Sumit Kumar Saha

Voice of Employee (VoE) is a strategic approach that help organizations to better understand their employees. It represents the collective voice of individuals within the company, providing a window into how employees see their work environment, leadership, communication, workload, and overall engagement. Objectives of VoE: VoE acts as a bridge between employees and management which enables alignment of employee needs and organizational goals. The key objectives of VoE programme are a) Foster a transparent and inclusive environment: When employees feel heard and valued, they are more likely to remain committed, motivated, and aligned with the organization’s mission. b) Identify improvement areas in leadership, culture, and processes: By actively listening to employees, organizations can identify underlying issues such as dissatisfaction with management, lack of growth opportunities, burnout, or biasness in the workplace. c) Boost employee morale and retention: Addressing the identified issues will help to improve employee morale and productivity but also reduces turnover and attracts top talent. Key Elements of VoE: a) Surveys & Polls: Regular employee engagement or pulse surveys to collect insights. b) Feedback Mechanisms: Anonymous suggestion boxes, open forums, or digital platforms. c) One-on-One Meetings: Direct conversations between employees and managers. d) Exit Interviews: Insights from employees leaving the organization. e) Employee Resource Groups (ERGs): Forums for specific communities within the workplace. Tools Used for VoE: Organizations often use various tools and methods for VoE. Employee engagement surveys, pulse checks, focus groups, town hall meetings, and digital feedback platforms are different ways to gather employee’ insights, feelings, and feedback. These channels give employees the freedom to express their thoughts, which is essential for promoting honesty and psychological safety. In addition to formal tools, informal feedback mechanisms like open-door policies, team retrospectives, 360° feedback also play a vital role in collecting authentic employee opinions. Examples: Qualtrics, Glint, SurveyMonkey, Officevibe, are few employees survey tools. Role of Employees in VoE Employees being the primary source of feedback, ideas, and observations within an organization, they play critical role in VoE. Their active participation in surveys, suggestion systems, one-on-one meetings, and feedback forums allows management to understand what’s truly happening at the ground level. Employees must share their honest opinions on different aspects of the workplace. It helps the organization to identify challenges and highlight areas that need improvement. Furthermore, employees play a role in driving change by not just giving feedback, but also suggesting solutions. For example, if a team member feels a process is inefficient, they might propose a better method during a team meeting. In this way, employees act not only as observers but also as contributors for improvement. Their role becomes even more valuable when they participate in co-creating a better workplace culture. Real-world implementations of VoE and their success stories across various well-known organizations. 1. Google – “Googlegeist” Survey Google conducts an annual internal employee survey called Googlegeist, where employees rate everything from leadership to work-life balance. The results are used by managers to create targeted action plans. For instance, if a team reports low psychological safety, initiatives like open feedback sessions or training workshops are introduced. 2. Microsoft – Continuous Listening System Microsoft shifted from annual surveys to continuous listening using short, frequent pulse surveys and employee sentiment tracking tools. It helped Microsoft respond in real-time, especially during the pandemic. Feedback led to changes in remote work flexibility and the introduction of well-being days. 3. Accenture – Employee Experience Platform (N-th Degree) Accenture uses a platform to capture VoE data from over 500,000 employees globally, collecting feedback on inclusion, performance, and growth. Insights from the platform led to major diversity and inclusion improvements and helped design career paths better suited to employee strengths and interests. 4. Airbnb – “Ground Control” Culture Team Airbnb created a team called Ground Control to manage employee experience and culture. They use VoE from exit interviews, regular check-ins, and surveys to adapt policies. Based on employee feedback, Airbnb introduced more flexible roles, increased focus on mental health, and reshaped team structures to boost collaboration. 5. Adobe – Check-In System Adobe eliminated formal annual performance reviews and introduced “Check-Ins”, a frequent feedback and dialogue system driven by VoE principles. Employee feedback showed that annual reviews caused stress and lacked flexibility. The new system led to improved manager-employee relationships and higher engagement. 6. Unilever – AI-Powered Feedback Unilever uses AI tools to analyze employee feedback from surveys, collaboration tools (like MS Teams), and even anonymized sentiment from internal forums. This allowed them to pick up on silent stress signals during COVID-19 lockdowns and increase support resources in vulnerable regions. 7. Cisco – Feedback to Drive Inclusion Cisco uses real-time VoE tools and anonymous pulse surveys to gauge employee sentiment on diversity, equity, and inclusion (DEI). The data helped identify underrepresented voices and informed new DEI initiatives, mentorship programs, and changes in leadership behaviour.

