Q 800. Managers and leaders often face tough decisions where data is incomplete, options are conflicting, and time is limited. AI can analyze large amounts of information and surface recommendations — but leaders still need to trust and interpret those insights. Imagine an AI agent designed to support decision-making for managers in your domain.

What kind of decisions should it assist with, and what checks would you build in to ensure its advice is both reliable and aligned with organizational goals?

The best answer will be selected on the basis of: 

• Relevance of the leadership decision scenario  
• Practicality of the AI support mechanism  
• Insight into balancing AI advice with human judgment

 

Note for website visitors -

• This platform hosts two weekly questions, one on Monday and the other on Thursday. 
• All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. 
• To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/.
• The question will be open until Monday or Thursday at 5 PM Indian Standard Time, depending on the launch day. 
• Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be considered for winner selection. 
• If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. 
• All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honourable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term.

• 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.

Imagine in a healthcare BPO environment, we suddenly see a spike in pending claims and leadership needs to decide fast — do we pull people from another process which will put strain on exiting workflows, or do we push for overtime, which will eventually hit project bottom-line? Each decision will have pros and cons. Pulling people from other processes means putting strain on existing teams and approving Normally, that mean lots of manual efforts involving hours of pulling reports, debating in meetings, and still not having the full picture.

This is where I’d see an AI agent as a co-pilot – someone who will do the hard work and compile the necessary information from various places.  It could instantly scan the claims backlog, existing workforce availability, and compliance rules, then frame the decision for me. For example, it might say: ‘Option A — move 20 people from Membership tower to claims tower, closure rate improves by 4% but average handle time rises 2%. Option B — authorize overtime, closure improves 3% with no impact on handle time.’ Now instead of me digging for data, I’m looking at clear trade-offs.

But I wouldn’t want the AI making the call. I’d build in checks — it has to show me its data sources, flag any blind spots, and tie its recommendations back to our bigger goals like compliance and customer satisfaction. And I’d always want it to offer multiple options, not just one, because the judgment is still mine to make.

This way, I ensure the final decision is not just data-driven, but also in line with our company’s culture, values, long-term strategy, and the real-world factors that AI can’t fully capture.”

So, in my mind, AI doesn’t replace leadership — it accelerates the analysis, but the responsibility to weigh culture, strategy, and people still rests with me.”

Being a digital transformation consultant, I see AI as a strategic enabler and a partner in smarter decision-making in all businesses. For e.g. In Banking, the decisions it should assist with span across Retail lending, home loans, Fraud Detection, Customer experience, and Accounting Operations - areas where we have large amount of data is but have scarcity of time.

For example, AI can be very useful in preventing fraud. It can flag transactions by analyzing geolocation patterns, transaction velocity, and customer’s historical behavior. It can then trigger a real-time alert, block the transaction, and notify the customer—all before any financial loss occurs. With such a case AI can not only protects money losses but reinforces customer trust in banking system and make them feel safe and secure.

 

Checks that need to be built to ensure AI’s advice are both reliable and aligned with organizational goals for a bank.

• Strategic Goal Alignment: Before considering any of AI recommendations or automation help, it would be mapped against the bank’s current strategic priorities, like SLA, TAT etc. This keeps tactical decisions aligned with long-term vision.
• Regulatory Intelligence Layer: AI must be trained on evolving compliance frameworks—Reserve banks pre-defined norms, KYC/AML norms, GDPR, and internal governance policies. Hence, every insight should pass through a regulatory lens before reaching decision-makers, to ensure all regularity and compliance requirements are in place.
• Audit Trail & Logic Based Explainability: In banking, transparency is non-negotiable. Hence, we should ensure that AI generates all recommendation with a clear rationale, traceable logic and data backed. So, by building explainability, we will be able to build confidence among stakeholders and ensures accountability.
• Bias & Fairness Audits: Especially in lending and customer segmentation, AI would be regularly tested for bias. Decisions would be inclusive and equitable, with built-in mechanisms to detect and correct disparities.
• Human-in-the-Loop Protocols: I believe, AI should never operate in isolation. Final decisions—especially those involving high risk transactions (Money or Reputation) must involve human judgment.

