Q 802. In service environments, customers expect quick responses, but also want to feel that their unique situation is understood. AI agents powered by prompts and flows can deliver personalization — but if done poorly, they risk sounding generic or even invasive. Think of a customer-facing process in your domain. How could AI personalize the interaction in a way that adds real value, without crossing boundaries of privacy or trust?

The best answer will be selected on the basis of: 

• Relevance of the customer interaction chosen

• Practicality of the personalization method

• Balance between adding value and maintaining trust

 

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AI can be used to understand customers by creating a profile based on their past interactions. This can be done by not treating each customer interaction in isolation. This info helps the human agents/chatbots to start the conversation where it was left off in the past. Doing so also avoids asking unnecessary questions.

 

AI can also do real-time personalisation by listening to the calls, understanding the issue, and understanding the tone and mood of the customers and suggesting the next possible steps based on the interaction. 

 

 E.g., chatbots and virtual assistants can adjust tone, language, and content based on customer mood, demographics, and intent. These can also listen to the calls and suggest offers to the customers, send texts to customers regarding cart abandonment, etc. 

AI will convert each customer interaction by handling future exchanges according to favourites and behaviours. In my opinion, AI can read signals such as issues or happiness in customer interactions and adjust responses accordingly. For instance, Facebook uses AI to analyse posts, comments, and replies to understand user sentiments. Submitted recommendations can suggest products or services that a customer might like based on their previous actions or preferences. For example, online stores like Flipkart recommend items based on what you have viewed or purchased. Streaming services highlight shows that they think you might enjoy. I like crime & thriller movies; therefore, it highlights these kinds of movies & web series.

Every interaction feels personal with AI due to many factors. Some of these are listed below.

 

Randomness: One can choose the randomness in GPT to create varying answers, this is done exactly to make the users feel that they ain't talking to a bot. Hence, it gives the interaction a human touch. This feature in AI is defined by the term ‘temperature’. Higher the temperature, more the randomness.

 

Historical Data: The AI keeps on learning from the interactions. It adapts to what the user is looking for and also from the external world. This learning makes the AI feel more and more personal as more interactions take place.

 

Transformers and Sentiment Analysis: The GPTs use transformers to drive the tone, meaning and context of a conversation. They can also understand when someone is angry/happy/sad via sentiment analysis. Both these tools help them be more accurate and adaptive while responding back.

 

Omnichannel presence: AI reads through all the interactions and touchpoints like emails, chats, notes, etc. This makes them more personalised. They really know you well with all this data. This in turn helps AI become as personal as possible.

Pattern Finding: We all know AI is super fast at crunching numbers and processing data in this new era of smarter and faster computational power. With the omnichannel presence, AI captures data at each and every touchpoint, but it does not stop there. AI uses that data and its innate computational power to create patterns out of it. Ex: I play songs on youtube while driving to office. But I never play songs when I am home, I only listen to news or watch some reels. Youtube is smart enough to know this pattern and recommend me songs when I am not home and news when I am in my home.

 

 

When we talk about an organisation and particularly a customer interaction using AI, know that AI has all the above ability to become more and more personal. But, the data points we collect here can also invade his/her privacy. There are some points to keep in mind while utilising AI as a customer answering agent, these are:

 

 

Data capture consent: All the data points and their source should be made clear to the customer upfront. Also, only data that is relevant to the conversation (not personal) in nature must be captured.
 

Data Usage: Along with what data is being captured, it is very important to inform the customer about the way it will be used.

 

Prompt Guardrails: In the backend of an AI agent one can add some guardrails. Like not inferring finances, health, other very personal things when replying back to a query. 

 

Remember, only the relevant data should be collected, which is data required to sensibly answer the question. And true picture about data collection should be presented to customer to add a value without infringement of privacy.

