Application Areas of AI in Support
- The Rationale for Using AI in Customer Service and Support
- Exploring the How: Key Applications of AI in Customer Service and Support
This chapter is from the book
This chapter is from the book
This chapter is from the book
Knowing is not enough; we must apply. Willing is not enough; we must do.
—Johann Wolfgang von Goethe
In today’s rapidly changing digital landscape, the confluence of emerging technologies with established industries has led to unprecedented advancements. Artificial intelligence (AI) with customer service is one such powerful synergy. As businesses continuously strive to enhance the customer experience, AI emerges as a game changer, introducing efficiencies and capabilities previously thought unimaginable.
AI, at its core, is the embodiment of machine-driven intelligence, learning, and decision-making. Its influence is palpable across diverse sectors, from healthcare to finance, manufacturing, entertainment, and everything in between. However, its imprint is particularly transformative in customer service, where immediacy, accuracy, and personalization are paramount.
The past decade has witnessed a significant shift in customer service paradigms. Businesses are no longer solely competing based on products or prices; the quality of customer service has become a critical differentiator. AI serves as a bridge in this new landscape, helping businesses transcend traditional limitations. Whether providing 24/7 support through chatbots, analyzing vast amounts of customer data for actionable insights, or personalizing customer interactions in real-time, AI’s potential is vast and largely untapped.
But why is there a growing need for AI in customer service? For starters, the modern customer is more informed, connected, and demanding than ever before. They seek instant gratification, personalized experiences, and solutions that cater to their unique needs. Traditional customer service models, often reactive and limited by human constraints, struggle to keep pace with these evolving expectations. AI promises to meet and exceed these expectations with its proactive, scalable, and data-driven approach.
As we dive into this chapter, we aim to illuminate the multifaceted applications of AI in customer service, offering a holistic view of its current impact and potential for the future. Through expert insights, real-world examples, and forward-looking analyses, we’ll explore how AI is not just an add-on but a necessity in crafting the customer service experiences of tomorrow.
The Rationale for Using AI in Customer Service and Support
We live in a paradoxical time in which technology brings us closer, yet sometimes feels like it’s pushing us apart. The rationale for using AI in customer service and support isn’t about replacing the human connection we all crave—it’s about enhancing it.
Consider the Industrial Revolution—a tectonic shift that altered the fabric of society and propelled humanity into an era of unprecedented growth and complexity. Before this revolution, artisans crafted products by hand, laboriously perfecting each piece. Then came the assembly line, mechanization, and eventually automation—each a disruptive innovation that redefined what was possible. In the modern customer support landscape, artificial intelligence stands as the new machinery, a force as transformative as the steam engine or the power loom. Just as the Industrial Revolution was not solely about machines replacing craftsmen but about forming a symbiotic relationship, AI comes not to replace human expertise in customer support but to augment it. Let’s explore this new frontier where machinery, craftsmanship, AI, and human intelligence coalesce to forge a revolution in customer service and support.
The Business Blueprint: Efficiency, Scale, and Profit
Just as the advent of machinery during the Industrial Revolution brought scale and efficiency to production, AI introduces greater data-driven insights, efficiency, and cost-effectiveness into the realm of customer support. If we look at an assembly line, we see each station designed to serve a specific function to optimize production. And each station in that line works toward developing a piece of the finished product that is of higher quality and produced at a faster rate than one person assembling all the parts of the product alone. Now, apply this concept to the world of customer support. AI acts as those specialized stations, each fine-tuned to handle a specific facet of the customer experience. This modern “assembly line” enables companies to promptly address inquiries, accurately predict customer needs, and personalize interactions, creating an unparalleled level of service. Beyond just handling questions or problems, AI offers the power of preemptive solutions, dynamic learning, and the ability to seamlessly integrate vast amounts of data, transforming how businesses connect with and understand their clientele. As we delve deeper into this chapter, we’ll unpack the multifaceted benefits of AI, shedding light on its transformative power in efficiency, cost-effectiveness, personalization, and more.