Ultimately, AI should act like a high-performing analyst embedded in every team—fast, objective, and deeply contextual. But it is the leadership lens that would ensure those insights translate into meaningful, mission-aligned action. So, according to me, AI is going to help leadership in most of the work, but it would still require human judgement and decisions.

In today’s fast paced world where data is in abundance, AI can help scan through the data and provide insights to leaders for effective decision making. However, this approach can be flawed and the recommendations provided by AI agents can be questionable if the underlying data is incomplete or biased or if the recommendations are conflicting. In such cases the AI recommendations must be compared against the prevalent domain context and based on experience decisions need to be taken. Below are a few examples from the software services industry where AI can support decision-making:–

1.      Employee Upskilling - AI can analyze employee skill data and project demands to identify gaps and provide recommendations to design learning and development programs.

2.       Contracting – AI agents can help to draft contracts and later reviewed by experts, thus reducing the turnaround time

3.      Program Implementation – For greenfield projects, AI can help to design the data model,  create source to target mappings (STTM), generate code according to the STTM document , generate test data etc. For migration projects as well, AI can help to convert legacy code to cloud native codes, help to assess the legacy landscape, scan through code lineage and help determine a migration plan etc.

4.      Data Management – AI can assess source data quality, help in master data management and data governance.

5.      Performance Management – AI can help to provide insights about an employee performance by analyzing inputs from status updates in Jira, certificates completed , learning programs attended by the employee , tracking the allocation % in a year and also seeking insights from client feedback.

Though AI can provide good insights, but final decisions should be reviewed and approved by Delivery/Account leadership to ensure alignment with organization goals.

The roots of an organization lie in its culture. To stay competitive, be the innovator and the go-to market brand, organization transformation is needed at a cultural level. For an organization to have this transformation journey, AI can be one of the trusted partner only if used judiciously.

Let us debate this with an example. To promote organization wide learning and development, upskilling and efficiency improvement, from a strategy point of view an organization invested on an AI solution. Every month AI report was shared which gave visibility on number of training hours spent at Department level. Say we have Department A with 40 hrs and Department B with 240 hrs. It was but obvious that Department B got recognized. This was misleading as Department A had 5 team members with an average of 8 hrs per spent on training per member whereas Department B had 120 team members with an average training hrs of 2 per member. The feedback was given to the involved stakeholders and right behaviors were promoted. AI workflows were rebuilt to trigger training modules as per Department needs and dashboard was re-aligned to report correct metrics. This was possible because leadership had the visibility to the dashboards and timely actions were taken to make the AI platform more interactive and enabled the organization to promote a learning culture. An average 2hrs of training spent per member moved to an average 8 hrs of training per member within 6months.This also contributed to employee engagement and continual improvement initiatives.

For AI to be the trusted advisor for leaders, recommending following steps to be taken by leadership -

Creation of Core team - Before onboarding the AI journey, it is crucial to have the buy in and alignment of all key stakeholders. Create core cross functional team having leaders representing domain, business and technical expertise.

Strategic Initiative - Leaders are the influencers for setting the strategy. Leaders need to communicate the need and the objective for adoption and adaptation. Corporate to sponsor the strategic initiative. Go big bang with communication. Set the tone at the top. AI goals, KPIs , resources, etc. AI tools to be part of the short-term Annual Business Plan and long term 5-year strategy roadmap.

AI awareness – Educate leaders on what AI is and make them aware of its limitation. Leaders to be mindful and cognitive to overcome biases related to cost, technology and speed. Message from leaders to team to maintain ethics and transparency.

Building capability – One of the key reasons why AI can be the trusted advisor is for the speed of data availability, accuracy, reliability and faster decision making. Core team to explore and build AI solutions that would meet the business objectives. Core team to involve domain and technical expertise for bias in and bias out checks. Few key criteria’s for evaluation could be Cost, Quality, Risk of failures, Flexibility – Scalability, Processing Time, Implementation speed, data collection, processing, storage capability and ROI.

Proof Of Concept - Create prototypes, use cases, assess opportunity, prioritize and validate, design, build, test and deliver. Course correct if needed. Calibrate, train and update knowledge repository. Roll out the solution.

