How AI is changing the world of Customer Relationship Management


In today’s world, as differentiation in products and services reduces, customer experience has emerged as the primary differentiator. Thus, brands are investing significantly in how they engage with their customers, across the entire life cycle, with a strong focus on building up brand loyalty.  And the more personalised and contextual the engagement, brand loyalty could be potentially higher. Earlier, it used to take significant effort to understand the customer better, with interaction and proofing data sitting in silos. Today, with AI, this can be the key to create the nuanced experience that differentiates the brand.

CRM and AI – a continuous loop

In most cases, a customer has multiple touch points with a brand for the products and services they are consuming. These touch points and interactions vary by the industry, the use case, and the persona of the consumer. In the automobile industry, once purchased, the interaction may typically happen only once a year when the vehicle is up for servicing. However, since an automobile (in most cases) is a high-involvement product, the quality and depth of the interactions are critical to ensure that the brand experience is not tainted in any form whatsoever. On the other hand, if we are buying groceries, the interactions may happen once in 7-10 days. However, the quality of the products delivered has a higher impact on the customer experience. Similarly, while you may not visit a hospital frequently, the quality of interaction is crucial to building trust with the healthcare provider team. The above examples illustrate how customer experience can vary, and any CRM that helps with this has to capture the nuances specific to the industry.

The other point is about the data that goes into building a great customer experience.  It is not only the quantity but also the quality (and accessibility) of the data that is available.  For most large organisations and reputed brands, one assumes that the technologies are advanced enough for the data to be stored in a structured manner.  Admit it – that is not the case! Emails may be stored in one location, voice conversations may be stored in a different format (if stored).  And this data may also be unstructured (which is possibly a minor problem to solve). The challenge arises – how does one give that data to a customer service rep to use rapidly (and that’s the key – she or he cannot be taking 5 minutes to locate and then decipher the info) to respond to a customer – let’s say at a car service centre, or when at a hospital? The CRM journey begins right here. This is where AI comes into play.

Customer experience does not start when a customer reaches out for a service issue. Nay, it starts when the customer first connects with the brand. For example, a customer who wants to upgrade their car will reach out to a dealer (or maybe through a website or any other digital means) – this is the first touch point. This is followed by a test drive (2nd touch point), the negotiation (third touch point), the delivery (fourth), and six months later, the first service. At each touch point, the auto company can gather vital information through the interactions, and use that to ensure that the experience delivered during subsequent touch points leverages the information gathered during previous touch points. E.g. If the customer shared that he/she likes long drives, then the focus could be on ensuring reliability and upselling RSAs during subsequent interactions. This can be extremely powerful, and further enhance the customer experience ALONG the entire life cycle.

So, when a customer calls to book a service, is this information immediately available to the agent? The bigger question is, can the brand associate patterns with a customer and personalise the buying experience? A small thing like the manner of salutation a customer prefers can actually enhance the experience and may even translate to a larger quantum of spending than originally intended. This is where AI comes in – with the power to go through a huge quantum of data and pick out these minute nuances to lend the personalisation angle to data.

Coupled with Generative AI, the ability to rapidly analyse large volumes of data and provide meaningful insights becomes critical.  

The potential of Generative AI 

Gartner positioned Generative AI in the ‘Peak of Inflated Expectations’ on the 2023 Hype Cycle for Emerging Technologies, projected to reach transformational benefit within two to five years. It is encompassed within the broader theme of emergent AI, a key trend in this Hype Cycle that is creating new opportunities for innovation.


While typically, AI has been defined around NLP and structured models, Gen AI opens up a new world of human-like interactions. It doesn’t need NLP to be able to go through queries and can be based on Large Language Models (LLMs) making it a lot more powerful.  It doesn’t need structured data – in a certain form or template. That’s the power of generative AI. The quality of insights is far more polished and richer when it comes to Gen AI.

To take the leap or not?

Everyone is excited by Gen AI; it is in every boardroom and pub today. The question that you should be asking yourself (and Gartner’s Hype Cycle calls this out) is – is Gen AI what I need right now? The answer is – yes, do it now, and do it fast. The Gen AI Hype Cycle has been shortened a lot, as compared to past technologies. Keep a few things in mind, though:

  • Tech for the sake of tech has no meaning. Steer away from getting caught up in the FOMO. Don’t do it because everyone else is doing it, for the sake of doing it, or because someone on the board is asking you to do it. At some point in time, some stakeholder or investor will look at the amount invested in the initiative with no results and jump to a conclusion that AI/Gen AI doesn’t work for the organisation. Be clear about the outcomes, and focus on specific use cases, benefits, and impact that you hope to achieve. 

  • Get key stakeholders on board. Whether you’re going to test a marketing use case or a customer service use case, ensure that your stakeholders are completely aligned and committed to using Gen AI with your CRMs and will work with you to iron out challenges (and trust me, there will be enough – organisations are still learning) as they come up. 

  • Design for the future but implement for today. Implement small but design large. Often, teams make the mistake of designing and implementing for the future. If the execution does not go at the scale that is planned, it may end up looking like a failure. Instead, demonstrate early successes, and scale up as you go past the success metrics defined.  

  • Course correct quickly. For a lot of tech, you start going down a certain path and broadly remain on that path for an extended period before results become apparent. But with Gen AI, things are changing rapidly and the ability to quickly adapt based on new developments is extremely critical. 

AI is here to stay and not going anywhere, but deeper into our lives.  It can transform not only the quality of customer experience but also do that at a much lower cost (or increased value). And if that results in increased loyalty, then there is no further debate. 

This article is penned by Angira Agrawal, Global SVP GTM & Strategy, Exotel. 

Disclaimer: The article features the opinion of the author and does not necessarily reflect the stance of the publication.


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