where they encounter unfamiliar scenarios or questions.
In these situations, lacking the human capacity for judgment and intuition, AI might provide answers that aren’t entirely accurate or relevant.
This misleading information can arise from the AI drawing incorrect inferences from its training data or misinterpreting the context of a query.
Such inaccuracies, even if occasional, can lead to customers making decisions based on flawed information, potentially resulting in frustration, inconvenience, or even financial ramifications.
When customers reach out for assistance or information, they operate under the assumption that the guidance they receive is accurate.
However, when AI-driven customer service tools provide misleading information, this trust is directly compromised.
Even isolated incidents of misinformation can lead to a ripple effect, where customers begin to question the reliability of the brand as a whole.
Over time, this erosion of trust can lead to decreased customer loyalty, negative word-of-mouth, and a tarnished brand image. In a competitive marketplace, where customer trust is vital, ensuring the accuracy and reliability of AI-driven interactions becomes paramount.
Fortunately, this is something easily remedied by using the right AI tools. However, if you don’t know what you’re doing, you could end up shooting yourself in the foot.
One of the major challenges faced by AI in customer service is the inability to fully grasp the context in which a query is raised.
While humans can often read between the lines or rely on previous interactions and cultural nuances to interpret a customer's concerns, AI operates largely on the data it receives.
This means that if a customer doesn't phrase their question or concern in a way the AI is familiar with, the resulting answer may not be relevant or accurate, leading to customer frustration.
For that reason, using a public tool with a massive collection of data such as ChatGPT may not be the best route for your AI solution as its store of data could alter outputs in a way that affects accuracy.
Instead, by either setting up your own generative AI system or using a third party, you can create a model based on your organizational knowledge that is both accurate and dependable.
Not every AI model is created equal.
AI models, especially those that aren't highly sophisticated or adequately trained, can struggle with intricate or multi-faceted customer queries.
Human agents are adept at asking follow-up questions, probing deeper to understand the root of a problem, or even discerning when multiple issues are being presented at once.
AI, on the other hand, if not adequately trained can either oversimplify the response or fail to address all aspects of the query, which can lead to incomplete resolutions and dissatisfied customers.
At its core, customer service is a deeply human-centric function, often requiring agents to display empathy and emotional intelligence to identify with and help customers.
While humans can sense frustration, desperation, or urgency in a customer's voice or written message, AI lacks this innate ability.
Even with advancements in emotion detection, AI systems can sometimes miss the mark, offering responses that might seem cold, robotic, or insensitive, especially in emotionally charged situations.
This has begun to change as more advanced AI can do some pretty impressive sentiment analysis. However, it still has a ways to go before it can match a real human.
While AI has the potential to provide highly personalized experiences based on data, not all AI-driven customer service tools are there yet.
Customers increasingly seek experiences that are tailored to their history, preferences, and needs.
Even with advancements in emotion detection, AI systems can sometimes miss the mark, offering responses that might seem cold, robotic, or insensitive, especially in emotionally charged situations.
A generic or one-size-fits-all response from AI can make customers feel unheard or undervalued, impacting their overall perception of a brand.
To avoid this issue, be careful about what AI tools you use within your customer service operation. As we mentioned already, some are great at offering high levels of personalization while others are highly limited.
A common frustration encountered by customers is getting trapped in seemingly infinite loops with AI-driven systems, especially with chatbots.
A customer might pose a query that the AI doesn't understand, and instead of gracefully directing the customer to a human agent or another solution, the AI keeps cycling through its set responses. This can lead to prolonged resolution times and significant customer exasperation.
This one is mostly due to the way a chatbot is set up and not a challenge inherently with AI itself, but it’s still a worthwhile point to keep in mind.
A seamless transition from AI to a human agent is crucial for optimal customer service.
However, challenges arise when the AI system doesn't adequately pass on the information it has gathered from the customer to the human agent. This forces customers to repeat themselves, adding friction to the service experience.
Moreover, if the handoff isn't smooth or intuitive, customers might feel bounced around without clear direction, further eroding their trust and satisfaction.
Just a few years ago, the only relevant application of AI in customer service was in early chatbots with limited AI capabilities.
Now, the applications of AI in customer service are numerous and extend far beyond simple response generation on the customer side to an array of benefits for your agents as well.Here are some of the major applications:
It can be difficult for agents to find the information they need quickly and efficiently while communicating with customers.
Amazingly, AI can now help scan your knowledge base and automatically retrieve the information they need for the job at hand in the form of articles and help docs. It can even scan the conversation to offer suggested replies.