Communicate – Address myth that AI is people reduction method and promote it as value creation to customers and business. Send organization wide communication when milestones) are achieved. Share failure and success stories. Capture lesson learnt and submit solution in Knowledge repository. 

Governance and oversight – Management to have periodic governance. Reinforce ethical adoption of AI and compliance to data security and privacy and role based access to daily dashboards for better monitoring and oversight.

Reward & Recognize – To promote and encourage continual improvement celebrate achievements and reward all those involved in making and leading the transformation.

AI thus can be the trusted advisor for leadership if adopted in spirit and maintaining integrity and business ethics at the root level.

AI can definitely become a trusted advisor for Leaders. AI can do data crunching like no human and even extract trends more efficiently from that data (much better and faster than a human); but their are other aspects like ethical judgement, human touch and leadership which AI cannot replace. In short, AI can become a trusted partner and not replace a human.

 

 

AI as a trusted advisor

• Decision making: Any amount of data can be analyzed by AI in few minutes. AI can find out the monthly/yearly trends, risks, market outcomes in a much faster and efficient manner than any human.
• Mitigating bias: AI can identify bias via the organizational data and help companies become more inclusive.
• Automating mundane tasks: Al can automate repetitive tasks, freeing up the time for leaders to focus on more strategic things.
• Sentiment Analysis: sentiment analysis can be easily done by AI and help leaders gauge the customer sentiments, understand communication patterns, and flag potential issues. Same goes for employees to understand the morale of the team.

 

The human elements which are not replaceable are critical thinking, reading the room and ethical judgement.

On 8/25/2025 at 6:40 PM, Vishwadeep Khatri said:

Q 800. Managers and leaders often face tough decisions where data is incomplete, options are conflicting, and time is limited. AI can analyze large amounts of information and surface recommendations — but leaders still need to trust and interpret those insights. Imagine an AI agent designed to support decision-making for managers in your domain.

What kind of decisions should it assist with, and what checks would you build in to ensure its advice is both reliable and aligned with organizational goals?

The best answer will be selected on the basis of: 

• Relevance of the leadership decision scenario  
• Practicality of the AI support mechanism  
• Insight into balancing AI advice with human judgment

 

Note for website visitors -

• This platform hosts two weekly questions, one on Monday and the other on Thursday. 
• All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. 
• To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/.
• The question will be open until Monday or Thursday at 5 PM Indian Standard Time, depending on the launch day. 
• Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be considered for winner selection. 
• If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. 
• All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honourable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term.

• 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.

 

Conventional decision-making process is like a structured problem-solving process, which involves understanding problem statements, data collection, analyzing scenarios & arrive at the conclusion. Similarly, AI models analyze given set of data, identify patterns, train themselves and calculate the likelihood of different outcomes for both old & new cases. Therefore, case-based AI models can be developed for decision making for administrative as well as for functional scenario.

The models may have different limitations like biases, hallucinations etc. which can be handled using techniques like Prompt engineering – develop precise prompts to guide the model, fine-tuning or further training, grounding of models using specific data & RAG – feedback mechanism.

Success of any model depends upon its accuracy and accuracy of any AI model depends upon the following: 1) Data Accuracy 2) Enough data to make an accurate prediction 3) inclusive & representative data to avoid biased prediction 4) In scope data perfectly aligned with intended model 5) Consistent & labeled data.

However for high risk & sensitive decision making models must be accompanied with HITL to ensure appropriate content creation with post generation review

 

 

I recently worked on a project where we built what we called a CO-CEO AI agent for a marketing company. The idea was simple: instead of treating the AI as a background tool, we brought it into the decision-making process almost like another executive. Its main job was to help the CEO design marketing strategies for different clients.

Now, here’s the twist. We didn’t just let the AI spit out strategies in isolation. It was invited to client meetings, not literally of course, but through structured prompts where the full context of the client’s business, challenges, and goals was fed in. That way, its recommendations weren’t generic “playbook strategies,” but tailored to the actual discussion the leadership team was having. That made it much more of a trusted advisor than a black-box machine.