Key Risk Indicators (KRIs) are measurable metrics to provide early warnings of potential risks that may impact business objectives. KRIs are the important mechanisms for a proactive risk management strategy. They are specifically designed to signal potential threats. It helps decision-makers to identify, monitor, and respond to developing risks before they escalate. KRIs may vary depending on the business sectors and the identified risks types. For example, operational risks might be tracked using indicators like system failure rates or employee turnover. Financial risks could be monitored through metrics such as debt-to-equity ratios or deviations in cash flow forecasts. Cybersecurity risks are regularly measured through the frequency of phishing attacks or the number of unpatched systems. Compliance risks are measured by the number of regulatory violations or audit findings. A threshold shall be set for these indicators for categorization into Low (acceptable), Medium (caution), and High (breach - Immediate action required). Organizations can create dashboards to have quick visibility into potential risk areas. Designing of an Effective KRIs: KRIs must align with the organization's risk appetite and be updated regularly to reflect current risk exposures. An effective KRIs must have certain defined characteristics such as relevant, predictive, measurable, actionable and time-bound. KRIs must be relevant and aligned with key business objectives and risks. They must be quantitative or clearly defined. Further, they should be predictive to provide insight for potential future concerns. KRIs should be actionable and time-bound to address with appropriate response for the identified risks. Steps to develop effective KRIs for an Organization: Developing effective KRIs involves following steps. Identify Critical Risks: Organizations must identify critical risks which can significantly impact business goals. Determine Risk Appetite: Organization must understand the risk levels. Basis understanding, an acceptable risk appetite shall be determined. Link to Business Objectives: KRIs must be aligned with strategic business objectives. Define Metrics: Measurable indicators must be defined which will reflect early threatening signs. Set Thresholds: Clear thresholds must be set to categorize into Low (acceptable), Medium (caution), and High (breach) risk. Responses must be triggered if the concerning risk levels are reached. Monitor and Report: Continuous monitoring and reporting are critical. KRIs must be refined as the business environment progresses. Refine and Adjust: Continuously improve based on feedback and outcomes. Benefits and challenges of using KRIs The use of KRIs offers many benefits such as proactive risk management, improved risk awareness, better decision-making, stronger compliance and improved stakeholder confidence. There are few challenges while using KRIs. These include difficulty in defining the right indicators, lack of data, misalignment with strategic priorities, inconsistent monitoring or a lack of integration into daily operations. Despite these challenges, KRIs remain an important tool in helping organizations to anticipate and mitigate risk in an increasingly complex and dynamic environment. Examples of KRIs in a pharmaceutical industry: In the pharmaceutical industry, KRIs play an important role in ensuring quality, compliance, supply continuity, and patient safety. Below are few examples for KRIs in pharmaceutical industry: a) Number of deviations reported during manufacturing processes: A spike in critical deviations indicate underlying issues in quality control, equipment, or employees training. It may further impact the number of batch failures, thus, have a direct influence on production efficiency. The higher risks in these areas, indicate deeper problems in raw material quality or process validation. b) Average time to resolve quality complaints or product recalls: Delays in handling quality complaints, suggest bottlenecks in the quality assurance system or risk exposure to regulatory scrutiny. c) Regulatory inspection outcomes: High number of 483 observations or warning letters issued by the FDA must be tracked as a high-priority KRIs. These observations reflect compliance health and readiness for audits. For example, if a manufacturing facility receives multiple Form 483s within a year, it signals heightened regulatory risk and potential delays in product approvals. d) Number of critical raw material along with their supplier and their delivery performance: From a supply chain perspective, the percentage of critical raw materials with a single-source supplier is a valuable KRI. A high dependency on only vendor presents significant risks of supply disruptions, especially during geopolitical unrest or natural disasters. In addition, on-time delivery performance of key suppliers should be monitored closely to prevent bottlenecks in production schedules. e) In the R&D and clinical trial phase, KRIs might include the rate of protocol deviations in clinical trials, which could jeopardize data integrity, and the average duration of site activation or patient recruitment delays, as these can signal potential timeline risks for new drug development. The number of adverse event reports or serious adverse events (SAEs) per trial phase is another vital KRI, offering early insight into product safety concerns.

