Q 792. When AI takes over parts of a process — such as answering queries, making recommendations, or triggering actions — the roles of humans around that process also shift. Think of a process in your domain where AI has been (or could be) introduced. How would the responsibilities of team members change? What new roles or skills would be required, and how would you redesign the process to ensure human-AI collaboration works smoothly?

 

The best answer will be selected on the basis of: 

• Relevance of the chosen process and role changes
• Clarity in identifying new responsibilities or handoffs
• Practical insight into enabling effective human-AI collaboration

 

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• Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error-prone because our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/

• We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be considered for winner selection.

 

In the Business Process outsourcing domain, our Continuous Improvement team will play a vital role in driving improved service delivery by improving efficiency & waste reduction. If AI has been introduced, the core activities of analyzing data, automate redundant or routine manual activities & identifying the logics or patterns from the data will be very simple. However the Continuous Improvement function will still play certain roles.

 

Without AI, Continuous improvement team focus on:

• Process mapping through SIPOC, VSM
• Identify process gaps through audits or collaboration with Stakeholders
• Do root cause analysis to understand all possible causes
• Lead Improvement initiatives through Six Sigma or Lean methodologies
• Track data and compare pre & post implementation impacts.

If AI could be introduced, then

 

AI Roles:

• Monitor performance metrics & highlights areas on concerns in real time
• Analyse the feedbacks or responses through LLMs
• Automation of all reports & dashboards
• Predict trends / issues by continuously analyzing performance
• Introduce better models based on emerging needs

Responsibilities of Human roles:

• CI Analysts will focus on evaluating AI-driven observations for strategic fixes.
• Process Consultants will explore new design collaboration models
• Trainers will concentrate on enhancing the workforce with emerging technologies.
• Data analysts will shift focus on creating more predictive / prescriptive models.

With AI merger, the Continuous improvement team might go for upgrading new skills such as AI Literacy, Digital Tooling, Strategic thinking, etc to understand how the models work or how to interpret the AI insights. Also learn to work with fine tuning the automated dashboards aligning Ops requirements.

 

To ensure a smooth collaboration between the Humans & AI, certain process redesign might require.

• AI act as first monitoring layer to watch metrics & suggest improvement areas in real time
• CI team will review the suggestions, validate & prioritize aligning strategic directions
• CI team will review the logics or prompt regularly to ensure the model accuracy & avoid false alarms.
• Collaborate AI suggestions with realtime feedbacks to derive strategic solutions.
• Define clear roles of AI & CI teams.

This way Continuous improvement team will upgrade from traditional problem solvers to proactive change agents.

This solution is clear and easy to understand. It also gives fantastic instances of how AI and people could work together.

Real time Scenario - You decided to help clients who had questions about financial research.
It can take a lot of time and energy to answer clients' inquiries about ESG ratings, how indexes work, or how to give credit for good performance. In the past, analysts and customer support reps had to sift through data repositories, old reports, and policy manuals by hand to get the proper answers promptly.

How to Use AI in Your Daily Life Every Day
AI systems, such as large language models that learn from their own data, might be able to do a lot of the work on their own now:

• Quickly going through questions and sending them to the proper group.
• Putting together answers depending on what we've said or written down.
• Giving customers real-time connections and information based on data from both structured and unstructured sources.
• AI makes a lot of changes to how teams operate together.

Before: Client Service Analyst; Now: Client Service Analyst Responding right away Making sure that the answers AI gives are right and follow the rules
Research Analyst: Helping with follow-ups that give you further details AI needs managers and team leaders to provide it the appropriate knowledge and things that algorithms can't see. Getting things done that you need to do Making guidelines about when AI and people can't work together and keeping an eye on how they do it

AI Work Flow Needs New Jobs and Skills

• The designer looks at data, builds decision trees to help them make sense of things, and makes sure that problems can be reported up the chain.
• Prompt Engineer/AI Trainer: provides clear direction to the model with real time examples for further refinement of model.
• Domain expert: validates AI responses by performing QA/QC audits/checks to identify any errors, this is called a human-in-the-loop validator.
• AI Trust and Ethics Champion: Ensures all AI proposals are simple and provides good understanding to clients and addressing any related questions.

Ecosystem has made it easier for people and AI to operate together.
Input Stage: Customers can ask questions by email or through a portal.

• The AI Triage Layer categorizes clients into different groups based on priority of their needs, what they are, what level they are at and How to serve:
  • AI will answer right away, but only if you're sure.
  • Get someone else to look at it if you don't know
  • If you can't figure it out, give it to an analyst.
• Review Layer: Reviewers look at the AI's suggestions and add more information.
• People who work with clients and analysts give things a score from 1 to 10. AI gets smarter all the time by making minor modifications and enhancements.
• Making sure that everyone at work gets along: People may see why and how an AI generated a suggestion via Transparency Dashboards.
• Editable Response Templates: This way, folks don't have to start from scratch; they can just make what they currently have better.
• If clients or the government complain or ask inquiries, advise them not to use AI.
• Teach and Get Buy-In: Don't just show teams how to use AI; also show them how to think like AI. For example, teach students how to deal with people who are excessively convinced of themselves and how to spot bias.