From this project, I learned that there are two layers to making AI advice truly useful and trustworthy for leaders:

1. Where It Should Assist
Pattern spotting across campaigns: Leaders don’t always have the time to compare 50 different client reports. The AI could highlight trends (e.g., “clients in retail are seeing 20% higher engagement when campaigns run mid-week”).

Scenario testing: Instead of one “best” strategy, the AI could lay out three options: low-risk, high-growth, or balanced. This gave the CEO choices rather than a single directive.

Speed on background research: Before walking into a client strategy session, the AI could summarize competitor campaigns, past results, and market conditions in minutes.

These are areas where AI’s scale and speed give leaders an advantage without replacing their judgment.

2. Checks to Keep It Reliable
Context gatekeeping: The AI was only as good as the context it had. We made it a rule that client objectives and constraints must be captured first (almost like a briefing note) before the AI gave advice. No context, no strategy.

Audit trail of reasoning: Every recommendation had to come with a short rationale, “this works because past campaigns in similar industries showed X, Y, Z.” This gave the CEO confidence in the “why,” not just the “what.”

Version control for prompts: As we refined how we asked the AI questions, we tracked changes. For example, when we shifted from “generate campaign ideas” to “act as a CMO and propose three strategies with risks and trade-offs,” we documented it. That way, if a change caused worse outputs, we could roll back quickly.

 

Human override always on: The AI was never treated as final authority. The CEO still made the call, but with stronger input in less time.

Honestly, what made this whole setup work wasn’t the AI being “super smart.” It was the way we used it. We never treated it like it was going to run the company or make the final call. Instead, it was more like a second set of eyes, someone in the room who could throw out a few options, show the risks, and spot patterns the rest of us didn’t have time to see.

The clever part wasn’t the output, it was the process: making sure it only answered once we’d given it the right context, tracking how we asked questions so we didn’t lose improvements, and always keeping a paper trail of its reasoning so it didn’t feel like magic. At the end of the day, the CEO still made the decisions. The AI just made those decisions faster and more informed.

That’s really the trick to building trust. You let the AI contribute, but you don’t hand over the steering wheel. It’s not there to replace judgment, it’s there to make good judgment easier.

AI can become a trusted advisor for leaders but cannot completely replace human judgement. So, hybrid approach of AI insights merged with human judgement will be the best result for a leader to take a decision that will be aligned with organizational goals

 

Positive outcomes of AI-driven results:

• AI can analyze huge data and predict near to accurate decisions with minimal time consumed that a leader might miss
• AI can run multiple complex cases simultaneously and get real-time answers with minimal efforts that leader can refer to
• AI ensure fairness & reliability in decision if AI is properly trained
• AI can work 24*7 and provide multiple best scenarios that a leader can select for

 

Negative outcome of AI-driven results:

• It’s difficult for a leader to trust AI decisions since it is difficult to understand complex AI algorithms and this lack of transparency is “Black Box” problem
• AI can provide skewed decisions if historical data is biased and AI is not properly trained
• Leaders might lose critical thinking skills if they are over-dependent on AI decisions
• AI uses large data to analyze and this might raise privacy & security concerns

AI-Powered Incident Management System

 

At our e-commerce platform company (PAAS model), we need to support environments of all customers who rely on our system for their online ecommerce sales and we need to have continuous uptime and meet strict SLAs. We use NewRelic as our observability platform which collects massive volumes of logs, traces and metrics. After filtering we are analyzing over 60TB of telemetry data per month. We’re now building a system to query this data for AI model integration, enabling anomaly detection, incident correlation, and predictive analytics.

 

Decision Making Scenario- Analyzing and classifying incidents in real time

When an Alert is generated the Operations Manager need to make quick decisions during disruptions like outages, performance degradation, or critical bugs. These incidents needs to be evaluated quickly due to SLA requirements since they directly impact buyer satisfaction, conversion rates, and revenue for our clients. The challenge is to prioritize correctly, weighing severity, scope, resource constraints when choosing action steps.

