Leaderboard

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.

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.

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

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.

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

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.

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.

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.

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.

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.”