AI might be the first thing clients see, which would speed up responses and offer workers more time to get to know each other and improve things. You will need to tweak it a little bit for it to function, though. Tell the person who gives you a job that learning loops are constantly open and that AI is being used to aid people instead than taking their jobs.

The selected process is Reviewing inpatient medical coding quality 

Normally, a coding quality team undertakes the following responsibilities: 

• Reviews a sample of coded charts
• Mistakes with flags, such as missing MCC/CC or using the improper DRGs gives coders input.

Enhancements to the precision of accuracy scores using AI in several forms include for error prediction or DRG suggestion. 

Real-time scanning of coded charts anticipating possible coding errors or DRG mismatches is now possible by AI 

Spotting human-inspection, high-risk charts 

Pre-screening with AI:

Every coded chart runs via AI, which notes possible issues (such as missed MCCs). 

Review by a Human-in-the-Loop: 

Reviewers or coders confirm flagged charts. 

Decisions are recorded as true or erroneous in AI  

Quality Analyst Track error trends, escalate patterns Train AI models using labeled examples, monitor drift, and identify false positives

Team Leader assigns audits, generates reports, coordinates AI-human workflows, defines handoff points, resolves escalations.

Align with coding standards, retrain ML models, and optimize prompt logic with the help of an AI Specialist / Clinical SME (who was previously not a member of the team). 

On a weekly or monthly basis, feedback is used to retrain models, improve prompts, or edit regulations. 

Lead + SME is responsible for mediating any conflicting coder input or low-confidence AI flag. 

Need New Talents This, though, is only successful if the process is modified to include AI handoffs. Using analytics and model governance improves roles. Openness and repetition help to develop trust.

A dialogue from a yesteryear movie goes like this " Take your greatest fear and multiply it by X". I wonder if this is how today's workforce feel!!

While it is true the whole business world is fascinated by the development and prowess of AI, the global workforce at least from low to mid-level feels a strong sense of fear of job security and panic. Especially people above 40 years.

Some say AI can automate repeated, mundane tasks so as to free some bandwidth for the employee to focus more on creative, enriching and fulfilling tasks in the current or new role. While others say it replaces many jobs which are labor intrinsic. We know both are true though skewed towards later.

 

In my view, AI system is and has to be used as a tool to empower workforce rather than be seen as a threat to the workforce. AI system by itself is not responsible for impact on the workforce with respect to layoff, but very poor Strategic Planning by the leadership of the companies Is.

 

Now looking at the topic, what happens if AI becomes part of the team, how would the roles change, well I can say ideally the following needs to happen even before companies plans to implement AI system to ensure there is a smooth Human –AI collaboration.

Objective of the implementation of AI system and its Impact:

What is the business problem that AI is going to solve? Is it to improve efficiency, improve customer satisfaction, increase top line or reduce bottom line (most relevant eye opener for business) etc., what is the impact that it is going to bring to specific tasks, processes and roles? An assessment needs to be done and quantify the impact clearly.

 Is it going to automate the tasks done by a role if yes by what percentage? This is the basic activity leadership team has to do, as this will impact the work force.

Communication, Transition plan, feedback mechanism:

Once impact assessment is done, there must be a clear, honest, transparent communication from the leadership team to the employees. Leadership should very clearly state the reason for the implementation and how it will directly or indirectly affect the workforce. Leadership should be absolutely clear that AI is to empower employees and not replace them. It could be an opportunity for the employees to re-skill, upskill wherever there needs to be a transition in roles.  Also allow employees to give honest feedback about how they feel, what their fears are, any inhibitions etc. This way leadership can be empathetic and can understand and act on important pointers.

After all technology serves and empowers its most valuable resource: its people

 

Analyzing an example where AI can be a part of the team and how the roles can change.

Let’s say a company is planning to automate the routing of a IT support ticket.

The current process is; IT support tickets are raised from across the region on a centralized IT support tool. There is a resource in IT support whose major role is to monitor the queue and allocate it to a support resource who has the least open tickets in his/her queue.

IT support team receives on an average 35 tickets a day, approx. 4 to 5 ticket allocations to be done every hour manually. It takes around 5-7 minutes to allocate a ticket to appropriate resource based on the least ticket logic. Though the ticket queue monitoring resource will have approximately 20-30 min an hour to work on support tickets, in reality the ticket frequency is dynamic, so the resource needs to break frequently between resolving the tickets and to monitoring and allocating the tickets to appropriate resource.