 

AI Agent Support

We have been working on a design of an Incident Response AI Assistant with the final goal that it will

- Aggregates and analyzes real-time data, such as error logs, user requests, system metrics, and incident tickets.
- Scores incidents by combining:
    - Business impact (example: conversion drop, cart abandonment)
    - Customers or geographies affected
    - Urgency indicators like large number of error log entries, memory or cpu spike
- Generates and recommends priority and type based on pre-learned categorization
- Suggests response paths to help mitigate the problem
    - Quick mitigation (rollback the latest patch)
    - Escalation (pass to development team)
    - Monitoring (observe performance metrics)
- Offers confidence scores on its solution

 

For this we are creating solution with two key components to support the decision-making and the communication

 

1) Incident Analyzer Bot which will

• automatically detect system issues using AI/ML
• Learn from historical incidents to categorize alerts by severity and type and reduce false positives
• Correlate related events — for example, grouping three different alerts under a single outage — to help managers see the full picture quickly
• Find and provide reference of related incidents from our historical data to help provide the manager with information about the previous RCA and solution

 

2) Ops Chatbot is focused on communication (not customer-facing)

• If a critical issue is detected, it can notify customers automatically and proactively before they notice it themselves.
• It supports manual overrides, customizable communication methods (like email, message, chat) with pre-defined message templates.
• Makes message suggestions to the manager, who can review and approve them directly in tools like Microsoft Teams.
• Manages follow-ups automatically if the issue remains unresolved — for example, sending timed updates like “we are still analyzing your system” every 1, 6, 12 hrs depending on the case.

This AI assistant will help to surface and prioritize incidents quickly but the final decision remains with the manager who reviews the categorization, solution recommendation and approves the communication suggested by AI. The manager will be have full discretion to override, approve, or modify the AI’s actions.

We have planned to create feedback loop to help the system learn and improve over time and implement accuracy monitoring by comparing AI predictions to actual outcomes, review the AI's confidence scoring especially when AI uncertainty is high. This regular validation against historical incidents  should help us ensure that the human/AI-assistant together work towards meeting customer's SLAs.

An AI agent designed to support decision-making for managers could assist with a variety of complex issues. To ensure its advice is reliable and aligned with organizational goals, specific checks would need to be built into its design and implementation. The AI agent is replacing the role of the Secretary and Business planning Manager for  C level. All the planning and administrative tasks done by these roles are being replaced with the AL Agent, the latter does the planning and provide insightful recommendations to the C level for decision making.

 

Decisions to Assist With

An AI agent could be a powerful tool for assisting with data-intensive and high-stakes decisions where human cognitive biases or time constraints could lead to suboptimal outcomes. Examples include:

 

Supply Chain Optimization: The agent could analyze real-time data on inventory levels, and demand forecasts to recommend the most cost-effective and efficient distribution strategies. This goes beyond simple automation by adapting to dynamic disruptions .In our organisation , which is represented in five countries across Africa, analysis of real time data is crucial to quickly overcome unexpected scenarios.

 

Talent acquisition is a tedious task when making hiring decisions, an AI could evaluate candidate profiles against the department requirement from a vast dataset of employee to identify individuals with a high probability of success in a new role. This can help mitigate unconscious bias in hiring.

 

The agent could process and synthesize a market research and competitor analysis, to recommend the optimal geographic regions or product categories for the company's expansion. In terms of logistics acquisition like Laptops , PC ,AC , the AI agent would look for the best cost effective option to choose from which at time it is oversight by the  human analyst working on it.

For the AI's advice to be trusted and effective, these checks are mandatory and crucial:

 

1. Explainability and Transparency

The AI should not be a "black box." As mentioned in the previous answer provided on whether it is a black box or glass box .It must be able to explain the reasoning behind its recommendations in a clear and understandable way. For example, if it recommends a new supplier, it should be able to show the specific data points—such as historical on-time delivery rates, cost comparisons, and risk scores which helps to come to a  conclusion. This builds user trust and allows managers to interpret the advice within a broader context in committees and clients meeting.

