8 Innovative Ways of Using AI in Customer Service [+Examples]

AI has been a part of the customer service journey for a long time. Most progressive brands jumped on the AI bandwagon decades ago, and the laggards did so after the recent AI boom led by Generative AI. 

However, 6 in 10 customer service leaders admit they struggle with ineffective AI solutions, failing to gather real-time customer needs and data for personalization, according to our Outsmarting Adversity Report with CCW. Lack of real consumer intel leads to disjointed conversations and experiences, with 67% of people feeling agents “hardly know or care for them at all”! 

The situation is grim. But adversity is opportunity in disguise. It’s time to shift the focus from scale to innovation. AI tools can do more than just automate data gathering and give robotic responses when your agents are away. Let’s uplevel AI in customer service by following disruptive, unexplored paths that culminate in hyper-personalized support experiences and unwavering customer loyalty. 

But where do you start? 

From these inspiring examples of AI in customer service, of course. Path-breaking brands, untapped use cases and demonstrable success – here’s the dose of inspiration you need to kickstart your AI success journey.  

Example 1: Unified consumer insights 

Your audience is talking about you, your competitors or your industry everywhere, and they expect you to be listening. Put yourself on their radar with AI-led consumer insights. 

AI arms customer support teams with cross-channel consumer insights, enabling a more tailored customer experience. No matter where the customer connects with your business, AI knows their persona beforehand, courtesy of publicly available product reviews, social media channels, and forum posts.  

AI can provide deeper insights on existing customers, prospects and leads via their purchase histories, feedback surveys and customer interaction analytics, informing your agents with answers to questions like: 

• Why did they reach out to you earlier and why did they drop off? 
• What was the customer satisfaction (CSAT) score of previous interactions? 
• How did they respond to specific content/product recommendations? 
• Which resource and channel did they prefer for issue resolution? 
• What was their feedback and suggestions for improvement? 

Fueled by these granular insights, customer conversations become more meaningful and contextual. Customers feel heard, agents feel productive and your response time beats the best. 

3 Steps to Transform Your Customer Experience with AI-Driven Insights

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Example 2: Intelligent support workflows 

The human touch vitalizes customer support, no doubt. With AI-powered customer service workflows as a trusty sidekick, human agents can exert energy in building genuine human connections. 

Routine and repetitive support tasks like appointment setting, feedback collection, information sharing and basic troubleshooting can easily be automated using AI workflows. With step-by-step guidance and visual aids (diagrams, images, videos, help articles etc.), workflows lead customers to satisfactory resolutions and save agents tons of time. 

Example 3: Individualized agent training 

Research proves that coaching that is provided immediately after an interaction (also termed “integrated coaching”) has the potential to improve agent performance by 12%. That’s where AI enters the picture and facilitates agent coaching in a stepwise fashion: 

Step 1 - 100% Sampling: AI-powered quality management tool goes through 100% of interactions to give an undistorted picture of agent performance that’s beyond the scope of random sampling. The aim is to eliminate bias and subjectivity from quality management and coaching. 

Step 2 - Agent-level analytics: AI drills down into agent-level analytics, summarizing cases handled, CSAT levels, average handle time and other metrics, giving a helicopter view of how each agent is performing, lagging and acing on various customer service parameters.

Step 3 - AI scoring: Next, AI scores and benchmarks agents on quality and compliance parameters, such as active listening, empathy, etc., to identify top performers.   

Step 4 - Performance scorecards: AI generates scorecards to reflect absolute and relative performance, strong and weak technical skills and trends in scores. 

Step 5 - Live coaching: Using the above insights and tools, supervisors and QAs design individualized coaching programs as well as continuous support, feedback and guidance via real-time nudges. 

Example 4: Tone moderation 

In an emotionally charged environment, it’s easy for agents to get agitated and give off-tone responses that can end up negatively impacting customer satisfaction and brand perception. Today, there are Generative AI-integrated solutions that manipulate and moderate response tonality according to the given customer service scenario and CSAT prediction. From professional to persuasive to empathetic, agents can pick the appropriate tone and control the conversation more effectively.

Example 5: Agent assistance 

AI-led agent assistance has evolved by leaps and bounds from a time when agents had to window-hop to gather customer data and knowledge base content in between calls. The modern customer service software offers a delightful agent experience via advanced AI productivity boosters. 

AI can curate proactive content recommendations by reading the customer’s state of mind intuitively. From help articles to complementary products, everything is presented on a platter to agents, helping them navigate conversations efficiently. 

Additionally, agents don’t have to break a sweat composing unique responses to routine queries. AI-driven smart responses present templatized responses that can be customized with a single click. 

AI can auto-summarize cases and dispositions, minimizing after-call work for overworked agents and streamlining handovers. With a large part of slog work off the agent’s plate, their productivity and morale improve, containing issues of burnout and churn. 

Learn more: How Generative AI Drives Agent Experience 

Example 6: 24/7 Self-service 

According to our roundup of customer experience statistics, 85% of customers claim they resolve their issues with self-service tools without agent intervention. That’s one reason why AI self-service is gaining ground with small and big brands alike. 

Add a dash of AI to traditional self-service tools like knowledge bases, chatbots, and voice bots, and voila! You get super-efficient nifty helpers that can predict customer intent accurately and surface content that satisfies it every time, giving instant gratification a whole new meaning! 

• AI knowledge base uses auto-tuning to improve its search algorithm to understand which search results to show for which queries. 
• Conversational chatbots ditch rigid, scripted answers in favor of conversational answers by using natural language processing (NLP) and natural language generation (NLG) to interpret queries and compose answers, respectively.  
• Voice-based agents use AI to convert speech to text, removing background noise and other disruptors to get to the root of the problem. Machine learning (ML) helps decipher meaning and retrieve required information from the backend.  

Go Deep: How to Use AI Search in Your Knowledge Base Help Portal  

Example 7: Smart routing 

Customers dislike being transferred from agent to agent, which is why AI-powered smart routing is a must for every customer-obsessed team. It ensures cases are paired with agents who have the requisite skills, proficiency levels, and capacity to solve the case in first contact. The end result: happy customers and happier agents. 

But how does smart routing help? 

• It analyzes the persona of the incoming customer and handling agents historically. 
• It understands the contact reason, classifies it as an appointment, complaint, information query, or technical query and determines the required skill proficiency level for successful resolution.  
• Last, it maps proficiency level to agent capabilities to score a first contact resolution. 

Example 8: Response compliance 

Another great example of AI in customer service is the automation of response compliance with brand guidelines and regulatory norms. AI scrutinizes agent/bot responses and flags off-brand tone, grammatical errors, bias, prejudice, sexual undertone and business jargon, among other things. This way, you can avoid legal hassles and PR crises that threaten to spread like wildfire and tarnish your brand reputation. 

Wait, there’s more. AI not only spots these discrepancies but also suggests fixes and rephrasing options that can be deployed with a click. Commitment to “Responsible AI Technology” builds consumer trust that goes a long way in the hyper-competitive business landscape.

Need more examples of AI in customer service? 

If you’re still on a quest to find the best use cases of AI customer service and enviable success stories, head to our website for first-hand accounts from brands that are leading the AI revolution.  

Better still, get into the thick of things with a full-featured trial of Sprinklr Service, the world’s only truly unified customer service solution purpose-built to help companies improve processes and cut costs with AI and automation.