Major problems here are ticket queue monitoring and allocation of tickets is not considered to be an intellectual task to be done by a skilled resource and due to this the ticket resolution by the same resource is becoming in efficient.

 

Let’s consider that due process as mentioned above is followed before implementing the AI system and inducting it into the IT support team, The AI will now monitor and automatically assign the IT support ticket to appropriate resource based on the least ticket queue logic within few seconds.

The improvement from 5-7 minutes to assign a ticket will now be changed to few seconds. Moreover, the resource who handled this task is now free to resolve more tickets than before and there is an opportunity for that resource to upskill to not only giving inputs, testing the AI system, but also can be a part of improvement teams for future AI projects as this experience becomes invaluable for the company.

So this AI system implementation has relieved the resource form a repetitive, mundane task but also has allowed to learn and up skill and open a door of opportunities for being more creative in problem solving across the company.

Hence I see AI becoming part of the team will improve team by empowering them, provide opportunity to upskill and transition to new roles.

If companies say that they have to layoff due to automation or optimization then there is one classic example, Microsoft laid off around 2000 employees in 2021 citing various reasons, two years later there were close to 2000 new employees recruited by Microsoft with new age tech skills with more pay out! Ultimately there is no reduction in headcount but one set of people replaced by another. I believe this trend will prevail for few years, until companies realize that the value of a resource is much greater than life cycle cost of AI.

 

With AI, doing more and more of our human tasks through automation, there are several things that come to mind.

1. Since AI will be replacing much of our coders today or anything that we tactically do within the data realm, this is going to fundamentally shift the responsibilities of the coders if they want to remain marketable.  They must allow themselves to change with the technology and wrestle with where and how they may work within AI.  Their responsibilities will change.  They will be expected to change.   

 

2.  One of the people I respect said that we, the technologist, should not look at ourselves as "doers" of technology any longer, but as "directors".  I think this is absolutely right on.  Our roles are going well we can create and directly AI to create the solutions. AI is going to fundamentally change the way we think about what is productive.  How we once defined professional "success" will also fundamentally change because we no longer have to "do" the things or "perform" the things that we once measured our success upon.  We must lift our eyes and imagination higher now, out of the "doer" mentality to a "what can we imagine", "what are the possibilities", "how can I use AI technology be harnessed to serve humanity in ways", that were just a few short years ago, unimaginable.   

 

3. Designing a human-AI collaboration process, I think, is a huge question that we are all scratching our heads about,, largely because this is all very new to the masses.  But like any good design or solution, we must always be mindful who AI is serving.  Implementing AI for the purpose of AI, should NEVER be the goal.  As AI Solution Architects, we must always make sure that whatever is designed by us, created by AI, that it comes back to the question, "does it make a difference in making the life of our customers, or humanity, richer and better?"  I think keeping "US" central to the goal of anything we design with AI, it will become more apparent where it makes sense to implement human-AI collaboration.   

With the advent of any new processes, technologies there has to be change in the Business process flow.

Impact of the AI introduction in the Business process will vary depending on how much changes have been brought in the As-Is process in terms of the role change, job description etc.

So, while working in the Program management team, our first encounter with the AI related term or development was through the introduction of Notemaker powered by Gemini (G-suite).

Earlier before the addition/introduction of AI, the Program managers were tasked with leading the meeting discussions and at same time keeping a tab with the Minutes of meeting and then ensuring that we are sending the reminders, doing follow ups with the swim lane team members and ensuring that the actions are closed.

Before the introduction of the AI in our Program management team there were primarily below roles of Program managers:

1. Collection of requirements i.e. Epics

2. Rationalization of the Epics with Product managers

3. Daily sprints and sprint retros review

4. MVP product demos

5. Program Increment review

With the introduction of AI in G suite i.e. Gemini and Atlassian Intelligence (JIRA) there was considerable change in the Job description for us as Program managers as the AI team members were able to keep note of the meetings during the PI planning, daily sprints, sprint retrospective and then the Stories and Epics progress.

So, the role of Program managers shifted from facilitator to being more inclusive in the problem understanding of the subject and then being part of the decision making for the completion of the tasks.

The time which was earlier spent on action tracking and monitoring of the actions for closure shifted to being able to spend time on the creativity, strategy, Risks and focussing on the risks mitigations. We were able to focus on derisking the negative risks and working on the positive risks.

Job description changes were brought in by the Human resource department to document the changes in the job role as there were considerable changes in the job description and subsequently the changes needed to be done in the Goal settings, balance scorecard for the performance appraisal.

The Process Excellence team also needed to update the SOPs and Process flow diagram to adopt the changes which were brought in because of the introduction of AI and changes in the role descriptions of the team members.