In addition to that, you can use AI to generate support templates to streamline replies. Men’s grooming brand BOVEM recently started using AI to create custom support templates that agents could use to quickly and easily respond to common support topics.
The result was a 20% increase in customer satisfaction rates as well as a substantial 30% reduction in agent response times.
With data such as that, it’s no understatement to say that AI can now offer a more streamlined process where your entire team spends less time searching for answers and more time focusing on high-importance tasks.
Whether you use a dedicated software or respond via email, most teams still ultimately send their support responses via email.
With AI, you can enhance your email support in a variety of ways:
Speaking of email, AI can do a lot more than just send automated emails.
You can even use AI for email prioritization. AI can automatically scan through your inbox and identify the high-priority messages you need to get to first.
If you use a dedicated support software for customer service, you can have AI work through that or route the messages to your inbox.
Instead of scanning through 100+ emails each day, figuring which to get to first, AI can do the work for you by allowing you to get to the important stuff right away, saving you both time and headache.
Speech-to-text isn’t new. In fact, the first speech-to-text software was released nearly three decades ago by way of Dragon Dictate in the 1990’s.
Unfortunately, it never worked all that well.
However, recent developments in AI have turned a somewhat unreliable tool into one that is both fast and super reliable.
Specifically, AI can now take recorded support calls and turn them into customer transcripts automatically, allowing you to study your team’s responses and find areas for improvement.
These transcripts can also be used to help train new agents, becoming a valuable training resource as well as to identify customer trends and behaviors (all of which AI can help with).
Taking that same idea of AI text-to-speed further, AI can also now one better: generate full support summaries.
You can have AI scan all customer interactions to automatically pull out key pieces of information such as:
With this, you can get a full, holistic view of that customer’s support experience without lifting a finger.
Similar to inbound email prioritization, you can now use AI to scan support requests and automatically prioritize the most important requests.
Similarly, it can take that two steps further with:
Taken together, these features offer the potential to save hundreds of hours of manpower each year from the average customer service team, who often get bogged down in routing requests to different departments and people.
How about walking customers through the entire purchase process?
1-800-Flowers developed a digital concierge service for customers who might not quite know what they’re looking for, or who simply rather have a conversation about their needs (think walking into an old flower shop).
Using natural language understanding and generation (NLU/NLG), their GWYN (Gifts When You Need) chatbot can do just that, interpreting a customer’s request and finding a relevant bouquet or other product to fit their need. It even helps them process their order right then and there.
While you may understandably not be ready to completely revamp your online ordering process just yet, this type of solution is great for customers who might need a little extra assistance figuring out what they’d like to get (or a more step-by-step hand-holding checkout process).
One of the most interesting applications of AI in the customer service realm is sentiment analysis.
AI can now scan communications with customers and pick up on certain cues, providing insights concerning customer sentiment, intent, and language, as well as offering suggestions for responses.
This is more than just a time-saver, it allows agents to pick up on things they otherwise might not have noticed so that they can offer more effective responses (and faster, as they can spend less time reviewing each request’s information).
So far, we’ve talked about what’s possible with AI currently. But you probably already know that things are just getting started.
So, now let’s talk a bit about what is to come in AI (as it relates to customer service) and what to look forward to.
Here are a few trends to keep an eye on:
Gone are the days when voice assistants were just about setting alarms or playing music.
Their future in customer service is promising:
Understanding the emotional state of a customer can be a game-changer in customer service.
Here are some things you may be able to do with AI soon:
Anticipating a problem before it becomes one is the pinnacle of customer service (or avoiding the need for it altogether).
That’s what these AI features could help you with:
Finally, the merging of AI with Augmented Reality (AR) and Virtual Reality (VR) presents uncharted territories of customer support.
This stretches somewhat far beyond what you might think of AI currently, but they’re all valid technologies that are currently in the works:
The future of AI in customer service is brimming with possibilities.
As AI continues to evolve, it will offer teams unprecedented opportunities to revamp and elevate their customer experience.
Not only that, it allows those same teams to drastically cut down on the time and manpower it takes to handle that same number of requests, allowing you to do more with less (and just downright better).
In fact, with Gleen AI, you can use the full power of a generative AI/ML system and GPT-4 to optimize your customer service process in multiple ways through a single tool.Schedule a demo to see what Gleen can do for your customer service team (and business).
No matter what you use to bring the power of AI to your customer service team, we hope this guide helped you take the first step.
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