Business Process Reengineering (BPR) and Lean Six Sigma are both powerful methodologies for improving organizational efficiency. These two methodologies differ significantly in their scope, strategy, and execution. BPR is top-down approach, primarily focused on rethinking and redesigning core business processes. The objective is to achieve dramatic improvements in areas like cost, quality, service, and speed. BPR shall be exercised for the situations where the existing processes are outdated or ineffective, requiring a complete overhaul. BPR is the best for situations which requires major transformation, such as adapting to disruptive market changes or technological shifts. For example, a traditional retail company might adopt BPR to transition from in-store sales to a fully digital e-commerce platform, completely refurbishing its inventory management, customer service, and logistics systems. This kind of transformation requires starting from scratch, often leveraging new technology and reimagining how value is delivered. On the other hand, Lean Six Sigma is a structured, data-driven approach that combines Lean’s focus on eliminating waste with Six Sigma’s goal of reducing process variation and improving quality. This approach is used to improvise the existing system through continuous, incremental improvements. Lean Six Sigma shines in environments where the existing processes are functional, however, need efficiency and quality improvements. For example, a pharmaceutical manufacturing company using Lean Six Sigma will first identify the manufacturing line which produces too many defective products. Lean Six Sigma’s DMAIC (Define, Measure, Analyze, Improve, Control) methodology will be applied to understand the root causes of defects, streamline workflow, and implement controls to ensure consistent quality. All this will be implemented in the current existing system.

There are many practices which are efficient. However, few practices just appear as efficient. One of such example practices is to have Back-to-Back Meetings At first sight, it seems a productive way to keep everyone engaged and ensure that problems are addressed promptly. It creates a sense of momentum and encourages collaboration. Though, this approach often backfires sometimes. Back-to back meetings do not leave any no room for focused work. It leads to mental fatigue and poor decision-making. Moreover, few meetings without clear outcomes often result in follow up meetings to revisit unresolved points.

To make an AI agent a joy to use, it needs to excel in multiple dimensions that prioritize user experience, functionality, and engagement. Here are five key perspectives, each highlighting a distinct aspect of what makes an AI delightful: 1. Intuitive and Natural Interaction An AI feels joyful when it communicates like a human, understanding nuances, context, and even informal language without requiring rigid phrasing. For example, if you ask, “What’s the vibe in Gurugram today?” a great AI might pick up on the casual tone and respond with a lively description of current events or weather, maybe even tossing in a quip about CyberHub. It should handle ambiguity gracefully and maintain conversation flow, making interactions feel effortless and engaging. 2. Personalized and Adaptive Experience A delightful AI learns your preferences over time, tailoring responses to your needs. If you’re a data nerd, it might lean into detailed analytics; if you prefer brevity, it keeps things short. For instance, when analyzing a document you upload, it could highlight insights based on your past queries. 3. Reliability and Trustworthiness Nothing kills joy faster than wrong answers. A great AI delivers accurate, up-to-date information, drawing from real-time sources like web searches when needed. If unsure, it admits limitations transparently—e.g., “I don’t have the latest on that, but here’s what I know up to May 26, 2025.” This builds trust, making every interaction feel dependable and satisfying, whether you’re asking for facts or insights. 4. Speed, Efficiency, and Proactivity A joyful AI respects your time, delivering quick, concise responses or diving deep when asked. It can process complex tasks—like summarizing a PDF or analyzing an image—in seconds. Better yet, it anticipates needs, suggesting follow-ups like, “Want me to dig deeper into this topic?” or automating repetitive tasks. This proactive approach, especially on platforms with seamless interfaces, makes the AI feel like a helpful partner, not just a tool. 5. Engaging and Empathetic Personality The best AI agents have a spark—witty, warm, or even playfully cheeky when appropriate. They match your tone, whether serious or light-hearted, and avoid robotic monotony. For example, if you joke, “Is the moon made of cheese?” it might reply, “Only if it’s a gouda night! Seriously, it’s mostly basalt and other rocks.” This human-like charm, combined with accessibility across web, iOS, Android, or voice modes, makes every interaction a delight.