 

The roles of say Program managers, Human resource and Process Excellence and other team members (part of Swimlane) changed to more strategic thinking and action oriented from the previous one operational activities and challenges to being part of the creative and strategic contributor. So, the introduction of the AI and job description changes have been a boon for many as this has resulted in the skill development and skill enhancement of many of the team members. 

In certain other cases there are redundancy of certain roles and new role requirements needed to be created like the Business Analyst, Financial Analysts and other roles no longer required and in place of that someone from the Financial Planning and Analysis needed to be aware of the AI usages for the analysis part of the work.

HR needed to work on the Job description for most of the role and this was a larger project where certain roles become redundant and certain roles needed to have job enlargement.

In our payment posting process, there’s been ongoing discussion around how we can bring in AI to ease some of the workload. Honestly, it makes a lot of sense. A big chunk of what our teams do here is repetitive like

• Matching payments from ERAs or scanned EOBs,
• Entering them in the system,
• Checking totals

It is rule-based and time-consuming. Payment Posting process is more suitable for automation.

Now, if AI comes in, it is not going to replace the team. That is not the point. What it will do is take over the basic stuff like

• Reading payment files,
• Identifying the right accounts,
• Applying the amounts where it’s confident.

So instead of spending their day doing data entry, our staff will shift into more of a reviewer role. They will be checking what the AI did, fixing anything that looks off, and stepping in when the system does not have a clear answer.

 

Long Term Focus of the Team:

It is not about speed anymore, it is about accuracy and judgment. People will need to spot inconsistencies, validate edge cases, and be comfortable saying, “Hey, this looks wrong and let me fix it.” Also, there needs to be a way for them to flag issues easily like when the AI misses a particular payer format or misreads an adjustment code. That feedback should go right into refining the system.

 

The leads or supervisors will also have new responsibilities. Instead of just managing productivity, they will have to keep an eye on how well the AI is doing. Are there repeat errors? Are some payers consistently causing problems for the system? It is a bit of a learning curve, but over time, the system should get better. Especially if we create a strong loop for learning from mistakes.

 

Key aspects to consider:

We will need to train people not just on the new tool, but on how to work with it. It is not about using the tool blindly. They need to know when to trust it and when to step in. That balance is going to be key for this to work smoothly.

 

So, in short AI can help a lot in payment posting, but only if we design the process to support both the technology and the people. The human role does not go away, it just becomes more focused, more skilled and honestly more interesting.

As a part of the Business and process Excellence team’s across different organizations I have been involved in numerous projects involving process optimization and implementation of RPA’s.

Having managed omni-channel contact centers, I have closely worked on implementations of ACD’s (automatic call distribution) and Autonomous IVR's for telephony set-ups. And have been fortunate to be a part of Live Chat implementation projects involving Chatbots in recent years.

With the expansive capabilities of AI, what we had set out to implement was an interactive, self-learning, autonomous “Dynamic Packaging Module-DPM” for an OTA’s Holidays business.

The idea was to integrate the DPM and Chatbot on the site, which would be able to handle Customer Inquiries related to the packages involving mostly customizations and any related questions to T&C’s, payments, offers, amendments and cancellations, upgrades, visa, insurance etc. immediately.

Given, the fact that 60% of the inquiries were for “Customized Packages” it was important for our teams to be able to handle such customers by offering complete details “tailored” to their requirement in the First Interaction itself thereby improving conversions and customer experience, hence the decision to go ahead with the project.

So, in addition, to building a Backend Product loading and managing tool involving the integrations of supplier API’s, Direct / Extranet contracts, GDS and Airline feeds, offline packages, ground transportation and ancillary services. We needed a Front end, which allowed both our teams and the End customers to be able to “Customize packages” as per their needs on real-time basis, while making use of the AI assistant. And finally, all interactions, requests, quotations to flow into the CRM and ERP tool for overall booking management, human agent and AI model training and retraining, Quality Audits using AI for all interaction summaries.

The Front-end or the “DPM” was built using AI/ML algorithms, using on-premises LLM’s, which were trained on some 100,000+ itineraries for countries across the world. The AI QA tool had been trained on Sales scripts, product knowledge, soft skills and Quality monitoring sheets to score conversations. Both these tools had their respective KB’s.

Let’s look at the project implementation in terms of restructuring of processes, new roles and responsibilities both for Human agents and the AI, handoff protocols laid down, Human-AI Collaboration framework and efforts, Phase-wise implementation plan, Training requirements for the team and Revised KPI’s.

AI-Integrated Sales Process for Holiday Package OTA

Revised Process Flow

 

-          Initial Customer Contact

• The AI chatbot initiates the first interaction within 15 seconds of a customer visiting a Landing or Package page, offering assistance to “Customize a package” or offer the “off-the-shelf products”.

• The AI qualifies the lead by gathering destination preference, departure city, travel dates, trip duration, no. of persons traveling, travel intent – couple, family, honeymoon etc. and budget, if any.