 

2. Human-in-the-Loop Validation

The AI agent should function as a co-pilot, not an autonomous decision-maker. Nowadays the co pilot functionality of Micosoft 365 has become a powerful tool used in the organisation when doing meetings on Teams, the minutes of meeting and summary of the long conversations done are easily summarised and make up to the point. The manager would then use their domain expertise, emotional intelligence, and contextual understanding . This is a quality the AI lacks to make the final judgment call

 

3. Goal-Weighted Metrics

The AI's objective function should be weighted to reflect organizational priorities. Feeding  the AI tool with the organisation strategic plan ensures that when decisions are taken , it is abiding to the company core values.

 

4 Ethical Constraints

The system must have built-in ethical checks to prevent recommendations that could lead to unfair or biased outcomes. Given that we operates in different markets with a mixture of people from different ethnicity ,  the AI is specifically trained to avoid making decisions based on protected characteristics and to flag any potential bias in the input data.

 

These checks turn the AI from a simple tool into a collaborative partner, empowering managers to make better-informed, more strategic decisions while maintaining accountability and ethical oversight.

The decision making by leaders has always needed data and insights as back up on which the causes and action points hinges upon.
The leaders have always wanted to make their decision making faster but not without data and facts.
But often they have hit the roadblock because of the inefficiency crept in the systems by virtue of process and technology deficiencies in the system.
Usual approach by the leaders have been to focus on ways to improve the process and system through process improvement steps like Lean Six sigma approach and Business process reengineering. These improvement steps have most of the time benefited the process and technology enablement for the leaders to arrive at improvement projects and subsequent steps to improve. The only concern which leaders have around the time it takes to deliver the projects which ranges from months to sometimes years depending on the complexity of the problem statement.

Leaders have always been on lookout for tools or methods through which they can work on the decision making tools which will help in the quicker decision making and increase in the Profit in P&L statements of the Company.

E.g. in one of the manufacturing assembly line I worked with the COO knew that the quality parameters are in pretty much bad conditions and needed immediate drastic improvement steps but whenever he was chairing HoDs meeting, he used to come across different localized set of data which was being presented and thus leading to contradictory claims and counter claims resulting in no action points of significant impact.
The problem statement was overcome by asking all the team members to work on the data cleansing and having a master data sheet by working together so that the data sanctity is not in question. COO was able to take some drastic steps in the quality improvement like implementation of lean and TOC concepts for further improvements which was able to help being down considerably.

Now how I see AI as opportunity and how we could have done things differently back then or facing a similar situation, how differently could have approached the problem statement.

Now with the agentic AI there could have been three different types of agentic AI which could have been developed and deployed viz. the agentic AI for the data cleansing and standardization, second agentic AI with analyzing the data and bringing in the insights for the subsequent analysis and decision making and third one which could have collaborated with other data tools and would have generated a lot of insights in collaboration with other ERP tools.

The insights would have been generated much faster and would have lesser time as compared to conventional approach and would also have had the flexibility to provide details as per the change in requirement by COO or management/HoDs.

Now is the question is how to ensure that the insights would have been correct and not wayward.

So, by collaborating with the quality team would have set the mean and median in the numbers of say non conformance per unit and other parameters for us to arrive for any deviation in the insights which is being generated. Also, regular analysis of the data and insights should have been carried out by the AI data engineer entrusted with this work.

Agentic AI would have been such an enabler for us to move ahead in the lean implementation journey and could have delivered more Profit in the P&L statements of the company at faster pace.

582 Words

Decision making by leaders is traditionally a competency that comes with years of experience and analytical thinking. Now, in today’s world can the leaders make use of AI in decision making. There are different type of decisions, executive decisions, decisions to add or not add HC, should we invest more in labour or technology or data security, usually all this is based on need of the hour, business goals, budget, urgency and risks.

This is also beneficial in day to day manager decision making in operations. For example in a banking customer service environment, especially one dealing with queries like UPI failed transactions, balance inquiries, or standing instructions not honored, an AI agent can be a powerful decision-support tool for managers. Here's how it can assist and what checks should be built in to ensure reliability and alignment with organizational goals.

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What decisions the AI can assist

1. Prioritization of Customer Queries

• E.g: A UPI transaction failed for a high-value customer vs. a routine balance inquiry.
• AI Role: Use customer segmentation, transaction history, and sentiment analysis to prioritize cases.
• Manager Decision: Approve escalation or fast-track resolution.

