AI in Customer Support: Threat or Savior

Professionals and experts agree that AI will likely impact multiple industries, including customer support, in the next ten years. AI is perfectly suited to answer questions with immutable answers, and customer support often falls under this category through company policy or troubleshooting, but is it still too early to integrate an AI into your enterprise?

Here’s the good news: no, it isn’t too early to adopt an AI assistant for your company, but before you start bargain hunting, you should first understand what today’s artificial intelligence is capable of.

The Evolution of AI in Customer Support

It’s easy to think of AI as a more modern concept, but its story begins in 1964 with the development of MIT’s ELIZA natural language processing computer program. While quite simple by today’s machine learning standards, ELIZA represented a proof of concept for a machine that could respond and interact as naturally as a human.

Fast forward to the 1980s, and interactive voice response systems were in full swing, using voice responses to answer simple questions and guide customers to the appropriate customer service agent. Chatbots hit the scene with internet saturation, forever linking customer service to the need for a dynamic response system that could operate 24/7. Voice recognition pushed this initiative further, letting users interact with vocal input instead of touch-tone button presses.

With the advent of machine learning (ML), computerized responses no longer needed to rely on rule-based systems; they can evolve intelligently to fit the needs of their diverse customer bases. Responses became more fluid, responsive, and varied, especially when generative AI arrived in the late 2010s, and when data collection became the norm in the 2010s, ML algorithms had no want for new data to learn from. The stage was set for ML use cases, and customer service was one of the obvious sectors of interest.

Potential Challenges and Concerns in Enterprise AI Adoption

While today’s examples are impressive, AI is not truly intelligent. It is limited to the data it was trained on, lacks what we would call ‘common sense,’ and cannot reason or create independent thoughts. That means that it can struggle to display emotional intelligence and certain nuances or contextual challenges can generate friction with your customers. It is also limited to the quality of the dataset it was trained on.

Think about AI in terms of what it can do to improve your operations rather than replace them. AI is fantastic for answering frequently asked questions, answering quickly (no more being on hold!), answering via any messaging or texting app, and getting customers to the appropriate support specialists.

That means your customer support team can be smaller, as only unique issues without established answers will get through. You can also bet on having a happier team as they can put their efforts towards something that actually requires their attention. Call centers have notoriously high turnover and retention rates, leading to increased training costs and bad team morale. With AI handling the majority of inquiries, your team can breathe a little easier, and your customers can get what they need quicker.

AI-Powered Chatbots: Revolutionizing Customer Interactions

Despite being relatively new, chatbots are already providing an AI-powered customer experience that is making waves and setting trends.

They can stay awake 24/7 and answer in multiple languages, meaning borders and time zones no longer present a challenge for international coverage. They can achieve this while still responding naturally, which makes customer interactions more intuitive and engaging.

Machine learning can also enable predictive analytics in customer service, which can give conversational chatbots context about a customer, their frequent issues, and how to solve them. Through context clues in a customer’s response, they can even start to understand which specialist they need to guide the customer to.

The risks involved with AI-powered chatbots in business largely revolve around prompt manipulation and vulnerabilities in the AI’s learning model. Techniques like prompt injection, jailbreaking, SQL injection, and vulnerabilities in your AI’s API or source code can disrupt the chatbot’s functionality and potentially leak data.

To combat such problems, restrict the length of text users can enter, filter customer responses to replace potentially harmful words, use anti-malware solutions designed to protect against such attacks, use parameterized queries so customer messages are always treated as data and not executables, and stay up to date with the latest security best practices.

Predictive Analytics: Personalizing Customer Experience

Predictive analytics is a method of using data to forecast outcomes and personalize the customer experience. AI is great at pattern recognition, and knowing what to expect out of a customer is a great way to improve their user experience. That’s why the predictive analytics software sector is poised to grow by more than $35 billion from 2020 to 2028.

This method can help in several ways.

Highly personalized experiences: Your customers interact with hundreds of companies every day, so they expect an experience that reflects their needs. You can use AI to predict what your customers want before they know they want it by basing recommendations or interactions on their previous actions.
‘Churn’ reduction: AI can look for data like bad feedback, cycle times, retries, and customer effort to identify customers who may be unsatisfied or interested in pursuing services with a competitor. This equips your support team with the information they need to take action before you lose the customer.
Forecasting customer needs: By analyzing previous customer actions, AI can anticipate what a customer wants and curate what gets marketed to them, what kind of messaging resonates with them, and what products to recommend.