Efficiency and Scale
When we talk about infusing AI into support mechanisms, it’s not just about innovation—it’s about unparalleled efficiency. The amazing processing capabilities of AI surpass those of humans, analyzing vast quantities of data in the blink of an eye. Take AI chatbots as an example: they can rapidly comb through terabytes of data, referencing solutions from millions of past queries. The result? The most common concerns get resolved in real time. This swift intervention allows human agents to channel their expertise toward more complex issues. Compared to traditional customer service processes for which agents might spend minutes or even hours or days searching for answers or escalating issues, the speed of AI-powered systems offers an almost incomparable advantage in timeliness and effectiveness.
Cost-Effectiveness
At first glance, the investment in a state-of-the-art AI customer support system might seem substantial, but viewing it as a long-term strategy rather than just an immediate cost is crucial. According to IBM’s Watson blog, the data reveals that businesses could potentially reduce their customer service costs by as much as 30 percent by implementing conversational solutions, such as chatbots.1 These AI tools can handle 80 percent of routine tasks and customer questions.
When assessing the true value, consider the operational differences. Human employees, as invaluable as they are, come with inherent limitations. They require benefits, occasional time off, continuous training, and are bound by a conventional workweek. In contrast, AI operates on a different spectrum. It can operate 24/7, unconstrained by working hours, illness, time zones, public holidays, or weather disasters. In practical terms, this continuous operation equates to the output of about 4.2 full-time human agents every single week, minus the breaks or downtimes. Spread this efficiency over a year, and the cumulative cost savings and enhanced productivity start to paint a compelling picture. This perspective makes the upfront AI investment justifiable and a wise move for future-proofing your customer support strategy.
Personalization and Data-Driven Insights
AI algorithms are designed to process and interpret data at a scale that’s simply unattainable for human analysis. Every day, countless bytes of data flow through customer support systems. To put this into perspective, imagine going through a library that adds thousands of books every hour; this is the amount of data businesses often handle. AI can efficiently peruse this “library,” pinpointing patterns and intricacies that might elude even the most skilled analysts. Whether it’s recommending a product in an e-commerce platform based on a customer’s purchase history, adapting responses based on sentiment analysis from past interactions, or tailoring learning modules for students based on their learning habits, AI-driven personalization isn’t just an incremental improvement—it’s a paradigm shift. This level of detail, applied consistently across millions of users, transforms decision-making processes. Businesses can segment their customer base with surgical precision, ensuring every interaction feels bespoke. The outcome is a significant elevation in customer satisfaction because interactions become less about generic support and more about individualized, insightful experiences.
Scalability and Consistency
In the modern digital age, where support interactions can range from hundreds to millions daily, AI provides the bedrock on which businesses can scale and expand their customer support operations without a proportional increase in human resources. To understand the sheer magnitude, consider this: while a human agent may efficiently address a single customer query at a time, AI systems can simultaneously process and respond to thousands of inquiries without batting a digital eyelid. And they do this around the clock. Imagine the operational capacity of a 1,000-person call center condensed into a single AI system that never sleeps, takes breaks, or experiences fatigue or stress. This unparalleled scalability ensures that businesses can cater to a growing customer base without sacrificing the quality or speed of service. Moreover, AI algorithms perform with a level of consistency and precision that is difficult for human agents to maintain, especially in high-pressure, emotional, or repetitive scenarios, irrespective of the volume or complexity of tasks.
Reducing Time to Resolution
In the dynamic landscape of customer support, every second counts. Studies have shown that a mere 10-second delay in website load times can make users leave, so imagine the impact of delays in customer service. The quicker an issue is resolved, the better the customer experience. AI systems don’t just shave off a few seconds; they revolutionize response times. Consider this: where a human agent might take several minutes to look through a customer’s history, identify patterns, and suggest solutions, an AI system can achieve this in mere milliseconds. In fact, Boston Consulting Group (BCG) estimates that implementing generative AI at scale could increase productivity in customer service operations by 30–50 percent or more.2 Guiding customers through troubleshooting steps, AI can autonomously navigate and decide on the most efficient route based on vast datasets, effectively minimizing time-consuming back-and-forth interactions. With every tick of the clock vital to a great customer experience, AI’s ability to expedite resolutions can make the difference between dissatisfied customers and loyal advocates.
