The application of a "technology" in a restrained "field" is assessed based on different factors — from the speed of adoption and distribution to perceived value and associated Quality of Service (QoS). Aspects such as cost efficiency, innovation, ease of use, reliability, scalability, etc., also contribute to the idea of a technology aptly befitting the social context.
And like most technologies that have stamped themselves in society (in disparate fields), Artificial Intelligence has "established" a strong case for its capabilities in heightening the notion of super-effective and optimized customer care.
In a paper dating back to 1992, Debra Logan and Jeffrey Kenyon from Carnegie Group Inc. elaborated on the software solution named "HelpDesk" and how it drove the assimilation of conventional architectures and AI technology to streamline the "administrative, diagnostic, and [referencing]" processes related to customer support. Again, this was 29 years ago.
So, to say that AI is (still) the tech for the future would be a little illogical. Obviously, there's a lot to explore in this sector. Nevertheless, as it turns out, AI has started moving out of the research labs and into customers' everyday lives and habitats. It is pretty evidently today's technology and must be viewed in that light when thinking about leveraging it for your business.
Having said that, we must understand that AI in customer care is not a matter of "if." It's a matter of when, where, and how you will make it work for your organization. And this guide, we hope, will help you navigate through this journey and give you a fair idea about the scope of Artificial Intelligence in customer care.
The Case for Artificial Intelligence in Customer Care
Proceeding with what has been said earlier, AI in customer care can optimize and enhance customer experience and efficiency. The proliferation of digital channels and devices has given rise to an explosion in digital customer care interactions. With AI, customers can now get far more from companies in a better and faster way.
That said, artificial intelligence for customer care entails the elaboration of five distinct themes.
1. AI for Enhancing Customer Experience
2. AI for Enhancing Agent Experience
3. AI for Optimizing Company's Overall Support Process
4. The Top 4 AI Applications in Customer Support
5. Future of AI & Customer Support
Let's take a closer look at each of the above.
PART 1 - AI for Enhancing Customer Experience
We have seen AI for Customer Care in a myriad of brands and services focused on the improvement of customer experience. There are banks, airlines, mobile networks, online marketplaces, etc., all offering digital assistants like Siri or Alexa to interact with customers.
Many companies use chatbots to increase customer engagement by providing real-time information and insights unavailable through other channels. As a matter of fact, with AI-powered solutions, businesses can unlock the potential of their various channels and develop/tailor customer-specific improvements.
But first things first, why is it so important to shift to AI when customer service can be improved with the help of existing resources? The answer to this lies in the immense consumer expectations sourcing from digital channels.
The Sprout Social 2021 Index reveals that consumers from all walks of life use social media to interact with brands. In fact, 47% of consumers believe that companies that provide strong customer service and consistently engage with their audience are the "best in class on social." However, even after learning all about the capabilities of utter responsiveness across digital channels, brands often find it challenging to keep up with the quality of customer care.
The same index sheds light on the discrepancy between what the customers think and what the marketers attempt to achieve. For example, compared to 47% of consumers labeling quality customer service as the #1 brand quality, only 35% of marketers label it as an essential factor while placing it at the #5 position. Not only this, almost all the inbound queries (concerning different industries) have a significantly lower response rate.
A concrete example demonstrating this would be the 47% response rate of advertising and marketing agencies to an average of 68 inbound queries that they receive per day. Amongst all the other sectors, one would think that the marketing sector would be the most interactive, but that's certainly not the case here. And there can be several reasons behind this discrepancy.
—Maybe the agency doesn't employ enough agents to handle the influx of incoming calls, or the automated system simply doesn't possess the capacity to respond to such queries.
—Maybe most questions are redundant or irrelevant to a marketing company; a person on a call might ask about certain brands under the agency's roster only to get more information later.
—The agency might be busy with other projects or projects as of a higher priority and can't make time for handling extra queries.
—Perhaps agencies don't have a CRM system that can easily aggregate such information and deliver real-time responses without compromising on time or quality.
What does this all point to? — That there's immense scope of improvement in the customer service domain. This scope, as it stands, can be only driven by the inclusion of a disruptive technology that could streamline the process and help the company leverage the existing infrastructure in the best possible way. Yes, it doesn't necessarily mean that conversational AI solutions can (or should) replace marketing agencies. In fact, the adoption of AI indicates that they can be improved and streamlined through automation — all in favor of improving customer experience.
