Turbo-Charging Customer Service with Artificial Intelligence


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With companies struggling to survive the COVID-19 storm, new approaches and tactics have replaced existing business models and strategies. However, the biggest challenge for businesses remain ensuring superior customer experience and preserving relationships to thrive in this new normal.

In fact, according to a survey conducted by Forrester, consumers now feel more fragmented, disconnected and less trustworthy of brands than before. Accompanied with minimal physical interactions, companies are in search of devising unique ways to interact with customers and keep them happy and connected.

Current customer service models are hinged on availability of an army of agents to satisfactorily resolve customer queries. These agents would interact with the customer, understand their problems, and then provide the best possible solutions. The CRM system used by agents, typically included case management features allowing them to create cases and route to the best possible individual or team who would then attempt to resolve query based on their expertise.


With companies keen to cut costs, customer service managers are now forced to find novel ways that provide similar or enhanced level of support without expanding their team footprint. Consumer behavior too, is changing, as customers expect more personalized experiences with faster resolution time. This is where Artificial Intelligence (AI) and Machine Learning (ML) plays an integral role in reshaping customer service models and improving customer relationships.

AI/ML can not only offload routine tasks performed by agents, but also provide customers with self-service channels to find information quickly and raise a query if needed. It can provide supervisors with real-time service ticket intelligence to optimize support operations. In fact, support managers can scale up operations in a very short time to support multiple product / service lines leveraging AI/ ML.

With AI transforming traditional contact center operations, McKinsey forecasts that by 2030, 70% companies will adopt AI, with the majority using a full range of AI technology.


AI can fundamentally transform several aspects of the customer service ecosystem. From helping agents with intelligent ticket routing to generating insights based on service ticket data, AI can have a long-term impact across the value chain.

Top goals of enterprises for implementing an AI-powered customer service include:

  • Deepen customer relationships
  • Scale support operations quickly
  • Increase implementation ROI
  • Insight driven decision making
  • Enhance business functions
  • Empower agents



The benefits of AI, though significant on agents and customers, will be seen across the ecosystem. Let us explore key scenarios where AI can enhance customer interaction and improve agent productivity.

Elevating customer self-service with Bots

With customer service becoming a key differentiator, companies know that they need to be available for the customers 24X7. Initially, companies accomplished this by providing generic information via multiple sources such as FAQs, trouble-shooting videos, and guides. However, lack of personalization left customers overwhelmed ultimately leading to frustration. In fact, a Gartner survey found that only 9% customers reported resolving their issues completely via self-service.
AI takes this to the next level by humanizing the delivery of relevant information. Conversational bots leveraging ML can handle majority of customer queries by looking at transactions, making updates to personal information, and handling simple tasks like account unlock and password resets. They are also able to seamlessly hand-off to a live agent for more complex interactions in addition to passing on the already collected information. This is also true in case of virtual assistants like Alexa, Google Home where NLP is leveraged to “listen” to customer queries.

AI-powered Customer Communities

Customers love to look at information on the website before reaching the support desk and, hence it is crucial to have a robust community portal with relevant information. An AI-powered customer community can understand the intent and contextualize customer’s search by cross-referencing with customer’s transaction, location history, behavior and suggest the most relevant answer.

It can mash-up information from multiple sources such as product manuals, training videos and point to a similar query that has already been answered. This not only improves case deflection rates, but also customer satisfaction as queries are resolved almost instantly.

Intelligent ticket routing and automated response

An L1 agent is tasked with triaging incoming tickets, classifying them and routing to the appropriate team. This is often time consuming and can be overwhelming in case of a deluge of tickets due to an ongoing service outage. AI can automate the entire process of tagging incoming tickets from multiple channels and summarize, gauge intent, understand urgency and effectively route them to the rightly skilled specialist. Additionally, a contextualized response can be generated and send to the customer instantaneously.

Imagine a scenario where customer is facing issue in installing or configuring the product. When the customer raises a ticket, links to installation videos or setup FAQs can be sent while ticket is being serviced by a team. This will help the customer troubleshoot the issue before even raising a query with the support desk.

Empowering Agents with Cognitive search

Today’s support agents are expected to do more than just resolving customer queries. They are expected to build customer relationships by generating leads, providing product information, cross-selling / up-selling and be the voice of customer to the enterprise. However, this can be made possible only if they are armed with the right information that is also contextual. Knowledge Centered Support (KCS) is one of the guiding principles for a successful customer support operation.

AI-driven Cognitive search can bring in relevant information that is stored across CRM systems, knowledge portal, company intranet, rich media content and blend into a form that is easily consumable. This allows the agent to find relevant information without sifting through long search results. Cognitive search can also be scaled up quickly and fine tune the relevancy of results over time.

Customer service is one of the primary areas where AI is being used and showing an impact. Being in an era of personalized customer service and advanced customer experiences, equipping CRM platform with AI can be a great way to take the plunge into the upcoming business transformation. All the right tools and techniques can help agents to be more effective and productive in this new world order.

1. https://go.forrester.com/blogs/as-overall-energy-wanes-preserve-relationships-with-your-highest-energy-consumers/
2. https://www.orange-business.com/en/magazine/secret-agent-ai-contact-center
3. https://www.gartner.com/en/newsroom/press-releases/2019-09-25-gartner-says-only-9–of-customers-report-solving-thei

Manjunath Srinivasamurthy
Manjunath is a Senior Manager and part of the Customer Experience group within Brillio. With over a decade of experience across Business Consulting, Product Management and Systems Development, he has worked with several Fortune 500 clients to support their digital transformation journey.


  1. Great article, Manjunath, and I like your mention of relevant structured data as a prerequisite for successful use of AI! Even more I like that you put KCS at the core of any AI initiative since the methodology provides for structured articles and through the links between articles and cases you’ll get the required relevance. Thus the algorithms do have enough food for thought.
    On a sidenote, the methodology has been renamed to Knowledge Centered SERVICE four years ago to reflect it is not only to be used in technical support but in any knowledge intensive services area.

  2. Thanks for the feedback Kai. Yes , you are correct – KCS ( Knowledge centered Support/Service) is now a critical component of any customer support strategy and even beyond . And with search technologies having evolved from a simple keyword search to cognitive , information is not just quicker and easier to find , but also relevant. With self-service channels being the primary ones that a customer would tap into, it assumes even more significance.


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