Multi-Channel Support
Modern consumers seek support through multiple channels, including email, phone, social media, and web chat. In today’s fast-paced digital world, customers are hopping from one channel to another, like browsing TV channels. Keeping up with this can be a bit of a juggling act for businesses. Many have tried the multi-channel approach, thinking they’ve got it covered. But here’s the catch: Today’s customers are looking for more than just multiple channels—they want those channels to talk to each other, giving them a smooth and seamless experience. Let’s examine these two strategies.
Multi-channel experience
This strategy involves businesses engaging with customers across multiple platforms (such as social media, email, phone, and chat). Each channel operates in isolation, meaning the customer might get different experiences or would have to reiterate their issues when switching from one channel to another.
In this strategy, AI plays a pivotal role in enhancing each channel’s strengths. Deploying AI-driven chatbots or virtual assistants tailored for specific channels like email, chat, or social media can provide quick, accurate responses. Additionally, AI’s capability to independently analyze user behavior on each channel helps businesses tap into channel-specific preferences and trends. This granular data analysis can be immensely valuable, especially in channels bombarded with queries, as AI can automate responses to frequently asked questions, ensuring timely and consistent customer engagement.
Omni-channel experience
This approach focuses on delivering a seamless and consistent experience across all platforms. The emphasis is on integrating the different channels to unite the user experience. When a customer transitions from, say, a mobile app to a phone call, the information flows seamlessly between the two, and the customer doesn’t have to “start over.”
In contrast to multi-channel, the omni-channel strategy demands a seamless customer experience across all platforms, and AI is the linchpin in achieving this. By integrating data platforms, AI can ensure that information flows smoothly across all touchpoints. Imagine a scenario in which a customer transitions from chatting with a bot on a website to speaking with a human agent over the phone. With the help of AI, this switch is seamless—the agent is already equipped with the chat history, context, and customer details. Beyond this, AI’s power of predictive analytics personalizes the customer’s journey, anticipating their needs and preferences. And when it comes to understanding the customer’s holistic journey, AI-driven unified reporting offers businesses a comprehensive view of interactions, regardless of the platform.
Customers today are savvy. They’re not just looking for multiple channels to utilize; they’re looking for an experience that delivers results efficiently. Nobody wants to explain their issue over and over every time they switch from, say, chat to phone. Both multi-channel and omni-channel strategies have their merits. While diving headfirst into multi-channel might seem like a good start (and sometimes it’s a necessary first step), it can lead to some redundancies and inefficiencies.
Think of it like having several remote controls for various devices—each one does its job, but you’re often left fumbling between them. In our analogy, omni-channel is the universal remote. With AI powering this approach, not only do all the channels come under one unified strategy, but the data flow is also smooth. This means businesses get a 360-degree view of their customers, leading to richer insights and more tailored services.
As the digital landscape changes, adding a new channel to an omnichannel setup (backed by AI) is like adding a new device to your universal remote. It’s integrated smoothly without causing disruptions. Sure, setting up this integrated strategy might be a bit pricier initially than just juggling multiple remotes (or channels). Still, the investment pays off with happier customers, streamlined operations, and insights that can shape the business’s future.
From Reactive to Proactive, Preventive, and Preemptive Support
Let’s rewind a bit and think about the traditional customer service model. Picture a customer stumbling upon an issue, dialing up the company, waiting, and then—fingers crossed—gets their issue resolved. It’s a model that’s been tried and true but is also reactive. Fast-forward to today when AI is flipping this script in dramatic new ways. Instead of just reacting, we’re entering a world where AI allows businesses to be several steps ahead of customer issues.
Imagine a setup where AI sniffs out potential hiccups and starts working on solutions before the customer even knows there’s a problem. This is proactive support. Then, there’s preventive support where AI, with its machine learning prowess, doesn’t just wait for issues to pop up; it predicts them and sets up defenses, ensuring the problem doesn’t even rear its head. And the cherry on top? Preemptive support. Here, the AI isn’t just predicting issues; it’s seeing them on the horizon, darting forward, and fixing them autonomously. No fuss, no drama. For the customer, it feels like cruising on a smooth highway. All these layers, woven together by AI, are redefining the very essence of customer support, leading us to a future where interactions are not just about problem-solving but about preventing problems in the first place.