2. Root Cause Analysis

• E.g.: A customer complaining that a standing instruction was not honored, maybe a customer lost an investment opportunity in an IPO — is it due to insufficient funds, system error, or third-party failure?
• AI Role: Aggregate logs, transaction data, and system alerts to suggest probable causes.
• Manager Decision: Decide whether to initiate a technical fix, customer compensation, or policy review.

3. Reversal or credit recommendations

• E.g.: UPI failure caused a missed payment for a premium customer.
• AI Role: Analyze customer value, historical issues, and policy thresholds to recommend goodwill credits or fee waivers.
• Manager Decision: Approve or modify compensation.

4. Process Optimization

• Example: Frequent failures in standing instructions from a specific bank.
• AI Role: Detect patterns across customer complaints and suggest process or partner bank reviews.
• Manager Decision: Initiate cross-functional investigation or vendor engagement.

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What checks can be inculcated to ensure reliability and alignment to business objectives

1. Transparency and Explanation

• Soln.: AI must provide a clear rationale for its recommendations (e.g., “Customer has 3 prior complaints and is in the top 5% revenue bracket”).
• Benefit: Builds trust and helps managers make informed decisions.

2. Human involvement

• Soln: AI suggestions should be reviewed and approved by managers, especially for financial or reputational decisions. Apply HITL
• Benefit: Ensures accountability and prevents blind reliance on automation.

3. Policy Alignment Engine

• Solution: Embed business goals, organizational policies and ethics into the AI’s decision logic (e.g., compensation caps, escalation rules).
• Benefit: Keeps AI recommendations compliant with internal standards.

4. VOC

• Solution: check success and failures of AI assisted decisions, track outcomes and feed them back into the model to improve accuracy.
• Benefit: Continuous learning and refinement of decision quality.

5. Biasness audits

• Solution: Regularly audit AI outputs for bias (e.g., favoring certain customer segments unfairly).
• Benefit: Promotes ethical decision-making and regulatory compliance.

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In a banking customer service process workflow: UPI Failed Transaction

1. Customer Complaint Received
2. AI Agent Analysis:
   • Transaction logs
   • Customer profile
   • Historical complaint data
3. Recommendation:
   • Root cause: Payment gateway timeout
   • Suggested action: Escalate to tech team, offer ₹100 goodwill credit
4. Manager Review:
   • Approves escalation
   • Modifies credit to ₹50 based on policy
5. Outcome Logged for Learning

 

 

1. Customer Query Intake – UPI failure, balance inquiry, etc.
2. AI Analysis – Transaction logs, customer profile, sentiment.
3. Decision Support – customer credit rating, prior history, Prioritization, root cause, compensation suggestion.
4. Manager Review – Human validation based on pre-decided bank criteria
5. Action & Feedback – Resolution, learning feedback loop for AI.

 

 Policy Alignment Matrix for Banking Customer Service AI

Query Type	AI Recommendation Scope	Policy Constraints	Escalation Criteria
UPI Failed Transaction	Root cause analysis, retry suggestion, goodwill credit	Credit limit ₹50 per incident; max 3 credits/month	High-value customer, repeated failures
Balance Inquiry	Provide latest balance, detect anomalies	Must verify customer identity; no financial advice	Suspicious activity or mismatch in balances
Standing Instruction Not Honored	Identify failure reason, suggest manual payment, notify customer	No auto-compensation; notify customer within 24 hrs	Recurring failures, regulatory breach
Card Block/Unblock Request	Confirm identity, initiate block/unblock	Must follow 2FA; no override without customer confirmation	Fraud suspicion, system error

In 2W Automotive domain , AI can assist the managers on key decision areas:

1.Production and Inventory planning 

  Challenge: Fluctuating demand, JIT systems, raw material shortages.

   AI Support: Forecast demand shifts at model or region level, suggest inventory distribution, flag supply chain risks early.

2.Model portfolio strategy 

   Challenge: Balancing ICE vs. EV investments, deciding when to sunset legacy models.

   AI Support: Simulate long-term performance of model lineups, compare regulatory impacts, analyze regional readiness for EV adoption.

3. Supplier and Vendor selection

    Challenge: Cost vs. reliability, geopolitical risks, ESG concerns.