1.1 AI for Optimizing the Customer Journey
Daniel Newman on Forbes writes that the customer journey is no longer linear. And at SupportGenie, we have observed just that. Often, a group of customers scroll through our customer support solutions and instantly decide to make a purchase. Alternatively, some of them create and maintain a free account, only to make a purchase at a later date. They never really leave our website but still might need to interact with the customer care team.
These customers are typically more sensitive to the quality of information they receive and want a seamless experience across all channels. And then, there are digital natives who have shifted from making purchases in brick-and-mortar stores to buying products/services online. More often, they research and compare products/services before coming to a decision.
The point is that all customers create their own path, leaving trails in the form of millions of data points for businesses to follow.
That being the case, it's only natural for businesses to plug into different digital channels and try to stitch a more meaningful, personalized, and seamless customer map — one that doesn't depend on a fixed path from point A to B.
Naturally, this might be a bit too onerous, but that's where AI comes into the picture. With the help of AI, companies can automate the process of reorganizing customer journeys for every individual customer.
a) Listening to What Customers Have to Say
Data is paramount but what's even more predominant is the knowledge about leveraging that data. To begin with, in order to gauge the customer's journey, one must start by listening to what people have to say. Businesses can do this by tapping into different data channels and using the power of AI to make sense of that data.
Sometimes, it might just be a matter of picking up on specific keywords and analyzing the customers' pain points. In other cases, it might involve learning from prior interactions that took place via chat or call center agents.
Tools such as SupportGenie's AI Conversational Chatbots are built to analyze conversations in real-time. While constantly improving in their ability to chat, they can surface the most critical conversational touchpoints from their observations and gauge sentiment, intent, etc. And the great part about this whole process is that:
i. It does not require any additional effort from the agents/marketers/entrepreneurs, and
ii. The customer data remains secure and confidential.
"Thanks to natural language processing, a chatbot can identify keywords in a customer's query and respond in kind with a coherent message, one often formulated by drawing on a sophisticated database" — Yingzi Xu in Australasian Marketing Journal.
b) Defining Customer Personas for Customized Experiences
Let's say the automated insights gleaned from analyzing conversations reveal that a customer has an urgent need for support in order to configure hardware. Using such insights, businesses can start by segmenting customers into three categories.
i. A customer who wants to purchase hardware but needs help configuring it.
ii. A customer who has previously purchased hardware and is now in need of help configuring it.
iii. A customer who has previously purchased hardware and is simply looking for self-help options to make things easier on them.
The facilitation of customized experiences reflects upon the proactiveness and demonstrates the empathetic face of the brand.
c) Defining Customer-centric Strategies
With customer personas now defined, businesses can move on to defining customer-centric strategies. All it takes is a bit of creativity, some forward-thinking and contextual knowledge to come up with a strategy that not only aligns with the business goals but also caters to the needs of customers.
For example, in 2020, one of our long-term customers began their journey towards improving customer experience by simplifying support processes, reducing resolution times, and developing new tools for their marketing team. They started out by implementing Chatbots and then added several other customer service channels such as Facebook Messenger, Twitter, and WhatsApp. They also developed a mobile app that lets customers quickly access their support data — all in real-time.
1.2 AI for Measuring Customer Satisfaction
In order to understand whether the customers are satisfied or dissatisfied, one must first define what "satisfied" means for a particular brand. For instance, a customer who recently bought a new laptop might be less likely to rate their experience negatively than someone who is facing technical issues with a gadget that they purchased three years ago.
The key here is to use AI and machine learning models to derive insights from the data points in question. For example, an AI developer might be able to apply a "Customer Satisfaction Index" tool to compute customer satisfaction scores. Or they could come up with a "Customer Satisfaction Map" and visualize that across the customer base.
Simply put, one might want to use an AI model to calculate customer satisfaction scores on a more granular level and then visualize that across the customer base. One way to do that is to ask for customer feedback via, say, AI conversational chatbots.
These feedbacks could be based on a 5-point Likert scale (anything that could lend a quantifiable insight into the customer's emotions). These scores could then be mapped to find out the three top areas where businesses could focus their energies on improving customer satisfaction.
The point is that when an AI model is deployed to measure customer satisfaction, enterprises can expect more accurate insights and formulate customer-centric strategies.
PART 2 - AI for Enhancing Agent Experience
Often, the talk around AI improving the customer support domain is limited to enhancing the customer experience, and understandably so. However, it wouldn't be wrong to say that optimizing the agent experiences is the prime driver for the altogether effectiveness and Quality of Service (QoS) expected out of a brand's customer care.