As game-changing as AI is in customer service and its notable advancements, it’s crucial to understand its role: AI is an enhancer, not a substitute for human intelligence and empathy. Think of AI as the GPS navigator in a car, assisting the driver who remains in charge. In an optimally structured customer service framework, AI assists human agents, helping the agent as they address complex and nuanced situations. Together, they pave the way for an efficient, effective, and empathetic customer experience.
The Craftsmanship of Empathy: Humanity in the Machine Age
As great a navigator as AI can be, humans are so complicated that relying on AI algorithms to fully comprehend and accurately predict outcomes in every situation is unrealistic. Human interaction, with empathetic understanding, is valued by customers and can be a key differentiator in the overall customer experience. AI’s role is to quickly and deeply analyze large amounts of data, offering actionable insights that enable humans to focus on those unique human skills of active listening, demonstrating empathy, and problem-solving to customize solutions for each customer.
Following our deep dive into the business perspective and the advantages AI brings to the table, it’s pivotal to spotlight the enduring role of human agents in customer support. Even in an increasingly automated world, their significance in the customer service sphere remains undiminished. Let’s demystify together the belief that AI will completely take over the role of human agents in the service and support world and all the good reasons why AI is best positioned as the assistant, guiding, and supporting, while humans remain firmly in the pilot’s seat, steering the journey.
It’s About the People, Not Just the Numbers
While it might be tempting to think of AI solely as a way to cut costs and increase efficiency—although those are nice perks—it’s far more than that. At its core, support is about people and genuine human connections. It’s about that mom trying to install a baby monitor who’s lost in technical jargon. It’s about the college student miles away from home and in a different time zone needing instant support for a malfunctioning laptop. AI helps us reach these people when and where they need assistance, offering a helping hand guided by data but warmed by human empathy and ensuring every interaction feels smart and heartfelt.
Time, the Most Precious Gift
We’ve all been there—waiting on hold for what feels like hours, listening to soul-draining hold music, and finally speaking to an agent who has to shuffle through pages or screens of information before helping us. AI cuts through these delays, offering instant, accurate assistance for straightforward issues and freeing up human agents to assist you with more complex matters. The gift of time is one of the most precious things customer service can offer, and AI is a tool that helps give you just that.
Meeting You Where You Are
The essence of customer service lies in its ability to adapt to diverse preferences and needs. Different individuals naturally gravitate toward different communication channels—some may lean toward the immediacy of chat, while others may opt for the formality of email, the ubiquity of social media, or the familiarity of a voice call. AI steps in like a maestro, orchestrating this multifaceted concert, adjusting and refining each note (or interaction) to the rhythm of individual preferences. Yet, while AI offers precision, the human touch brings soul to this symphony. Once engaged in customer interaction, human agents add layers of understanding, empathy, and genuine connection, transforming each AI-enhanced interaction into more than just a transaction—it becomes a personalized and meaningful exchange.
The Nuance of Insight
At the heart of effective communication lies the ability to perceive and understand another’s intent. For example, you might wonder how Natural Language Processing (NLP) pinpoints intent within customer interactions. Well, it’s an intricate blend of science, vast data sets, and a touch of linguistic finesse. Initially, NLP evaluates the words we express. For example, sentiments such as “thrilled” or “love” typically suggest positive feelings, whereas terms like “frustrated” or “disappointed” denote negative ones. By accumulating extensive databases of these words and their associated undertones, AI systems can gauge the sentiment embedded within a text. But the process dives deeper than mere word choice; the arrangement of those words plays a pivotal role. The structure or length of a sentence can often provide subtle hints about a customer’s feelings like short, sharp sentences potentially indicating annoyance. Furthermore, for voice-driven interfaces, the latest NLP tools can assess the tone and pitch of spoken words. A heightened pitch might signal distress, while gentler tones could imply satisfaction or calm.
Here’s where it gets super useful in the customer service and support ecosystem. Once the AI has these intention insights, they’re distilled into actionable feedback for human agents. This isn’t about replacing the human touch—it’s about enhancing it. Imagine a support agent getting real-time prompts like: “the customer seems agitated” or “the customer appears to be pleased with the solution.”