 

AI is very well capable of interacting with customer and make them feel personal in each interaction by merging data, context and empathy at scale without crossing boundaries of trust. Better customer interactions with less customer efforts and high CSAT scores will definitely help the business expand

 

Customer’s expectation from any service are

·       Quick response on any queries raised

·       Receive accurate response on queries raised to avoid asking repeat questions

·       Faster query resolution with minimum interactions

·       Easy to interact with and have a sense of personal touch by letting them feel valued, heard and understood

·       Expect trust on data privacy

 

AI helps in better interaction with customer by:

·       Collecting the context with consent by pulling required data keeping in mind not using hidden data

·       Knowing customer better and in depth by building customer profile using data from past purchases, past interactions or support tickets, browsing history, etc

·       Use smart prompts for Bots that works on prefilling summary and also acknowledge earlier context showing respect and care

·       Feel customer valued by predicting needs and suggesting next steps that will help query resolution prior customer asks the question

·       Switch tone basis customer tonality and empathize basis sentiment analysis that reflects customer sentiment

 

For example, Account Payable Helpdesk is the function included in Procure to Pay process that handles queries from vendors, internal or external stakeholders related to invoices processed, payments done and PO created.

 

Below are few AI capabilities mentioned that can be used in AP Helpdesk across PTP:

·       Build AI Chatbots for automated query handling with 24/7 support and less repetitive tickets

·       Routes the ticket to required department basis query raised for faster and convenient resolution

·       Captures invoice data using OCR and NLP from documents or emails

·       Proactively inform vendors or required stakeholders on invoice status thereby reducing escalations

·       Resolve complex queries by searching policies, SOPs, etc using intelligent search & knowledge base

·       Adjust the tone basis customer’s response and urgency thereby building trust

·       Flags invalid invoices, duplicate payments thereby preventing financial losses

For me, the real problem with AI in customer service isn’t that it exists, it’s that it often feels like you’re talking to a robot that doesn’t really “get” what you’re saying.

 

One situation that sticks with me is when I had issues with my bank’s mobile app. I called support, and the first thing I got was the automated AI system. The problem I had wasn’t one of the “standard” ones, so it kept pushing the same canned answers back at me. After a few minutes of frustration, I finally got transferred to a human agent. The interesting thing is, even though that person couldn’t immediately fix the issue, I left the call feeling more relaxed, simply because they asked me the right questions, let me explain everything, and made me feel understood. That human part is what AI still struggles with.

If AI is going to make interactions feel personal, it has to do more than spit out quick responses. It needs to listen the way people listen. That means asking clarifying questions before offering a solution. It also means learning from real conversations, not just reading scripts, but actually picking up how humans talk, where they pause, when they use humor, and how they handle frustration. If AI could “sit in” while humans solve problems, it would learn that flow and stop sounding so generic.

Amazon’s customer service is actually a good example of where this balance already works. When you reach out, the AI doesn’t try to solve everything on its own. It takes the basic information like your order number or what type of problem you’re having, and then smoothly hands you to a person if the issue needs more attention. Sometimes it even comes back later in the conversation to wrap things up, like confirming a refund. As a customer, you don’t really notice the handoff, because it feels seamless. And that’s the key: it feels like someone is actually paying attention to you, not just pushing you through a system.

If I were to sketch it out, it’s kind of like this:

Customer explains problem ---> AI listens + asks clarifying Qs ---> Human rep takes over if needed<--- AI can return for simple follow-up (refunds, updates)


The point is, personalization doesn’t mean AI has to act exactly like a person. It means the customer walks away feeling like they were heard. If the AI can do that by clarifying, by knowing when to step aside, and by sounding less like a script, then the interaction will feel personal and trustworthy. Otherwise, people will just keep waiting for the human rep, like I did with my bank.

Let us consider the example of a healthcare customer care support system built to answer queries from patients regarding healthcare plans, check claims status , provide guidance on medical services or book appointments.  An AI agent powered by prompts and flows can deliver the work but need to follow certain guidelines to deliver personalization and staying within the boundaries of privacy & trust.