Naturally, at the core of every business lies the need to be effective and efficient in serving their customers. Nevertheless, the definition of "efficiency" differs for different businesses. What's ultimately necessary is that customer support agents must manage to assist as many customers as possible in the shortest amount of time possible.
In the best-case scenario, customers are able to get in touch with agents via phone, email, and chat and resolve their issues. However, at times, agents are unable to deliver the appropriate responses — this can be due to knowledge- or experience-gap, amongst other reasons. Such scenarios can be frustrating both for the customers and the agents.
To address this critical issue that plagues every business across the world — and one that has no fail-safe solution — it makes sense to deploy AI solutions into the support domain. Such AI solutions could include chatbots that can field basic customer queries or automated email responders that can handle the most common questions and requests.
One of our long-term partners has documented their journey towards improving agent efficiency. They have deployed chatbot solutions into two different channels: website and mobile application. As it turns out, to field customer inquiries via these channels, the agents only require a few hours of work every day.
In fact, when chatbots were first deployed into the channels, the number of requests was reduced by 80%. As a result, the team was able to spend more time on the well-performing channels instead of trying to handle unanswered calls, which otherwise resulted in a loss of productivity.
2.1 AI for Optimizing Agent Workflow
It is essential to understand that the inclusion of AI doesn't imply direct "replacement." Yes, some tasks would be lifted off the shoulders of customer support representatives but only to equip them with the jobs that entail creativity, innovation, and decision-making.
Salesforce's analysis of 3,500 customer care representatives reveals that 51% of the agents working without AI agree to the fact that they have to hustle through mundane tasks all day. Almost 70% of the high-performing representatives believe that AI can help with managing the workload.
To that end, one pain point that has persisted in the customer service industry is the need to manually check tickets which can often take hours to a day or longer. With customer services being a high-priority concern for businesses, it might not be possible to end up with all the agents working on the right tickets. Naturally, this leaves them susceptible to losing customers as they continue to experience delays and miscommunication.
Using AI for workload analysis, businesses can start by inclining towards workflow mapping. This is done by creating workflow rules and using an AI engine to map agents in real-time to the tickets that require their attention. This engine can also re-prioritize tickets automatically as needed.
Let's now dive deeper into how AI helps customer agents optimize their workflow.
a) The ability to leverage past issue-resolution data
As mentioned above, data is everything in customer service. The more data that agents have access to, the more effective they can be at their job. Nevertheless, it is challenging for customer service agents to derive insights from various data sources such as previous issues, customer preferences section, or account-related information.
One way of helping them with this is by deploying AI chatbots in the support center. With a chatbot, agents can access all the information they need in one place without having to jump from one screen to another. Another method is to help them with a data-driven platform that not only leverages AI's capabilities but also presents the insights in the most understandable way possible — all courtesy of an intuitive interface.
b) The availability of knowledge-base articles
The idea behind deploying a knowledge base (KB) is to answer the most common questions as quickly as possible. KBs are useful because agents don't have to worry about asking repetitive questions. Besides, they don't have to refer to Google or some other search engine for that matter, thus, warding off the risk of delaying issue resolution.
"66% of customer service teams use knowledge bases, compared to 82% of customers who use knowledge bases (e.g., online FAQs)." —Devon McGinnis, Salesforce
c) Facilitation of streamlined after-sales pipeline
Companies are always looking for ways to improve their service delivery. One of the best ways to do so is by segmenting their support or customer experience apps into various parts.
After-sales pipeline (ASP) is one such segment where businesses can integrate AI (and chatbots are one way to do this). Articles in ASP pertain to the following:
i. Open cases that have been resolved; closed cases that have not yet been dealt with by agents.
ii. Cases that have been resolved but haven't received payment from the customer. This gives agents, or customer care executives (CCEs), an opportunity to follow up with the customers.
iii. Open cases that agents have not yet resolved. These cases are assigned to the next agent in line for resolution.
iv. Cases that have not yet been opened. These cases are assigned to the next CCE, who is available to resolve them.
v. Cases that have been opened but haven't been discussed with the customers. This allows agents to contact the customer by phone or email and inform them about their respective cases.
As such, AI-powered solutions are also able to "integrate post-sales systems into [their] knowledge bases, thus, creating an effective environment for resolving issues and keeping customers coming back for more."
d) Reduction in response time
Perhaps the most crucial factor defining the success of a customer support service is the agent's availability to respond swiftly.
