How IT Leaders Can Redefine Customer Success in The AI Era

AI and digital transformation haven’t changed the core of customer success—they’ve changed how IT leaders drive it, says Vijay Tambwekar, Author of Customer Experience Decoded and former Strategic Advisor at NTT Data Services.

IT leaders are no longer just responsible for keeping systems running—they are at the forefront of shaping customer success.

Customer success has always been about helping customers achieve their desired outcomes. While the core definition remains unchanged, the methods and tools used to drive success have evolved dramatically with AI and digital transformation. IT leaders now find themselves not just as enablers of efficiency but as strategic partners influencing business outcomes through data-driven insights, automation, and intelligent systems.

“The IT team is at the table for running and growing business. This has increased the responsibility of IT teams manifold to ensure the models they build for data capture, storage, analysis, and drawing inference are close to reality, do not step on customer privacy, do not infringe on any copyrights and bring that efficiency and effectiveness to business,” says Vijay Tambwekar, Author of Customer Experience Decoded and former Strategic Advisor at NTT Data Services.

Vijay is a results-driven leader with over 15 years of experience in global delivery, portfolio management, and operations. With a proven track record in driving green delivery, revenue growth, and customer satisfaction, he has successfully led large global teams while managing delivery operations for portfolios worth $1 billion. Passionate about customer experience, Vijay has spearheaded strategic initiatives in NPS improvement, SLA optimisation, and leadership development.

In this conversation, we sit down with Vijay to explore how IT leaders can bridge the gap between technology and customer success. From simplifying complex IT landscapes to leveraging AI-driven insights, he shares his perspectives on creating lasting value. 

Excerpts from the interview:

How should IT leaders redefine customer success in an era of AI and digital transformation?

Customer success per se has not changed because of the current era of AI and digital transformation. The definition of customer success remains the same; it is simply “making customers successful in any endeavour they are engaged in”. IT has always been a partner in helping businesses improve by building efficiency and effectiveness through tools that technology brings forward. 

As technology progresses, new tools are available for IT to deploy and for the company to use. So, technological leaps constantly change the “How” portion of customer success, whereas the “Why” and “What” portions mostly remain constant.

With the advent of AI and digital transformation, things have changed drastically, which will continue for a long time. This has given the IT team some power to play in the business domain rather than the technology side of it. IT is part of the team understanding data and training it to enable some thinking for answering customers’ questions. They are working hand in hand with business teams. 

The IT team influences business strategies and decisions through the intelligence gathered. They are at the table for running and growing the business. This has increased the responsibility of IT teams manifold to ensure the models they build for data capture, storage, analysis, and drawing inference are close to reality, do not step on customer privacy, do not infringe on any copyrights, and bring efficiency and effectiveness to the business.

What are the most critical KPIs and metrics for measuring IT’s impact on customer experience?

Before measuring IT’s impact on customer experience, an organisation must determine how it will measure customer experience. Is it going to use a direct method of survey or an indirect method of measuring the final result, e.g. it could be an increase in revenue from a specific set of customers, a specific set of products or a particular set of geographies, or it could be higher footfalls to the store or new website launched as part of customer experience improvement initiative.

In the direct measures, a few KPIs are used by organisations like NPS score, First Contact Resolution, First Response Time, etc., which assume that if the score is higher than SLA, the customer is satisfied and having a great experience. I have seen many times that this is not true. 

The overall SLA level might have been met, but the resolutions that missed the SLA might have created enough redness to overshadow the SLA achievement’s goodness. We see this watermelon effect with many customers (green outside- SLA met, red inside- Customer unhappy), resulting in challenging situations during renewals or expansion opportunities. So, these measures are good but not sufficient.

They should be complemented with something related to the ultimate outcome expected out of the improvement initiative. This is where indirect measures come into play. Depending on the initiative (assuming that the improvement initiative is an IT initiative like creating/improving a portal or something where an IT intervention is introduced), organisations can track expected outcomes. 

Simple basic measures for tracking could be the Number of Customer Visits to new sites (before/after and trend), revenue per customer per visit or during a period, revenue per product category per customer per visit or revenue per customer in different customer segments. These numbers are available with the company, and trends can be plotted; the IT intervention will be successful if the trends are in the right direction. Otherwise, course correction might be needed based on the trend and analysis. This information can also help plot ROI from the initiative, which is critical for allocating investments for future initiatives.   

Customer experience and its impact on the final outcome are relative in nature and depend on many factors in the ecosystem beyond initiative, which are dynamic. It is difficult to map the exact correlation between IT initiatives and Business Impact. However, with the help of AI algorithms, this could be created and continuously refined based on the data on the ground to get a model predicting IT impact on Customer experience. Of course, this is a bit far from now, but it is a definite possibility.  