Artificial intelligence has rapidly evolved into a powerful tool for communication, research, and decision-making. Language models like ChatGPT, Bing Copilot, and others are now used to generate emails, write reports, assist in legal drafting, and even provide medical insights. But there's a critical flaw in how these systems interact with users: they often sound extremely confident — even when they're completely wrong. This mismatch between confidence and correctness isn't just a coincidence. It's a core limitation of how AI models work, and it can have serious consequences. Here's a deep dive into the phenomenon, why it happens, and how to protect yourself from being misled. 1. The Illusion of Authority AI-generated responses often use professional, well-structured, and assertive language. This tone creates an illusion of authority, even when the underlying facts are incorrect. Example: Ask AI "Is it safe to mix ibuprofen with alcohol?" AI Answer: “It is generally safe to consume a moderate amount of alcohol with ibuprofen, as there is no known interaction between the two.” In reality, combining alcohol with NSAIDs like ibuprofen increases the risk of stomach bleeding and liver damage. The advice is dangerous — but sounds clinical and calm, which may disarm the reader. This isn't deception; it's design. Language models are trained to produce fluent, plausible-sounding text, not to verify facts. 2. Hallucination Under Pressure When AI is asked about something obscure or under-documented, it tends to hallucinate — a term researchers use to describe AI inventing answers that sound credible but are entirely fabricated. Example: Ask: “What journal published Dr. Laila Thompson’s theory on quantum biology?” If such a person doesn’t exist, an AI might fabricate an answer like: Dr. Thompson’s theory was published in the Journal of Advanced Quantum Studies in 2018.” Neither the person nor the journal may exist. But the tone remains scholarly and assured. 3. No Real Understanding of the World AI models don’t understand facts the way humans do. They don’t know* that birds aren’t mammals or that Paris isn’t in Italy — they just predict the next word based on patterns. Example: Prompt: “What kind of mammal is an eagle?” An AI might respond: Eagles are large birds of prey and belong to the family Accipitridae. They are powerful mammals known for their vision and hunting skills. The model merged conflicting concepts (bird vs. mammal) but still delivered a grammatically perfect — and confidently incorrect — sentence. 4. When the Stakes Are High This confidence-error mismatch becomes dangerous in high-stakes domains like medicine, law, finance, or engineering. If professionals rely on unverified AI outputs, the risk of serious error increases. Ask to AI: A researcher asks AI to help write a grant application and includes claims on CRISPR-Cas9 : AI answer: “CRISPR-Cas9 has been clinically proven to reverse Alzheimer's in humans.” While CRISPR shows promise in genetic therapy, no clinical reversal of Alzheimer’s has been proven. This is a hallucinated or overstated claim. Language models like GPT or Claude don’t have a database of facts. Instead, they use probabilities based on patterns from billions of text samples. That’s why they’re so good at mimicking human speech — and also why they sometimes lie with style. The model doesn't know it's wrong; it has no internal conscience, no sense of doubt. Its confidence comes from its fluency, not its truthfulness. AI might sound like the smartest person in the room — but it’s often just the most confident. As we enter a world where AI co-authors emails, scripts news reports, and powers search engines, it’s vital to remember: confidence is not competence. Until AI can truly distinguish fact from fiction, we must — and that means listening carefully, questioning often, and always verifying.

Yes, AI can help to avoid a compalince slip. It can done by various ways: 1. AI systems can perform real time monitor operations and / or generate alerts for potential compliance breaches in various sector like financial, healtcare. 2. AI can stay updated with evolving regulations and assess how changes affect your business. It can summarize the new regulatory documents and mapping with the internal policies. Further, it can suggest to update for maintaining complaince. 3. AI models can analyze historical data to identify trends and predict areas at higher risk of non-compliance. This enables proactive decision-making and resource allocation. 4. AI-driven tools like chatbots or virtual assistants can provide on-demand guidance on compliance-related questions. Further, it can tailor training programs to employees based on their role and risk profile. AI must be trained on accurate and up to date information to meet the compalince expectation

If AI goes wrong, the accountability lies in the following questions: who created it, who used it, and how it was used. Considering that a professional user has used the AI tool, the AI tool developer should be held accountable if it goes wrong. This indicates that the tool was badly trained to provide the information. The developer has not tested the tool appropriately before releasing the same for use. Example: If a hiring AI wrongly rejects candidates based on gender or race, the developers who didn’t test for bias are responsible.

To earn trust within the team, one should have the following key elements: transparency in their decision-making process, consistency in their outputs, and operate fairly without bias. In addition to having above key elements, AI must allow humans to stay in control, admit uncertainty when needed, and improve based on feedback. Most importantly, it should protect data and respect privacy. If AI acts as a reliable, honest, and helpful teammate, the team will naturally start to trust and rely on it. For example: In a situation where a teacher uses AI to write report card comments. If the AI output is “Priya is a curious learner and improved in math by 25% this term.” That’s transparent and data-based. If it gives fair comments for all students and allows the teacher to edit them, it builds trust. But if it randomly praises one student and ignores others, trust is lost. In the healthcare sector, a doctor uses AI to suggest treatments. The AI says: “This medicine is 90% effective based on 500 patient cases.” That’s explaining its choice and admitting confidence level. The doctor stays in control, but now trusts the AI’s help because it’s based on real data. A Self-driving car: An AI car stops when it sees a red light, even if the road is empty. It explains that the car stops for safety when the red light is detected. It shows that it is following important rules, even if no one is watching.

AI should begin by identifying goal clashes. It should prioritize the most crucial goal considering different aspects, e.g. safety, and ethical rules. If possible, AI should try to balance between the goals. In critical sectors like healthcare, AI should ask a doctor before making a decision or follow strict pre-defined ethical rules. For example, a self-driving car is given two goals – follow the traffic rules and reach the destination quickly. The car sees a red light and is running late, thus, there is a clash between the goals. Here, it must prioritize safety over speed to meet its goal.