 

To personalize the experience of the customers, the AI agent can –

1. Provide response referring to the healthcare plan opted by the customer and thus providing personalized answers instead of generic ones.
2. Provide relevant information along with specifics. Eg; if a patient enquires about the amount of medicine to be picked up , AI can scan the details of medicines delivered to the patient in the recent past and provide relevant details
3. Provide discreet  details without making any medical assumptions; eg; if there was a annual health-check-up scheduled last week but not completed, then provide a friendly reminder to the patient.

To be trustworthy, the AI agent can follow –

1. Access only those information which the patient has consented to access, beyond which any PHI/PII data will not be accessed.
2. Provide reference of the documents, plans etc which the AI agent is accessing to retrieve the results. This maintains transparency and trustworthiness.

Thus a customer after interacting with the AI agent will have a better experience as they will receive more contextual information along with sources of information from where the Agent has pulled the data from.

Though the service industry has been defined by standards sets by humans and responses rated based on the overall experience in handling the query, with AI capabilities particularly NLP the engagement levels have increased with hyper personalisation. AI systems are able to ask pointed questions and provide specially customised response in the manner the query was asked by the customer. With a feedback mechanism these AI response are checked for effectiveness and help build layers over the Knowledge base and create historical references. Effectively the next time a similar query is asked the response time is reduced and the script can be hyper-personalised thereby prompting positive sentiment and overall satisfactory experience. 

That quote attributed to Jack Ma—"You should learn from your competitor but never copy"—is a powerful reminder that innovation comes from adaptation, not imitation. For banks aiming to make every customer interaction feel personal, the key lies in learning from other industries and customizing those insights to fit the unique context of financial services.

 

Here’s how a bank can apply this principle using AI:

 

1. Retail: Hyper-Personalized Recommendations

• Netflix & Amazon uses deep learning to adapt recommendations based on user viewing behaviour, time of day, and device used.
• Starbucks customizes offers using AI based on historical order, time of the year, and location.
• Sephora and Derma co (In India) uses AI for skin tone matching and personalized skincare routines.

Banking Application:

• Bank can learn from Netflix, Starbuck and Use AI to recommend financial products based on transaction history, life events (e.g., marriage, home purchase), and seasonal behaviour. They can also Offer dynamic, context-aware promotions (e.g., travel insurance before holidays).

2. Healthcare: Empathetic Personalization

• AI is used to deliver personalized chronic care support and preventive nudges.
• Patients expect seamless, relevant, and empowering experiences.

Banking Application:

• Banks can Provide empathetic financial nudges (e.g., reminders for bill payments, savings habits, budgeting tips).
• Banks can learn to be emotional intelligence through Use of AI to support vulnerable customers with tailored financial wellness programs.

3. Hospitality & Travel: Integrated Journeys

• There are many Brands which integrate flights, hotels, dining, and experiences into a single personalized journey. AI-driven “next-best-action” engines guide users through seamless experiences.

Banking Application:

• Banks can Create integrated financial journeys/ bundle service (e.g., mortgage + home insurance + renovation loan).

Based on above context banks can follow following Recommendations:  

1. Build a “Digital twin” for every Customer
   Integrate data across silos to understand customer behaviour holistically.
2. Invest in (Customer Brain) Real-Time Personalization Engines
   Banks can Use AI to deliver dynamic, context-aware interactions across channels and communication strategy.
3. End-to-End Workflows using AI
   Banks can Learn from retail and marketing leaders who rearchitected workflows to scale personalization. The experience must be seamless across touch points.
4. Human Touch
   Ensure AI complements—not replaces—human empathy and trust. AI can draft and use data to build context. Which can help Personalize not just based on data, but on timing, channel, and customer mood.
5. Ethical AI Use
   Banks must be careful and transparent about data usage by avoiding over-personalization and mitigate bias.