By offering these nuanced insights, AI acts like an assistant, guiding and augmenting human agents. This ensures that every interaction isn’t just about resolving an issue; it’s about truly connecting, understanding, and empathizing with the customer. The outcome? A more human-centric approach to customer support, enhanced by the precision and consistency of AI. In other words, AI ensures that the customer’s needs are acknowledged and genuinely understood.
The Interplay of AI, RLHF, and RLAIF
Contrary to some beliefs, artificial intelligence does not operate in isolation. Its accuracy, efficiency, and relevance are deeply intertwined with human collaboration, particularly through the lenses of Reinforcement Learning from Human Feedback (RLHF) and its complementary counterpart, Reinforcement Learning from AI Feedback (RLAIF). RLHF and RLAIF are instrumental in the training and fine-tuning of large language models, the engines behind breakthroughs like OpenAI’s ChatGPT,3 Google DeepMind’s Gemini4 and Sparrow,5 Anthropic’s Claude,6 Meta’s Llama,7 and more. Instead of training large language models to predict the next word, we train them to understand instructions and generate helpful responses.
In a nutshell, RLHF represents a bridge between human intelligence and artificial intelligence, allowing developers to refine and align AI systems more closely with human expectations and societal norms. At its core, when a chatbot furnishes an answer, it’s not merely spewing pre-programmed responses. Instead, that reply is the culmination of countless data points, refined and calibrated using human feedback. The model learns from raw data and the nuances and subtleties only human expertise can provide. Think of it as a student–teacher dynamic, where AI is the ever-eager student and humans are the seasoned mentors. This symbiotic relationship ensures that every interaction with AI reflects algorithmic acuity and human-guided judgment, cementing its role in optimizing customer service. The role of RLHF cannot be underestimated—it’s this very process that bridges the gap between cold computational logic and the warmth of human intuition, ensuring AI remains relevant, responsive, and reliable.
This approach has been crucial in making large language models (LLMs) more attuned to what humans want and expect. However, the challenge with RLHF lies in its appetite for human-generated feedback, which is both time-consuming and expensive to gather. This is where RLAIF comes into play, harnessing AI-generated feedback to guide learning. This method shines in environments where the complexity or sheer scale makes human feedback impractical or where precision and consistency are paramount (RLAIF can simulate millions of scenarios, providing feedback that’s mathematically aligned with desired outcomes—something incredibly challenging for humans to offer at such scale and specificity). RLAIF excels in domains where the feedback loop benefits from rapid iteration, consistency, and a level of detail that human oversight cannot ubiquitously provide. For a deeper technical exploration of these concepts, see Chapter 2.
This blend of human and machine intelligence doesn’t just enhance AI’s capabilities; it redefines them, ensuring that as AI models evolve, they do so in a way that is fundamentally aligned with human values and needs. The integration of RLHF with RLAIF ensures AI remains technologically advanced and deeply attuned to the human experience, making it an invaluable asset in fields demanding nuanced understanding and responsiveness—like customer service.
The Ethical Imperative
While the power of Artificial intelligence is undeniable, it also carries a profound ethical responsibility. The utilization of AI to discern and anticipate the customer’s needs must be counterbalanced by a non-negotiable commitment to data privacy and individual autonomy. For this reason, the integration of AI in customer service needs to adhere to rigorous ethical standards designed to uphold the customer’s privacy and safety. In this landscape, the technology serves not merely as a tool for efficiency but as an extension of the ethical commitment to respecting the customer’s individuality and rights.
Reinforcement Learning from Human Feedback provides a powerful method for improving AI systems, though it can also be a double-edged sword. On the one hand, it allows AI to adapt to human nuances, making it more attuned to our needs and behaviors. On the other, it can inadvertently absorb biases in the feedback it receives, highlighting the importance of ensuring that the humans providing this feedback are trained and aware of potential biases and that there are measures in place to correct these biases. Engaging a diverse group of reviewers in the feedback process is crucial to minimize biases in AI systems trained via RLHF. This diversity should span various dimensions such as gender, ethnicity, age, and cultural background, ensuring a broad range of perspectives and experiences are considered. By incorporating inclusiveness in the feedback loop, the AI becomes more representative and aligns more closely with the principles of Responsible AI, potentially mitigating the propagation of existing biases and fostering more equitable outcomes.