A survey cited by Statista revealed that 75% of the customers wanted an instant response from chatbots compared to 73% and 64%, respectively, wanting the same for face-to-face and telephonic interactions. Rest of the customers highly preferred responses within 24 hours.
Unfortunately, customer support agents are not always able to respond immediately, and understandably so. This can be attributed to reasons like low staff-to-case ratio, which, in turn, "affects the quality of customer service."
To ease this concern and improve agents' response time, AI can help by providing the needed information in real-time. This will not only ensure that the agents are well-prepared for any ticket but also help avoid the risk of asking customers to wait unnecessarily.
Adding a chatbot to the support center can certainly increase the responsiveness level. There are other ways of achieving this, too. One such method is using AI-powered analytics and notification tools that help agents manage the interactive flow of a queue and streamline the interaction process.
2.2 AI for Improving Agent Experience
When the workflow is optimized, agents are sure to experience comfort and satisfaction. If they can deal with cases quicker and easier, this will help them do their jobs better.
The following are a few of the changes that AI (or chatbot) can bring about in the customer care process.
a) Better interpersonal communication
The most common issue that agents face when communicating with customers is the risk of being misunderstood. This leads to miscommunication and misinterpretation of client information, which may, in turn, result in unhappy clients and agents losing their patience in dealing with them.
Inappropriate communication is usually caused by a lack of relevant knowledge of the agent. This is partly because the agent doesn't have enough data about the client's history or buying preferences and partly because his/her assumptions are not correct.
As mentioned above, one of the best ways to improve agent experience is to empower them with relevant information so they can better understand their clients. For instance, AI can be used to analyze and identify clients' buying patterns from their past interactions with customer service agents on certain items.
b) Stress-free working
Many customer support agents experience the stress of dealing with clients who are not satisfied with their response time. This leads to fatigue and other physical challenges that are detrimental to the quality of their services.
To help reduce the burden on agents, AI can work with an agent-management platform and offer continuous feedback on their performance or call-scheduling strengths. It can also manage their work schedule, so they have more time to focus on other inherently creative activities.
c) The ability to handle complex requests
As technology becomes more advanced, customers demand more out of their service providers. This results in a decrease in customer satisfaction levels.
Since there is only so much AI can do, it presumably can't deal with every kind of customer issue. But the good news is that businesses can use a combination of human/AI interaction for their support center.
For instance, AI can handle basic queries, while agents can be tasked to take care of complex issues. This is because although AI is better at handling routine tasks, it cannot always deal with those customers who have special requirements. Businesses can also use them in tandem to create a "hybrid human-machine intelligence" interface for better customer service.
Part 3 - AI for Optimizing Company's Overall Support Process
By now, we have seen how AI has been helping improve customer and agent experiences. Doing so, AI makes a strong case for becoming a great assistant both for the end-users and the support representatives. However, from a bird's eye view, AI does "more" than just enhance the experience of the core entities involved.
The word "more" can be refined by reflecting upon the benefits that businesses derive from incorporating artificial intelligence for customer care.
3.1 The Facilitation of Intelligent Predictions & Forecasts
Recall 1.1, where we talked about defining customer journeys (because they are immensely important). Gartner outlines that about 30% of the organizations can map out the customer journeys; however, that doesn't guarantee that they can leverage them effectively.
In fact, most companies cannot. But when they do, they get on top of the game and remain at the forefront of competition for a prolonged period.
The point is that organizations need to continuously improve the way they touch base with customers amidst the ever-increasing competition. The only solution to their issues is adhering to the statement "data is the new oil." And guess what helps create intelligent predictions and forecasts using this data? Artificial Intelligence (AI) does!
AI uses the collected data points from information sources such as CRM, financials, and other databases to decipher patterns and relationships that the human brain would otherwise not recognize. These patterns help unearth the future likelihoods of customers' preferences and interests — the primary reason why AI is indispensable in improving business intelligence.
3.2 The Optimization of Service Operations and Delivery
Kathleen Walch from Cognilytica notes that AI can detect patterns and anomalies from data, formulate best practices, discover insights, etc. These are the core factors that drive business efficiency in all aspects, including services, marketing, and administration.
In terms of customer care, AI can analyze the data agents generate to determine the nature of customer issues and the most effective way of resolving them. In simple words, it can evaluate what support center agents should do in their routine work.