• By using set parameters, the AI assesses the complexity of the inquiry.

• Basis the complexity scores the system directs the next steps for the handling of the query.

 

-          Queries Handled by AI (70% of initial contacts)

• Inquiries regarding package availability and pricing

• Standard booking changes (dates, passenger details)

• FAQ’s bout policies (cancellation, baggage, visa requirements)

• Basic information on destinations and weather

• Payment and confirmation support details

• Post booking status updates and reminders

 

-          Human Agent Involvement (30% of initial contacts)

• Multi-city itineraries that require customizations

• Group bookings for 10 or more people

• Special requests incl. accessibility, special medical needs, meals etc

• Managing refunds that involve exceptional circumstances or exceed standard refund policies

• Catering to High-value bookings above $X,XXX per person

• Handling customer escalations or requests where customers explicitly ask for human assistance

 

Evolution of Human Agents: From being Order Takers to Travel Consultants

The agents are now responsible for offering strategic travel advice, instead of just processing standard transactions. While focusing on customer motivations, recommending upgrades, and creating WOW experiences, the agents now act as Travel Curators.

 

-          New Core Responsibilities of Human Agents

• Curating complex itineraries involving multi-city routings

• Developing long-lasting relationships with HNI and repeat customers

• Promoting and upselling premium experiences and services

• Handling sensitive customer service-related issues

• Ensuring the quality of AI recommendations by regularly Auditing conversations

• Training the AI system through feedback and updating information in AI KB for retraining

 

AI – Human Handoff Protocols

• Full conversation history and customer profile to be provided by AI to the agent with a ‘Warm transfer process’ to be followed.

• The system marks the ‘Priority’ to define the urgency of the inquiry and provides the previous interaction sentiment.

• AI provides recommendation to the agent on the suggested action items based on customer type.

• AI to ensure customers don’t have to repeat themselves through smooth context transfer to the human agent.

• Handoff scenarios include – Complex itinerary requests, emotional or sensitive situations, HNI customers, technical issues or complications.

 

Collaboration Framework b/w AI-Human

AI support by Human Agents

-        AI training and feedback

• Agents flagging incorrect AI responses through live monitoring and post-chat audits.

• Updating new product knowledge directly into AI KB.

• Creating templates for recurring complex scenarios.

-         Quality Assurance and Risk management

• Reviewing AI conversation transcripts on regular basis for accuracy.

• Providing feedback for adjusting AI tone and persona.

• Identifying and recommending cultural nuances for regional and international customers, which AI might miss.

• Ensuring AI responses comply with industry regulations, company policies and meets customer privacy standards.

-         Data enhancement

• Tagging successful booking and upselling conversations for AI learning.

• Provide inputs on travel seasons (peak, off-season, shoulder) and booking patterns for AI to incorporate.

• Highlighting objection handling patterns to assist with AI’s response improvement.

• Providing SWOT analysis against competitors for improving AI pricing algorithms.

• Regularly calibrating to improve AI response accuracy.

 

Real-Time AI Support for Agents

-          Dynamic Sales Assistance

• Recommending agents with relevant packages during live conversations.

• Alerting agents of any ongoing flash sales or inventory updates mid-conversation.

• Offering real-time access to competitor pricing during conversations.

• Identifying and recommending potential upsell opportunities based on customer profile and sentiment analysis.

 • Offering alternatives in case the customer rejects the initial recommendation.

• Providing real-time destination specific expertise when agents handle requests for unfamiliar destinations.

-          Quality and Administrative Task Automation

• 100% QA of customer conversations.

• Monitoring conversation sentiments and raising flags when necessary.

• Identifying knowledge gaps and recommending relevant training’s.

• Summarizing of customer conversations and updating CRM records.

 • Scheduling follow-up emails, callback reminders based on the conversation and commitments made by the agents.

-          Booking flow optimization

• Automating mailers to customers with booking and document checklist.

• Pre-filling of booking forms with customer data received during query and quotation stage.

 • Validating Customer’s travel documents for their visa, immigration and validity requirements, ticketing and booking confirmations etc. and alerting agents for any identified issues.

• Generating the payment and cancellation schedules basis booking and travel dates

• Populating instalment and discount offers recommendations.

-          Performance Optimization

• Tracking conversion rates by agents and recommending relevant trainings for improvement.

• Sharing insights into best practices and successful sales patterns.

• Recommending staffing for aligning Work force according to peak intervals.

• Tracking C-SAT Scores depending on the type of interaction and customer feedback.

 

Gradual Phase-Wise Implementation Strategy

• Phase 1: AI manages only basic FAQs and availability checks.

• Phase 2: Introducing booking processing and payment handling.

• Phase 3: Adding multi-city, complex package bundling and recommendation algorithms.