 

 

Most of the e-commerce platforms function as a service. Amazon, Flipkart, Netflix and so on are the best examples. The e-commerce platforms use AI extensively because AI can help in personalizing customer interactions in service environments by using data-driven insights to tailor responses, offer relevant recommendations.

 

The key take-aways are :  

  1. Relevance of the Customer Interaction : Consider the customer-facing process, in an e-commerce support chat, where customers often seek help with orders, shipping, or product recommendations. The interactions will be highly relevant, as the customers expect fast, accurate, and empathetic service, and personalization can significantly improve their satisfaction and loyalty. Relevance will be magnified when personalization directly resolves frustration or adds convenience. 

 

 2. Practicality of the Personalization Method : AI can be used to pull the past purchase history and browsing behavior to understand preferences. Can securely access the customer’s support history, preferred communication channel, and current product usage. When the customer describes their issue AI can then reference the existing data, draw inferences and then suggest tailored troubleshooting steps or guide through the process or point to documentation relevant to their particular issue. AI can also offer context-aware recommendations. If NLP or semantics are in use, then AI can use behavioral segmentation to customize messages and support. 

 

3. Value without Compromising Trust or Privacy : In e-commerce platform we can notice that AI only utilizes information the user has explicitly shared or that is already tied to their account. It never pulls in data from unrelated sources nor showcases a customer's personalized data to any other customer. This adds-on the value of maintaining trust and privacy.

 

4. Use of the Right Content at the Right Time :  Based on the customer issue AI should be able to provide the right content at the right time. This also adds to the relevance, increases the engagement, increased nurturing and higher satisfaction rates.

While designing or creating the AI agents we might have to consider several guidelines, below mentioned are the most important:  

  1. By using Consent-Driven Personalization :  AI need to seek customers for permission/consent before using their data for deeper personalization, and clearly communicate what data is being used and how it benefits them.

 

2. Minimal Data : AI should only utilize information essential for delivering the requested service, should only collect minimal information. This helps in minimizing data exposure and safeguarding privacy.

 

3. Transparency and Control: This rule makes the customer feel that they are in control, gives customers easy access to manage data preferences, review data usage, and an option to opt out at any time (ex: deleting an account). This allows the organizations to  foster trust without compromising service quality, ensuring customers don’t feel their boundaries are being crossed.

 

 

 

 

Ai can make customer interactions feel personal if it combines context, Intent and empathy. It is important to train the AI to understand who the customer is, what they need, and how best to respond in a way that feels natural and humanly.

1. Context Awareness
    Use customer profiles using data from history. This allows to tailor the responses.
2. Sentiment Analysis
    AI should detect if a customer is happy, confused or frustrated. Also adjusting the tone, urgency. This makes a sense of being truly heard.
3. Proactive Engagement
    Instead of waiting for a customer to ask for help, AI can understand the needs. (based on browsing, behaviour AI can provide a support and suggest solution)
3. AI powered summaries for Agent
    Before a human joins a conversation, AI should provide a quick summary of the customers history and the mood. This would help the human agent to respond with empathy and precision.

In Alternative Investments Transfer Agency, this can add more value

The Challenge in Alternative Investments domain
    Investors and CRMs expect quick and accurate answers.
    But they also want to feel their query is treated uniquely. Generic AI responses feels robotic.
    Also need to ensure that too much of data or overused customer private data can bring a feel of intrusive.

Traditionally, Investor submits the subscription agreement and waiting for the update from CRM/Transfer Agent.

1. Contextual acknowledgement/Responses - Instead of saying a generic response that "your documents are being reviewed", AI can provide a contextual way of response, "your KYC document for fund XYZ is being reviewed. Only Identity proof and foreign Swift code need to be confirmed/verified" along with Turn around Time. This way the customer not only getting a timely response also a clear and precise response.

2. Proactive updates - This way we dont have to wait for Investor to ask for help. Based on the history, AI would be able to provide periodic updates by understanding what he might need to know.