The Business Case for AI in Customer Support

A recent study found that 23% of customer service organizations are already using AI to enhance their customer support, indicating that AI saturation in this market is no longer an “if” but a “when.”

In fact, AI has already been integrated into the backends of some well known companies, including Netflix, which uses AI to determine what you’d like to watch based on your previous selections. They also use AI to generate appealing, personalized thumbnails for their viewers and place content they are likely to watch closer to their location to improve streaming quality.

Netflix isn't alone; Starbucks uses AI to suggest new drink ideas, determine when to make orders, and offer personalized experiences through their chatbots. Domino’s Pizza uses artificial intelligence to start making pizzas before customers even place their orders and refine delivery routes with real-time traffic and location data. Amazon, Bank of America, Marriott, H&M, T-Mobile, Progressive, Zillow, and many more all use AI to enhance their customer support experiences, and the trend shows no indication of slowing down.

Enterprise-Specific Misconceptions

AI will replace customer support specialists: The Harvard Business Review published an article disputing this claim by pointing out that leading companies are using AI to augment their staff rather than replace them.
AI will make customer support less personal: From using AI to help personalize emails to helping customers decide on a gift to filtering and prioritizing urgent requests, AI is already increasing personalization for customer support across the globe.
AI costs too much: In a study from McKinsey, 79% of respondents said AI integration had increased revenue, with enterprises able to make 20% more revenue because of AI-based strategies.

Implementing AI in Your Customer Service Processes

Once you decide to implement your AI assistant, your process should begin with chatbots that serve as a 24/7 frontline to both support and sales.

Routine inquiries can take up time from your human agents that could be better spent on complex issues. Furthermore, stress in the customer service industry is common, with over half of agents saying that they would likely quit as a result of high-stress environments. Having an AI assistant that can handle the majority of requests via chat or call can reduce wait times and team stress levels while routing the most complex or urgent issues to the agents that specialize in that problem.

AI can also be proactive in SaaS customer support. Because it can analyze huge amounts of data quickly and efficiently, it can help you identify patterns and customer preferences by looking at past interactions. This allows your support staff to personalize and tailor the customer experience for customers with inquiries. It also allows you to understand what those customers are looking for in a product or service, giving you the ability to offer relevant products before the customer even knows they want it.

Navigating the Challenges: Ethical Considerations and Customer Trust

Ethical considerations play a pivotal role in the deployment of AI in customer support.

One primary concern revolves around transparency. Customers need to be aware when they are interacting with automated systems to foster trust and manage expectations. Clear communication about the role of AI in customer support prevents potential dissatisfaction and promotes honesty in business practices. The good news is that many consumers are already on board with the idea of an improved customer experience through the use of AI.

Privacy is another ethical cornerstone. As AI systems often analyze vast amounts of customer data, it is imperative to employ strict data protection measures. Adhering to privacy policies and obtaining explicit consent for data usage reassures customers about the security of their information.

Finally, bias in AI poses ethical challenges. To ensure fair treatment, regularly audit and refine algorithms to mitigate any unintentional biases that may emerge. A commitment to diversity and inclusion in the development process can prevent biases in AI systems.

The Future of AI in Customer Service and Support

The future of AI in customer support in 2024 and beyond is poised for remarkable advancements.

Predictive analytics will likely take the lead, enabling AI to anticipate customer needs before they arise. Hyper-personalization, driven by sophisticated algorithms, will redefine customer interactions, offering tailored solutions with unparalleled precision. Chatbots, now more intuitive and context-aware, will handle complex queries, leaving human agents to focus on intricate problem-solving.

As AI continues to evolve, the future holds a landscape where customer support is not just responsive but anticipatory, elevating the user experience.

Wrap up

The AI revolution is no longer a fantasy and is no longer confined to the field of study. AI now resides in our phones, homes, work, and favorite restaurants and businesses. Its promise is uncharacteristically optimistic for automation and seeks to enhance existing workforces without the need to cut back. Simultaneously, it serves as a cost-saving measure that can scale support teams without additional hires and improves the customer experience in ways that were previously impractical or impossible.

Whatever the future holds for this technology, the fact is that businesses are adopting AI systems at a rapid rate, with more than 80% of companies utilizing AI in their business operations, and customer service is at the top of the list.

2024 is the year to employ an AI assistant, and with Hoory, your enterprise can get a tried and tested AI chatbot that comes with other powerful enterprise features and applications. The longer you wait to try this technology, the more dated your customer support will feel. Take a look today, and find out what Hoory is capable of doing for your business.