Moreover, with AI models diving deep into customer behaviors and sentiment, there’s a risk of violating personal boundaries or misusing data. This makes it vital for AI in customer service to operate under rigorous ethical standards, prioritizing customer privacy and safety. It’s a world where technology doesn’t just serve as a beacon of efficiency but stands firmly as a sentinel, ensuring that every interaction respects a customer’s individuality, rights, and values. In this evolving landscape, responsible AI is not just an ideal—it’s the foundation that upholds the trust and integrity of every AI-human engagement.
As we advance into the digital age, we must not lose sight not lose sight of what’s genuinely important—the human connections that enrich our lives. AI in customer service is not about sidelining these connections but about nurturing and making them more accessible and effective. With a thoughtful approach, we can ensure that AI is a bridge between people, not a barrier.
Symbiosis Over Supremacy: The Steering Wheel of Strategic Balance
Harkening back to our example of the Industrial Revolution, we learned that machines alone didn’t produce the most effective results. It was the combination of both human labor and machines that propelled entrepreneurs to success. These same learnings can be applied to the customer service industry of today, where it’s not just AI or humans alone that create that great customer experience, trust, and, ultimately, loyalty. The balanced strategy of combining the empathic human touch with the precision of AI will create the greatest benefit to both customers and organizations. The combination of humans and machines will be better than either individually. This strategic balance is as critical today as the invention of quality checks and safety measures during the Industrial Revolution.
Creating a Seamless Customer Journey
The promise of a seamless customer journey is amplified exponentially with the incorporation of AI into customer service. With AI, businesses can ensure that customers receive the right information at the right time, thus minimizing friction and enhancing overall satisfaction. Advanced algorithms can predict common queries based on past interactions or frequently asked questions, offering customers solutions even before they realize they need them.
For instance, Spotify leverages AI to curate personalized playlists, ensuring users continually find music they love without searching for it.8 Similarly, Amazon’s recommendation system uses AI to suggest products to users based on their browsing and purchase history, thereby streamlining their shopping experience.9 Companies like Zara employ AI to manage inventory better, ensuring that popular items remain in stock and immediately available to customers, thus reducing wait times and potential dissatisfaction.10 Then there’s Sephora, which uses AI-powered chatbots to provide personalized product recommendations, enhancing the user’s shopping experience.11
In essence, AI doesn’t just ensure the customer’s journey is smooth and anticipates their needs, making each interaction feel personalized and intuitive. This proactive approach minimizes friction and elevates overall satisfaction, as customers feel understood and valued right from their first interaction.
Leveraging Data for Real-Time Decision-Making
AI systems can analyze enormous volumes of data to make real-time decisions that enhance customer experience. Whether it’s using machine learning to prioritize support tickets based on urgency or analyzing customer feedback to make instant adjustments, AI can transform a static customer service model into a dynamic, responsive mechanism.
For example, Netflix utilizes AI to analyze viewer habits and preferences, adjusting content recommendations on the fly and ensuring that users continuously discover shows and movies that align with their tastes, leading to prolonged engagement and satisfaction. Furthermore, airlines like Delta now employ AI-driven systems to manage and predict potential flight delays. They can proactively notify passengers about potential changes by analyzing real-time weather patterns, air traffic, and aircraft status, ensuring smoother travel experiences. Ride-hailing services like Uber and Lyft also capitalize on AI to make real-time pricing decisions, analyzing factors like demand, traffic, and local events to set fair and competitive fares.
AI’s real-time decision-making capacity is revolutionizing the landscape of customer support. By leveraging AI, support teams can instantly prioritize tickets, swiftly suggest responses based on past interactions and, combined with real-time performance monitoring and resource allocation, pave the way for a smoother and more responsive customer experience. Pioneering companies like Zappos and Amazon have tapped into these AI-driven benefits, witnessing a substantial boost in their response times and overall customer satisfaction.
In essence, AI optimizes a business’s operational facets and ensures that customer interactions are timely, relevant, and tailored. It shifts the paradigm from a one-size-fits-all approach to one where each customer feels the system is attuned to their immediate needs and preferences.