For instance, AI predicts which product a customer is looking for and informs the support staff about it. It can also identify when a customer has been served the most so that agents can focus on their support tasks which are more beneficial to the organization.
3.3 Omnichannel Support Facilitation
Businesses have to provide consistent support for their customers, regardless of where they interact with them. That means they are required to have customer service teams available on multiple channels to cater to the customers' preferred choice.
Research suggests that almost 90% of the customers prefer having an omnichannel interaction facility. This aligns with what Aspect Software's analysis revealed — businesses with substantial omnichannel facilities could retain over 90% of their customers.
However, the facilitation challenge is that businesses lack the means and resources to have a single team to manage the whole omnichannel support process. Thankfully with AI, agents can be routed to the channel of interaction that the customer prefers. Agents can even be trained to learn and use multiple channels while interacting with customers.
3.4 Automation — the Master of All
What's the overall implication here? — It means that brands can automate their interactions with customers, all in an attempt to reduce the cognitive load of agents. This is not an attempt to replace good old-fashioned customer service agents. Instead, it's a bid to enhance their capabilities through the power of machine learning and AI.
Essentially, it's about helping agents do their best work and enhancing the overall customer experience/customer journey. Automating your company's support process doesn't necessarily require complex algorithms and sophisticated neural networks and can still be highly beneficial.
Here are some ways how AI can automate customer care:
a) AI can schedule appointments and meetings without being asked to do so.
b) AI can remind customers about their outstanding issues (from different channels) and then respond to them all at one go.
c) AI could analyze agent notes, the time of the day, the number of open tickets from a specific customer, the channel preferences of a customer, etc., to offer first-class support with minimal effort on the agents' part.
d) The entire support team can be optimized to offer assistance at best possible time.
3.5 More cost-effective customer support
It takes a lot of time to train customer service agents and keep them up-to-date on the latest technologies. It can also take a lot of money to set up an efficient infrastructure that offers faster and more reliable services.
With AI, businesses don't have to spend money on training their agents on how to interact with customers or setting up the right infrastructure. Instead, they can leverage AI tools that can do these things for them and optimize their customer support center processes for better efficiency.
Even if the need for training emerges, those processes can be automated for better efficiency. This means that the effectiveness of an agent's time can be better utilized, and the money spent on training can be saved for other purposes like research and development, product development, and growth.
3.6 Capturing New Customer Leads/Selling Products/Services
In addition to having automated bots handle repetitive issues and queries, organizations can use AI-powered live chat as a new sales channel. Since the chatbots are constantly learning, they can be used to understand the customers' needs and requirements to offer suggestions for related products/services.
So, when it comes to nurturing leads to guide them down the sales funnel, businesses can use this automated chatbot to communicate with their customers about their needs and desires. The chatbot can offer suggestions and engage in conversations that help to understand the customer's buying behavior.
In simple terms, bots can help companies become more efficient and achieve customer-centricity. They can handle routine tasks that require minimal human intervention and maximize their time to focus on bringing in new leads. Bots can also analyze the company's data to make intelligent suggestions for improving its operations in the long run.
3.7 Higher Employee Engagement and Satisfaction
At the end of the day, your agents are a part of your organization's success. When employees feel they're being left out of the loop, it can lead to various issues such as underperformance, low morale, etc. However, with the employment of AI-powered solutions, agents (as explained above) can be equipped with immensely progressive resources and solutions.
Simply put, if the company is working on automating its support process and customer service agents are kept in the loop about it, it can increase overall productivity and engagement. The more engaged employees are with their work, the more they will value their employer's goals and objectives.
All these advantages are a testament to the fact that AI is the harbinger of success for companies. However, brands need to be aware of a couple of things before they fully implement AI.
a) First off, they need to know their data. Before embarking on any AI endeavor, they should perform an extensive data audit to identify gaps and prepare for the integration of AI in their business processes.
b) Businesses need to develop a clear understanding of their AI goals and objectives. They will then be able to plan the proper IT infrastructure, choose the right tools, set up the necessary resources, and allocate funding accordingly.
c) They need to hire the right AI talent. This will ensure that they don't lose sight of their goals and objectives and that the AI solutions take the appropriate path.
d) Finally, businesses need to have a proper evaluation mechanism in place. They can use different metrics like cost efficiency, time efficiency, user satisfaction ratings, etc., to figure out if their AI solution is indeed successful or not.
Part 4 - The Top 4 Artificial Intelligence Applications in Customer Support
We have seen what AI can do on paper, but what about its practical applications and real-life implementations? Let's take a look.