    AI Support: Evaluate vendor performance over time, assess financial health, and flag risk indicators (e.g. political exposure, labor issues).

4.Aftermarket and service operations

   Challenge: Maximizing lifetime value of vehicles and customer retention.

   AI Support: Forecast part demand, optimize service schedules, and personalize post-sale engagement strategies.

 

To ensure it is both reliable and aligned with organizational goals

1. Built in Human 0verride-even with strong algorithms, AI should act as a co-pilot, not an autopilot. Managers must retain authority and be encouraged to challenge the AI—especially when it presents a "high-confidence" recommendation that contradicts lived experience. 

2.Feedback and learning loop-

Managers’ decisions and their outcomes should be fed back into the system:

• Did the manager accept or reject the advice?

• What actually happened?

• Was the AI accurate or off the mark?

This loop helps the AI grow more accurate over time—and helps managers become better users of it.

3. Transparent logic and traceability -

the system should clearly outline:

• What inputs were used

• How the conclusion was reached

• Where uncertainty or data gaps exist

This helps managers understand the "why" behind every recommendation—not just the "what."

In the automotive domain,Quality, speed and precision are everything—but so is judgment. An AI assistant should not replace human reasoning; it should amplify it. By combining fast analysis with organizational context and strategic alignment, it becomes not just a machine that calculates—but a partner that collaborates.

 

 

For leaders to trust AI, we need to consider the following concerns :

* Can we build an AI agent with not enough data about the case to make tough and timely critical decisions by AI?

We expect with limited data to make biased and inappropriate decisions; however, we might be able to build an AI model that mitigates the data shortage issue, but this will be risky. Therefore, it is rather advised to build a selective automated system by keeping the critical decision to the leader, considering the output of the AI agent

* What are the possibilities when data is incomplete, or options are conflicting to overlapping, to get a trusted critical decision by an AI agent?

There is an opportunity to build an AI agent that works on different techniques to eliminate data shortage and conflicting data, probabilistic reasoning, uncertainty quantification, or contextual analysis. Bases on the nature of the business and criticality of decision; leaders  can work on segmenting the decision making approach into three different categories, level one we trust to AI agent to take decision where options are limited and the risk of which is controlled and supervised, level 2 where leaders can delegate the final decision making based on AI agents to reporting managers, and level 3 which is concerned with the real costly decision where tolerance toward errors is almost zero; in this category leader should avoid trust AI agent decisions but still they can consult it

* What are the working environment constraints that might limit the correct decision by the AI agent?

The level of working environments' complexity plays a critical role in AI agents’ rational decisions, and it is important to consider that there are deterministic and dynamic environments where transparency, social context, and ethical context need to be addressed when we deploy AI agents

* How far can the decision taken by AI be in favour of the business goals?

Any decision taken by an AI agent should be for sure in favour of the business goals, as this is part of the context and data set the AI agent is using to make decisions; however, “How far" is based on the robustness of the system setup in the business, the Business AI strategy, and proper ethical governance

* How long does AI need to be part of the working environment to get the correct sentiment for any required decision?

Sentiment is probably the next of AI machine learning, as it requires a bit of complex AI setup, the competencies of data AI is learning from, therefore, in our case, where the data dataset is incomplete and there are conflicting parameters, the sentiment will be biased even if we tried to enhance the dataset's integrity level

* Can we build a tailored AI agent to be able to detect the leader's tone in decisions?

Technically, it is possible, and this turns out to be an AI digital assistant. This would require sentiment analysis, NLP dedicated to the leader's personal data set. Now, how far will this agent be trusted for a critical decision? It all depends on a healthy relationship between the leader and the agent itself, in which, over time, the leader will be able to decide when to delegate the decision to the agent and when to keep it for himself

 

Conclusion:

An AI agent can be considered as an advisor in the case of a critical decision for a leader; however, we need to consider the problems and effects of having incomplete data or conflicting data, and based on that, we shall fine-tune our own ability to trust the AI Agent to make critical decisions

It is good to see the varied areas where AI can become a trusted partner for leaders. 

 

The best answer is from Ayomide. Well done!