• Phase 4: Achieving the complete integration with personalization features.

 

Training Requirements for Agents

• Tech skills for navigating through the AI systems.

• Advanced consultative selling techniques training.

• Training on cultural sensitivity for regional and international markets – New markets.

• Advanced CX training for crisis management and de-escalation techniques.

 

Introducing a Dual layer of KPI’s

-          AI KPI’s

• Response time – Less than 5 seconds for 95%.

• Deflection rate – 75% or more of routine inquiries handled by AI.

• Accuracy rate – 95% correct information provided for each interaction.

• Conversion% - Glide path targets for the first 06 months starting from 05% to 10%.

• Availability – 99% uptime during business hours.

• Handoff efficiency – 95% of AI to human transfers happen without customer repeating themselves or getting frustrated.

• C-SAT scores - Above 4.25 out of 5.

• Average order value – Increase in AOV’s by 15% MoM.

 

-          Agents KPI’s

• Complex booking conversions% - Glide path targets for the first 06 months starting from 15% to 30%.  

• Up sales % - 30% of the bookings converted accept upgrades.

• Customer Lifetime Value / Repeat customers - 30% increase in customer retention for agent handled customers.

• Handoff efficiency – 90% of the AI transferred interactions to be resolved within first agent interaction.

• Escalation resolution – 95% of the complaints to be resolved within 48-72 hours

• AI training and feedback contribution – Achieving weekly and monthly contributions targets towards AI improvement and training.

• C-SAT scores - Above 4.5 out of 5

 

Following an integrated approach, we were able to transition from a transactional to a consultative sales process. While AI handled the maximum volume and routine tasks, this allowed the human agents to focus on building relationships and solving complex problems.

As a result, we were able to see faster response times, improved CX, and increased revenue per booking.

The Bank has a Complaint Desk, comprising of a Complaint Officer and a Coordinator, working under Retail unit and reporting to Head of Retail. This is a unit that has lots of manual steps in its process and involvement of multiple staff within the bank whereby the collaboration of Human and AI is a must.

 

The process starts from the different channels that the bank has , where it can register complaints or a request from the customer via

 

1.        Face to face by customer

2.        Through phone

3.        Through email

4.        Website

5.        Letters / Suggestion Box

 

The nature of the request can come in the form of a query or a complaint and this is where there is a filtering done by the person acknowledging the request in order to decide on its nature :

 

1.        Inquire on type of complaint.

2.        If query;

2.1    liaise with staff /department concerned.

2.2    To respond to client immediately and resolve issue.

3.        If it is a complaint,

3.1     To ask client to sign a complaint form

3.2    Inquire on the issue, root cause, details from branch staff concerned or relevant department.

3.3    Escalate to Complaint desk by email under copy to Head of Retail.

4.        Issue letter to client upon advice from Complaint desk

5.        Log on LDC in case any operational loss

6.        Prepare paper for approving authority in case of any refund to client.

7.        Send letter to HOR for 2^nd signature.

Each department or unit has its own way of handling the request or complaint in the following way:

1.1                SME Level

 

1.        If it is a complaint,

3.1 Received by RM, he/she shall handle the client & resolve the issue

3.2 If issue not resolved, log on to complaint ticketing system

IN most of the cases, complaints are received through mails  or website. Based on the fact it is an administrative activity, IT operator channels these request to Complaint desk, who in turn sends mail to SME unit for inquiry.

1.2                Corporate Level

 

1.        Once a Query/complaint is received at the unit, same is sent to HOD.

2.        HOD sends to the respective RM looking after the portfolio of clients for whom the query/complaint was received.

3.        Matter is tackled at RM level after consultation either within the department or with any other department concerned with the case and HOD is copied/informed of all steps/progress.

4.        Copies of the correspondences pertaining to the complaint are kept in file.

 

1.3                SAM Level

1.        Most of the time Complaints desk channels complaints to SAM

2.        HOD SAM refer the complaint to the RM in SAM handling the file.

3.        RM calls client & tries to resolve the issue.

4.        If unresolved, escalate to HOD

5.        HOD tries to resolve the issue, talk to client

6.        Letter is sent to client

7.        Update Complaint desk

Ultimately all request or complaints are channeled to the complaints unit and the internal process requires lots of follow up to ensure prompt response to the customer

1.        Upon receipt of complaint, the details of the complaint is log on an excel format.

2.        Then Identify whether it is a query or complaint.

3.        Inquire & follow up with respective business unit

4.        If matter resolved, update the excel

5.        If unresolved, escalate to senior management

6.        Prepare letter to customer

7.        Prepare reports for Complaint forum (Compliance)

8.        In case of any compensation, give assistance to Business unit to prepare paper for approval.

9.        Prepare reply letters to regulator for cases reported by them

10.    And report submission on a monthly or quarterly basis.

 

In all the above units , either the RM/HOD/Complaints officer and Complaints coordinator are involved in handling the request of the customer.