4.1 Natural Language Processing (NLP) & Self-Service Chatbots
Chatbots are mostly known for being the most popular conversational interfaces that businesses use these days. However, NLP is the behind-the-scenes tool that powers these chatbots. The technology typically entails the automatic processing and analysis of written, spoken, and visual language. This is accomplished through Part-of-Speech-Tagging (PoS), parsing the sentences, and positioning the words on a hierarchy of categories.
That being said, NLP has been adopted as a convenient way to enhance customer support. With AI-powered NLP, customer service agents can be better equipped with the right skills to assist customers in their queries (without being told to do so). The accuracy of these automated chatbot systems can be made more effective by analyzing multiple data sources that may include online store reviews, conversational style analysis, and sentiment analysis.
The best part is that this technology is available on numerous platforms like Facebook Messenger, Slack, etc. As such, the businesses can streamline the support process across disparate channels, enhancing the customer experience and complementing the branding efforts.
4.2 Smart Monitoring and Knowledge Management
AI-powered knowledge management systems help businesses improve the overall efficiency of their customer support operations. AI can be used to develop unique guidelines, training programs, and libraries for agents to refer to. These insights can be gathered from various sources such as chat logs, conversations with customers, and various automated analytic tools.
Moreover, as an immediate result of the data gathered, AI can augment the agents' productivity, thereby making it easier for them to decide on the appropriate actions to take. Considering this, businesses can automate their real-time customer support operations at scale. This allows them to have their agents focus on more complex tasks as well as respond to critical customer issues.
4.3 Automated Resolution of Customer Support Tickets
Using AI-powered automation for resolving customer support tickets can be one of the most significant benefits of adopting this technology. Customer service agents are often left with repetitive, routine tasks that consume their valuable time and efforts, so automating these tasks using AI can free up their time and allow them to focus on other critical areas.
It also brings about a much-needed level of automation and efficiency in user journeys. This way, businesses can improve the quality of their digital interactions with customers by reducing reliance on understaffed customer service teams.
4.4 Robust Upselling and Cross-Selling Campaigns
AI can be made to identify the best sales approaches for you — bringing in more leads and higher conversion rates. This is something that can be done by using sophisticated conversational systems to tap into customer's emotions, preferences, needs, etc.
Upselling and cross-selling are among the most popular ways of trying to find out if what you're offering is a good fit or not with your customers. The problem with these two techniques is that they're mostly used by sales teams and not customer support teams, but this is where AI comes in handy.
Once the AI-powered cross-selling and upselling applications have been installed, they will work alongside agents to make targeted recommendations. This way, you can utilize AI to better predict your potential customers' needs while increasing conversion rates.
Part 5 - Future of AI & Customer Support
The future promises many exciting possibilities for the customer support industry. For instance, businesses will be able to use AI to keep a watch on the performance and health of their machines. This automatic monitoring will enable them to spot dangerous failures before they escalate into a crisis.
On top of that, they can also deploy AI-powered video call systems for live customer support within their offices, which significantly improves productivity. Lastly, smart automation can be used to provide enhanced customer support for businesses that have multiple locations across the world.
Thanks to the rapid growth of AI, businesses have numerous opportunities to get ahead of the competition and influence the customer support industry.
Nevertheless, with AI comes both opportunities and challenges, depending on the specific type of solution that is being deployed. To successfully leverage AI, the right technologies need to be chosen and then applied in the right way — which is why discussing the employment of AI-powered solutions with experts is vital.
Conclusion
Is AI better than humans at customer service? Yes and no. You see, although AI is excellent at handling tasks that are routine or repetitive, it's not always good at doing so those that are complex and/or strategic. In such situations, human input is still necessary.
But do the advancements in AI mean that humans will be out of a job one day? Not really. The fact remains that "technology will only ever supplement human labor. It will not replace it."
So, when considering whether or not to invest in AI-powered solutions, it's essential to consider what you want your customer service team to accomplish. If they are expected to handle a variety of issues and tasks, then consolidating their workflows with AI from start to finish is the way to go.
If your customer service inclines more towards strategizing and brainstorming with new businesses or perhaps discussing complex cases with customers, then you're probably better off hiring seasoned professionals and having them conduct these tasks — all while using AI to automate redundant tasks and complement the agents' efforts.
To sum it up, AI is here to stay. And by now, you would certainly agree that the argument isn't whether or not to use it — it's how and where to use it.
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