3. Friendly language - can improve the experience for the investors on a particular situation.

4. Seamless Human handoff - When there is an issue, AI can provide all summary to CRM/TA Agent, this way they dont have to ask multiple same questions to investors. It is also bring efficiency.

This way, Quick , Personalized, precise responses can be provided to Investors. This brings more trust as they feel they are "known". Investor would feel their case is handled with care, Quick turn around also compliance risk can be reduced.

In today’s world , we have to manage the customer’s  expectation and perceptions when interacting with AI across digital platforms .

 

At the bank , we do have the Customer kiosk for the customer to trigger any request . With the help of a virtual customer assistants which combines NLP and ML it may create human like interaction in a more personalised way  whereby it handle complex tasks for the customer without crossing boundaries of privacy or trust.

The AI solution acts as a self service tools which recognize the preferences of the customer and make relevant suggestion to add value in the interaction with the customer.

 

With the use of predictive analytics, the AI solution looks at the past behavior of the customer and compares it to real time patterns to figure out what the customer needs and suggest some recommendations. By analyzing customer data and behaviors, The AI solution can deliver highly personalized recommendations, responses and support tailer made recommendation to the individual customer needs. In this case, based on the previous loans taken or pattern of transactions done using their card, the AI solution can help the customer to take a decision easily.

 

 The AI solution can predict when customers might encounter problems and offer solutions before they even realize there’s an issue, increasing customer satisfaction and loyalty. The AI tools can read the tone and emotion in a customer’s message. With sentiment analysis technology the AI solution  evaluate language cues to understand how someone feels, whether they’re angry, frustrated or happy. This helps to respond faster to unhappy customers and handle tough conversations with more care. In the event there is an issue in their account and some wrong transactions was done, then the AI solution

 

To summarise , the evolution of AI as a simple Chat bot which interact with the customer on simple FAQ has transform over the time to a more human like interaction which is more personalised and sets its boundaries.

 

As ,2W service and spare parts domain is evolving  -AI has the potential to become more than trusted advisor that elevates entire customer experience and give customer delight.

But, to do it effectively , AI must balance personalization while taking care of privacy and trust is not compromised .

1. Personalization without violation 

   -Suggest engine oil brands based on past engine performance data and weather conditions

   -Suggest customer to carry spare tyre and coolant  in rough/uncertain terrains.

This personalization can be done on- device , which will ensure no personal data is compromised

2. Bike predictive maintenance

    - Instead of sending generic reminders, AI can analyze the past data to predict what needs to be replaced at certain time intervals . this will result in less bike downtime and improved bike performance and will result in personalised vehicle health monitoring .To ensure privacy, AI models can use decentralized learning, allowing data models to improve locally on customers devices, without sending any personal ride data to central servers.

3. Spare parts recommendation to user based on adaptation

    -AI can recommend spare parts based on current usage pattern and performance against data of the other users vehicle data to recommend OEM parts or local parts to balance durability , price and fit . User needs based trust drven process.To ensure privacy , AI can work with zero knowledge systems and offer suggestions without revealing user identity or prefrence to 3rd parties.

AI in the bike and spare parts sector isn’t just about automation — it’s about augmentation. When designed ethically and deployed transparently, AI can make every interaction feel customized, every suggestion feel smart, and every transaction feel secure.

 

 

One of the AI approaches to deliver personalised chatbot services without breaching user privacy is by using the user interaction history with the system, with which system will interact with user needs and preferences and offer user alternatives based on knowledge base, this will give user better personalized experience and better results and longer times of chat with customer before triaging ticket to agent to add him to chat. This standard process comes as part of the following outline:

·       Understand the situation: after the user typed in his request, the system will analyse the need based on the knowledge base and the user's history, then.

·       Evaluating the user request: based on the user's KB, the AI chatbot shall anticipate the real need and differentiate if the request is a valid one or not.