What’s your advice for IT leaders trying to drive continuous improvement in service delivery?

First and foremost, IT leaders should remember that continuous improvement is an ongoing process. It needs to continue even if the team meets all the SLAs agreed upon with the customer. There is always some room for improvement, and IT leaders should make conscious efforts to identify areas for improvement and work towards them. 

It is always a journey from customer satisfaction to customer delight; it is always a journey to get that loyalty; it is always the effort of creating a customer who asks for more and more services from you and recommends you to other customers, resulting in renewals and expansion. To build a continuous improvement plan, it is critical to understand where they stand initially and which customer needs are being satisfied, as well as not satisfied, by the services provided.

They can use the “Differentiated Needs Pyramid” framework, as explained in my book, Customer Experience Decoded, to get the lay of the land. Once this pyramid is created for customers with needs at five levels and what is being done through current service delivery to satisfy needs in these five levels, the needs which are not satisfied can be identified. 

These needs become the guiding pole to build the action plan. There is a possibility that certain actions may not be in the scope of the current engagement and could provide an opportunity for pitching new/additional services. There could be some merit in investing in certain improvements to get an advantage in customer mindshare, which will come in handy during renewal.     

The industry popularly uses two mechanisms to receive feedback on service delivery: the automated survey (sample) after ticket resolution and the periodic NPS survey. Insights from analysing survey results provide ideas for improvement programs, and the results of these programs are again measured through the following survey. I have found certain limitations with this process. 

The improvement programs often do not deliver the expected results, and the company finds them when it is too late. Customers must participate in these surveys, i.e., they must spend time filling in the survey, which many customers do not like to spend. These are typically done on a voluntary basis, and participation is not guaranteed. Because of its voluntary nature, most likely, we will not be able to have a correct/balanced sample for the survey, and there could be a significant bias due to unhappy customers participating in the survey rather than balanced participation. There is a high chance that the survey will have very low participation, and we will not be able to use these results for improvement initiatives. 

For NPS surveys, the surveying team chooses the respondents, and there is a high chance that they may not select “not favourable” customer leaders for the survey. There is also a scenario in which only a select % of customers respond. This leads to the possibility that the survey may not represent a comprehensive/all-inclusive picture of customer feedback.  

To avoid all these pitfalls and continuously obtain customer feedback, the IT team can adopt the mechanism “CX On the Go” explained in my book, eliminating the need for a survey. 

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With internal team rigour, the customer feedback/sentiment/satisfaction level could be captured in a structured manner after every interaction with the customer. This information can be sliced and diced from many angles to understand what is happening at the customer end. Once we capture the feedback, we can implement continuous improvement programs. 

These programs should start providing positive results and show up in our interactions after the implementation. The satisfaction trend will give us the confidence to go ahead with the initiative or apply a course correction.

With IT systems’ growing complexity, how can organisations simplify processes and be adaptive to change without sacrificing innovation?

If we look at the processes in the organisation, they remain more or less the same, but how they are executed is modified in the simplification process. Technology or IT systems play a significant role in this case.

If we can do a specific step in the process effectively if we automate a specific step to eliminate the manual step, or if we are able to find an alternate way of doing the same thing using a tool/product, we consider the process simplified and reap benefits from such continuous improvements.

When an innovation engine is running in the organisation, various ideas/proposals for improvement are tabled; these are discussed in a forum to make a Go/No go determination. This will be followed by the actual implementation of approved ideas and assessment of benefits. As the IT systems become complex and interlinked, it becomes challenging to bring in a change. This often requires a significant effort and can cause business disturbance. There is a natural tendency of “not to change if it is not broken”, which deters many in proposing new ideas.

These hurdles should be reduced to keep employees engaged in the pursuit of innovation.

Organisations can take up exercises to create better architecture, making changes in pockets easy. For example, a monolithic architecture could be moved to service-oriented architecture, which enables changes to smaller components without impacting the remaining portion of the system.

Organisations can create a process where small changes are introduced at a time, and benefits start accruing early. This removes the inhibition in the minds of employees to suggest innovative ideas and allows businesses to accept new ideas in small chunks. The agile method for innovation delivery will help achieve this    

Finally, celebration/ recognition of innovative ideas keeps the innovation culture alive and thriving.

What technology trends excite you the most?

I am excited about two trends in the current scenario:

Personalisation using AI in every aspect of customer interaction – it can be just making content available for browsing, or it could be vacations that you may like, clothes and appliances you may like, and the list goes on. I am curious about how the industry balances the elusive boundary of data and individual privacy. 

With significant development and productisation in Conversational AI, Generative AI and Agentic AI, I am curious to see how Conversational AI, Generative AI and Agentic AI collaborate and create an end-to-end service handling in “Untouched by Human” mode.

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