If AI has to be introduced to this process , then the following changes will happen in the process and certain activities within the roles of the front liners will be changed or eliminated.

 

Nowadays the use of mobile and website are more adapted to the trend of live of people. And more and more people make use of these means to send their request

With the introduction of AI,

The activities at the initial stage which determine whether this is a complaint or a query can be handle by AI just by feeding in some parameters for the system to be able to channel the request to the appropriate stakeholder

 

Also the frontliners like the branch staff will not have to attend to this tasks of sending this to the department or stakeholder concerned and do a follow up

By providing some filters at time of input by the customer can already save the bank time and resources to complete this task.

On top of it also , due to regulatory requirement, the bank has to acknowledge the request or complaint from the customer within a minimum delay. Which at time if done by human , this can be omitted or done outside the SLA

The recording and tracking of the request or complaint will be done by the AI without anyone having to log the record on the system . This also reduced the tasks of both the officer and supervisor who are involved in the creation , verification and allocation of the ticket.

On the reporting part , whereby the bank has to send a report to the regulator on a monthly or quarterly basis, this can be handle by the system through AI.

In a nutshell the collaboration of Human and AI works smoothly in this scenario, since some optimization of the existing process was done to eliminate manual intervention as far as possible through AI , thus allowing the different players in the process to have more time to spend with the customers.

 

For this process, the roles of the different stakeholders might not change drastically, it might be that some training to the OLD school ageing staff is needed, since they will have to adapt to the new process in collaboration with some of the steps being done by AI instead of Human.

 

 

 

As AI enters the process there is a shift of overall responsibilities for humans in the role. As AI is integrated in processes the human intervention shift from daily activities or steps to monitoring process and reviewing decisions generated by AI model. Lets take Incident management process as an example.

When AI is embedded in the process for incident management the regular service desk tasks such as ticket allocation, applying work arounds for problems appeared in past will be automated and AI will do this stuff from the historical data and reducing human efforts from the process, such Auto Ticket Triaging, Auto ticket resolution and allocation will reduce human efforts in the process.

So The roles for Human in the process will be changed as follows:

 

Role	Before AI	After AI
Service Desk Agents	Handle all queries manually	Focus on complex, emotional, or edge-case issues
Operations Lead	Monitor SLAs and coach agents	Oversee AI performance, handle escalations, and refine AI training data
Process Analysts	Analyze ticket trends manually	Collaborate with data scientists to improve AI models and workflows
KM's	Maintain static FAQs	Curate dynamic, AI-consumable knowledge bases
QA Team	Review agent interactions	Audit AI responses and ensure compliance/accuracy

 

The Following will be the new roles and skillset required for the role:

1. HIL Specialist - This will trigger when resolution for tickets will have a less confidence and could result in affecting CSAT scores.

2. AI Solution Integrator - Creates a AI model and design prompts for input of the data. design workflows for running the process in a optimal way

3. Compliance Lead - Monitors and ensures all the AI compliance are properly followed.

4. Change Management Lead - Transition existing process to AI enabled processes by following change management principals with aligning with all stakeholders.

We can redesign the processes with human AI collaboration as follows: 

• Feedback Loops - To retrain models
• Process Segmentation - bifurcation of incidents to be handled by AI such as P3,P4 tasks and use HIL for P1,P2's.
• Transparent hand off's - Use Chatbots at various level such as customer facing and agent facing transparently. They must be aware that AI is been used.
• AI Performance KPI's - Use KPI's such as accuracy, deflection rate, drift, CSAT so that AI performance can be monitored over a period of time.

Let us consider an IT Product development process.  A cross functional Scrum team is doing that development. The roles involved are Scrum Master, 4 full stack programmers (Developers), 2 automation testers and 1 Product Owner and the technology used are Java based web technologies with Microservice architecture

 

Let us see what roles existed before AI and what roles got modified/added/removed post the advent of AI

 

Table1 : Comparison on Traditional Cross Functional Scrum team roles Vs AI based Cross-Functional Scrum team roles:

 

Traditional Roles in the Scrum Team (before AI)	Roles in the Scrum Team (post AI introduction)	Remarks (Add/Modify/Remove/Retain)
Scrum Master	Scrum Master (AI enabled)	Retain
Product Owner	Product Owner (AI enabled)	Retain
Full stack Programmers(developers)	Full stack Programmers (AI enabled developers)	Retain
Automation Tester	Automation Tester	Retain
-	AI Governance Lead	New
-	AI Ethics Officer	New
-	AI Data Lead	New
-	Lead - AI Solution Architect

 

Note: Here, all new roles incidentally can be applicable at an organizational level (definitely at a higher level than at a team level)