· Personalised service by proposing a tailored solution based on the user's KB, preferences and additional new requirements of the current request

 

 

Ø  Let’s take an example from my domain (IT Helpdesk) serving around 1300 local staff:

·       Relevant Scenario:

A user from the transport team where he is expecting the onboarding of a new driver ASAP, for that he jumped into chatbot and listed his problem, the chatbot got back to same user chat history and asked him several question (after confirming the reception of the new request ); is the new Driver based in Sheldon site in QLD, does the user need access to system and requires a M.S teams license, do you think you can temporarily spare a phone for the started, and so on, this level of chat enhances the service for this users and others without the need to get connect you to one of the IT Helpdesk agents.

 

• Practicality of the personalisation method

The practicality will be affected by two main factors, the first one is the depth and readiness of the knowledge base, meaning the more the KB is organised, and the level of data that exists is enough for the AI tool to use and build proper scenarios

The second factor is by the user himself, where the more precisely the user can illustrate the issue, the better results they will get from the chatbot

 

Other factors may also affect this, such as the complexity of the system, the maturity of the IT helpdesk FAQ, and the Knowledge base.

• Balance between adding value and maintaining trust

Building trust between user and AI in general and Chatbot in specific is an ongoing concern and considered part of the modern AI discussion. The following are guidelines to build a trusted Chatbot:

·        Ongoing enhancements, at the level of design, agility and maturity of the KB

·        Make the chatbot privacy policy available for users as part of a transparent approach

·        AI is meant to think like a human, but not necessarily act like a human, meaning that we need to build a chatbot that is as friendly as a human, but when it comes to a human chatbot, precise replies and questions play a better role in building a trust relationship between the user and the chatbot

·        When sharing sensitive data, it should go through a secure/encrypted channel, which will increase the trust of the end user

Use case in PTP Domain One of the prominent use case is Vendor query resolution system in my domain. In Procurement to Payment domain, vendor reaches to know the status of their Invoice which was shared by vendor during the product/goods delivery. There could be various status of Invoice processing cycle before any organization release payment. Vendor reaches frequently to any organization until the payment has been released. Vendor expect prompt response to know the invoice payment status so that they can plan their cash flow. In the other hand Organization face enormous challenges to solve the vendor query and vendor get offended because of late response. Let me summarize the precise challenges.

1> Often Vendor support team not aware about the processing status of a Invoice

2> They refer multiple tools, such as excel, ERP, Bank Portal etc. to get the processing status of any specific invoice

3> Since the knowledge or information of Invoice is scattered and fragmented to many tools hence the agent spent more time to consolidate and share with Vendor. 

4> Vendor make escalation because of this kind of delay in response.

5> Supplier/Vendor will be more irate if they receive any generic response about their invoice processing or payment status. They want specific answer about the invoice status such as "Payment released", "Payment credited" , "Payment on hold due to goods are in transit" etc.

 

Solution

1> AI agent powered by prompt and flow delivered personalization response to individual Vendor queries.

2> The AI agent enquired the Invoice number to Vendor. Once vendor provided the invoice number then the flow first attempted to search the internal KB

3> The KB was created by API integration of various fragmented tools such as SAP and many different Bank Portal

4> Now when vendor ask for any specific query about any specific Invoice then the agent could able to search the internal KB to provide the specific information about the invoice processing status

5> In case the agent type incorrect invoice number then AI agent refer the external model capability and ask to verify the Invoice number.

 

After deploying this solution we observed significant reduction in Vendor escalation regarding late payment issue and moreover the vendor satisfaction also was significantly enhanced.

 

 

 

Congrats to B. Ravi Sankar! 
Your AP Helpdesk example was the winning response — practical, relevant, and trust-focused.

Appreciation to Gagan Kathuria and Debanjana Basu for excellent contributions with strong insights and practical use cases.

Special mention to Arunungshu for a highly practical vendor query resolution case.