Enhancing Upsell and Cross-Sell Opportunities
AI-powered recommendation engines have significantly reshaped the landscape of upselling and cross-selling, and the numbers speak for themselves. According to a McKinsey study, companies utilizing AI for recommendations have seen sales increase by 15 percent to 20 percent while simultaneously enjoying a reduced churn rate.12 Amazon, a leader in the domain, attributes as much as 35 percent of its sales to its AI-driven recommendation system.13 Furthermore, Netflix’s recommendation engine, aimed at keeping users engaged with relevant content, saves the company approximately $1 billion annually by reducing churn.14 These engines dive deep into customers’ purchasing history, search queries, and even browsing duration to curate tailored suggestions. By presenting these at opportune moments, businesses not only witness a surge in revenue but also cultivate a more personalized relationship with their customers. In effect, AI intelligently links customer needs to a company’s broader product or service offerings, maximizing value on both ends.
Automating Routine Tasks
AI can significantly reduce the workload on human agents by taking over routine and repetitive tasks. Incorporating AI into routine operational tasks is already proving invaluable in streamlining customer service.
According to a report by McKinsey, by automating manual and repetitive tasks, successful operations centers are reducing costs by 30 to 60 percent while increasing delivery quality.15 For instance, e-commerce multinational Shopify highlights in their blog that AI-powered forecasting in supply chain management can reduce errors by up to 50 percent, lessening lost sales and product unavailability by up to 65 percent.16 OpenTable uses AI to manage millions of restaurant reservations, allowing restaurant staff to focus on providing exceptional in-person experiences.17 And when processing claims, AirHelp assists over 16 million passengers experiencing canceled, overbooked, or delayed flights—its chatbot acts as the first point of contact for customers, improving the average response time by up to 65 percent.18 It also monitors all of the company’s social channels (in 16 different languages) and alerts customer service if it detects crisis-prone terms used on social profiles.
Microsoft has developed a service called “Copilot for Service,” which is designed to assist customer service agents with generative AI features. In Microsoft’s words, “Copilot for Service accelerates agent onboarding, and case resolution improves efficiency and automates tasks to help free agents to focus on customers. Embedded in their desktop of choice or deployed in Teams, agents can simply ask the generative AI-powered Copilot questions in natural language to receive relevant answers that leverage existing contact center knowledge to enhance agent productivity and customer satisfaction.”19
With these mundane tasks efficiently handled by AI, human agents can dedicate their expertise to areas that require in-depth problem-solving and human touch, greatly enhancing the overall quality of customer interactions. This seamless blend of machine efficiency with human empathy fosters an environment where both agents and customers benefit.
Fostering Continuous Learning and Improvement
Machine learning algorithms thrive on data; the more they process, the smarter they become. As a result, these algorithms can identify trends, patterns, and areas of improvement that may not be immediately apparent to human analysts, leading to a continuously improving customer service experience.
This immense data-crunching capability has led to breakthrough insights in customer service. For instance, Netflix uses machine learning to analyze viewing habits to predict and recommend shows users might enjoy next, resulting in a 75 percent selection rate from its recommendations.20 Similarly, Amazon’s recommendation system, also based on machine learning, drives 35 percent of its total sales by suggesting products based on users’ browsing and purchasing patterns.21 In the world of customer support, tools like Zendesk’s Answer Bot22 harness these algorithms to understand customer queries better, directing them to the most relevant solutions.
Fueled by algorithms, this continuous evolution ensures that customer service and support are responsive and predictive, anticipating needs even before customers articulate them.
Global and Cultural Scalability
AI systems can be trained to be more responsive and predictive and understand multiple languages and regional dialects, allowing companies to offer high-quality customer support that transcends geographical and linguistic barriers. This is particularly useful for businesses looking to expand globally without a proportional increase in support staff.
For instance, Microsoft Translator, a product of its Azure AI suite, currently supports real-time translation in more than 80 languages and dialects, empowering businesses to interact seamlessly with a global clientele.23 This gives human agents the added power to utilize AI to help translate real-time conversations, essentially adding greater understanding to any support experience when the customer and agent speak different languages. Beyond just translation, tech giants like Google are fine-tuning their voice assistants to recognize and understand regional accents, ensuring that a user in Texas is understood as clearly as one in Tokyo or Toronto. Such capabilities enable companies to maintain a consistent and high-quality support experience worldwide without exponentially increasing their human support teams. This multilingual skill of AI is invaluable for businesses eyeing global expansion, as it facilitates a culturally sensitive and locally resonant customer interaction, irrespective of where the customer or agent is located.