 

Table2: Responsibilities of Traditional Cross Functional Scrum team roles Vs AI based Cross-Functional Scrum team roles

 

Traditional Roles in the Scrum Team (before AI)	Key Responsibilities of the traditional roles in the Scrum Team (before AI)	Roles in the Scrum Team (post AI introduction)	Key Responsibilities of the AI based roles in the Scrum Team (after AI implementation)
Scrum Master	Coaching team members in Self-management & help the team in becoming cross-functional Helps Scrum Team focus on creating high-value deliverables (a.k.a Increments) Helps in removing impediments for the team Ensures Scrum events(meetings) happen on a cadence Helps the Product Owner in finding effective techniques for Product Management Facilitates stakeholder collaboration Helps in streamlining the Scrum adoption across the Organization (in which the SM works)	Scrum Master (AI enabled)	Coaches team members in Self-Management and help the team in becoming Cross-functional Helps Scrum team focus on Creating high-value deliverables  (increments) Helps the team in removing impediments Develops/Leverages AI tools to create meeting cadence for Scrum Events (meets) and allows AI tools to jot down key points as part of these events Guides the developers in leveraging AI tools such as Copilots for improving their productivity Encourages the Product Owner in finding AI tools that can help them in expediting User Story writing, Epic and Feature writing
Product Owner	Is accountable for effective Product Management	Product Owner (AI enabled)	Uses in-built Application Lifecycle Management(ALM) tools’ in built AI capabilities/AI tools for creating, modifying EPICs, features, User Stories
Full stack Programmers(developers)	Ensure that deliverables are met on time and quality is taken care as stated in Definition of Done (an agreed criteria within the agile/Scrum team)	Full stack Programmers (AI enabled developers)	Leverages Copilot for improving Code Quality, simpler and effective code writing, improving exception handling and saving effort on complex functionality writing.  The result is an efficient code producing quality outcome, with minimal or no rework. High quality with less development effort
Automation Tester	Ensures that all features are tested – regression and functional testing done, Ensures delivery quality is high by constant feedback on delivery quality to all stakeholders involved and tracks all issues to closure	Automation Tester	With AI based tools , more test coverage is done both for functional (System) testing and regression testing.  Complex Test scenarios are done far quickly Learning Curve for the Automation tool is much less now, when compared with the Past as AI takes in-charge
-		AI Governance Lead	Takes care of the overall AI policies and procedures cutting across all aspects of AI presence in the organization
-		AI Ethics Officer	Takes care of the Ethical aspects/concerns of AI involvement across the organization
-		AI Data Lead	Accountable for the data to be consumed by the AI tool/solution (component, agent, or any AI element such as CoPilot) for this process. Takes care of the Data Security, Data Integrity and Data Privacy aspects of the data that is used by AI for this process.
-		Lead - AI Solution Architect	Accountable for providing the solution/approach for making this process, AI based. In this specific context, this may be an overkill. For this process, this specific role is more of a guide to the whole team as which role can use what type of AI tool to improve their way of working or to increase productivity. But in a process where we tend to move towards AI (as final solution), then this is a pivotal role

 

 

 

As you can see from table1, table2, in this IT development process, the existing roles can get empowered with AI enablement.  In a typical IT development process, the above tables represent a more entry phase into AI transformation. The permutation and combination of using the existing roles and adding/modifying/removing roles vary from organization to organization and is highly contextual. One Organization can just rebrand the Scrum Master as AI Facilitator and another call it AI Technology Manager. So what is important is how do human beings collaborate with AI tools/agents

 

Therefore key to this AI and human collaboration are

• Setting the goals and objectives clear
• Ensuring alignment happens keeping this in mind and therefore laying out frameworks like OKR..
• Driving the corresponding KPIs/metrics (from the OKR)
• Collaboration with all the stakeholders with cadence
• Monitoring and tracking routinely the performance of the AI agents/tools (AI solutions)
• Inspecting and adapting the progress at every Product Review (in an agile world, this will be a Sprint Review at every few weeks)

The most recent example of an AI solution was dustbin based on AI segregating the waste as per bio degradable or non degradable. This application of AI is a commendable shift in the work being performed by a human resource vs the machine + AI solution. This is technology wherein the human efforts and exposure to waste is considerably reduced. Also it has improved the efficiency of work. In my scenario these type of jobs wherein human life may be at risk to exposure to gases, chemicals or involving risk to human life etc should be taken over by Artificial intelligence, rather than being dependent on a human resource.

It is good to see that the respondents are visualizing the role changes in their own areas of work. And hence it is recommended that one should read all the answers to get a view of how AI is changing the role requirements across different industries and domains.

 

There are 2 answers which stand out  and hence both have been declared as winners - Sargun Diwan and Sattar Mohammad Imran. Well done!!