One example of implementation in customer support is Spotify, which leverages AI in language translation to cater to its international user base.24 The company faced challenges in providing support across multiple languages, primarily handled by its European service centers. To address this, Spotify partnered with Sutherland to develop a real-time language translation tool, Sutherland Translate AI, for its email support channel. This tool uses AI, machine learning, and translation engines from Google, Microsoft Azure, and Deep Learning to translate real-time customer queries, preserving cultural nuances and details. This solution has enabled Spotify to support customers in various languages, including Arabic, French, Russian, Polish, Turkish, German, Dutch, Portuguese, and Spanish, without significantly expanding staff or increasing operating expenses. Implementing this AI-driven tool has enhanced Spotify’s capacity to handle customer queries in different languages, allowing for more efficient business operations and customer service delivery.
Crisis Management
AI’s capability to manage a large volume of interactions simultaneously is particularly beneficial during times of crisis or high demand. For example, during a product recall or major outage, AI systems can handle the sudden influx of customer queries more efficiently than human agents, offering quick, accurate information and reducing customer anxiety and frustration.
In 2016, Samsung faced a massive recall of their Galaxy Note 7 smartphones due to battery issues that led to some devices catching fire.25 While there is no documented evidence of using AI-powered chatbots during this crisis, customer support systems played a crucial role in handling the surge of inquiries from concerned customers. Let’s picture the scenario and how chatbots could have played (maybe they did) a major role. While Samsung’s human support agents could focus on critical cases and complicated issues, their AI systems could manage a significant portion of basic queries such as:
“How do I return my phone?”
“Where’s the nearest service center?”
“How do I get a refund?”
Doing so would ensure that customers receive prompt responses even during peak inquiry times. Moreover, using AI’s data analysis capabilities, Samsung could quickly identify frequently asked questions and update their chatbot responses in real-time, ensuring that accurate, up-to-date information was always available to their customers. This quick and efficient communication would help reduce customer panic and frustration during a challenging period for the company.
By promptly addressing and assuaging customer concerns during such high-stress times, AI plays a pivotal role in mitigating potential reputational damage and preserving brand trust. In such scenarios, AI’s ability to process vast volumes of interactions without being overwhelmed or fatigued offers businesses a distinct advantage in ensuring consistent customer satisfaction, even under pressure.
Enhancing Employee Satisfaction
Contrary to fears about AI taking over human jobs, well-implemented AI can actually enhance job satisfaction among customer service agents. By automating repetitive tasks, AI allows agents to focus on problem-solving and customer engagement, roles that offer more job satisfaction and opportunities for skill development.
A Forrester Research study indicated that tasks automated through AI can free up to 20 percent of a customer service agent’s time.26 By automating repetitive tasks, AI allows agents to shift their focus to more rewarding and crucial activities. IBM has reported that their AI system, Watson, helped reduce the resolution time of customer service inquiries by 10 percent, allowing human agents to spend more time on in-depth customer interactions.27 It’s clear from these examples that when businesses purposefully integrate AI, they not only enhance the efficiency of their operations but also contribute to a more fulfilling work environment for their people, resulting in better service and happier employees. Quoting Simon Sinek once again, “Customers will never love a company until its employees love it first,”28 so getting your employees to find purpose and satisfaction in their jobs by embracing all the perks that AI can bring to them becomes an enabler for achieving business results and higher levels of customer satisfaction.
While AI’s capabilities are truly transformative, it’s crucial for businesses to approach its integration with a balanced perspective. AI should augment, not replace, the human touch that remains vital for addressing complex issues and customer needs. When implemented thoughtfully, AI can act as a powerful tool that elevates customer service to new levels of efficiency, personalization, and effectiveness.
As we venture deeper into this new age, let this historical parallel illuminate our exploration. The Industrial Revolution wasn’t just about machines; it was about leveraging the best aspects of both human skill and mechanical efficiency. Similarly, the current revolution in customer service and support is not about choosing between AI and human expertise—it’s about integrating them in a way that amplifies the strengths of each.