Building Your AI Literacy
by Sandhiya Vignesh
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AI Atlas
This AI Atlas learning journey is tailor-made for non-tech professionals to confidently navigate the AI revolution. It's crafted from valuable public resources that have personally enriched my own learning experience.
HR Evolution with AI
A comprehensive learning journey designed specifically for HR professionals entering the world of artificial intelligence.
Week 1 Foundation
Begin your AI Atlas journey with fundamental concepts and practical applications in the HR landscape.
Practical Implementation
Learn how AI transforms HR processes through hands-on exploration and real-world scenarios.
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AI Atlas - Week 1 Plan
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A brief History of AI ( 7 minutes)
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What is AI for people in a hurry ( 5 minutes )
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AI for Everyone - Module 1 & 2 ( 2 hours )
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What is Machine Learning ( 17 minutes)
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Introduction to Large Language Models ( 15 minutes)
Google Cloud Skills boost for Partners ( check with your org if they have Partner ecosystem with Google) , If not , alternative is Microsoft Learn for this topic
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LLM's explained briefly ( 9 minutes)
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How might LLM store facts (22 minutes)
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Supervised & Unsupervised Learning ( 8 minutes)
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A Brief History of AI – The Future is Now
The journey of artificial intelligence spans decades of innovation and breakthrough moments that have shaped our modern capabilities.
AI is progressing towards Artificial General Intelligence (AGI), where machines could perform any intellectual task a human can. The future may bring advances in:
  • 🔹 Agentic AI – Autonomous AI agents executing complex tasks without human intervention. (e.g., OpenAI’s Operator)
    🔹 Explainable AI (XAI) – Enhancing transparency and trust in AI decision-making.
    🔹 Self-Learning Systems – AI models continuously adapting without retraining.
    🔹 AI Ethics & Governance – Regulations to ensure responsible AI usage.
    🔹 Biologically Inspired AI – Mimicking human cognition for advanced reasoning.
    🔹 AI-Human Collaboration – AI as a partner, augmenting rather than replacing human work.
AI’s journey from symbolic reasoning to deep learning and generative models highlights its transformative potential across industries, shaping the future of technology and human interaction.
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Current LLM Landscape
As of January 2025, the landscape of Large Language Models (LLMs) has seen significant advancements, with several notable models emerging:
OpenAI's Leaders: GPT-4o & o3
GPT-4o processes text, audio, and images in real time with rapid response and improved non-English performance. The newer o3 features enhanced reasoning capabilities with step-by-step processing, particularly excelling in coding, mathematics, and science.
Google & Meta's Innovations
Google's Gemini offers multimodal processing across text, images, audio, video, and code in three versions (Ultra, Pro, Nano). Meta's Llama 3 supports 30 languages with 8B and 70B parameter versions, trained on ~15 trillion tokens.
Asian Tech Giants
DeepSeek R1 achieves ChatGPT-level performance with just $5.6M budget. Alibaba's Qwen 2.5 supports 29 languages, scaling up to 72B parameters, specializing in code generation and mathematical problems.
Next-Gen Architecture
Mamba introduces the Structured State Space sequence model (S4), offering superior long sequence processing and hardware-aware parallelism. Mistral Large 2 features 123B parameters with a 128,000-token context window supporting 80+ programming languages.
These developments reflect the rapid evolution of LLMs, with a focus on enhancing efficiency, multimodal capabilities, and accessibility across various applications.
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Supplementary Readings - Week 1
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Industry Articles
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AI Playbook
Landing AI , Deeplearning.AI
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Teaching Computers to See
Fei Fei Li, a leading expert in AI, explores the fundamental challenge of enabling computers to understand and interpret visual information like humans do.
Real-World Applications
Computer vision powered by deep learning is revolutionizing various fields, from medical diagnosis to autonomous vehicles and advanced robotics.
Bridging Human and Machine Vision
The talk demonstrates how understanding human visual cognition helps create more sophisticated AI systems that can process and understand images naturally.
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Week 1 - Reflection Exercise
Personal AI Reflection
Identify and document three ways AI can enhance your personal productivity. Share your insights with your peers to spark discussion.
HR Innovation
Explore and list 3 HR tasks that could benefit from AI integration. Share your favorite AI concept with your peers in the group forum.
Deep Dive
Register for One Microsoft Learn Challenge to gain practical experience.

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Week 2 - Generative AI Essentials
Generative AI Fundamentals
Learn about Generative AI and its diverse applications across industries
Hands-on AI Experience
Gain practical experience working with ChatGPT and Claude AI assistants
Multimodal AI Systems
Understand how multimodal AI can enhance your productivity workflow
HR-Focused Prompting
Learn to craft effective prompts specifically for HR tasks and workflows
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AI Atlas - Week 2 Plan
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Introduction to Generative AI & Generative AI Fundamentals ( 30 minutes)
by Google Cloud Skill boost for Partners (( check with your org if they have Partner ecosystem with Google) , If not , alternative is Microsoft Learn / Coursera courses offered by IBM for this topic)
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GenAI Prompt Engineering for everyone ( 1 hour )
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Generative AI in a nutshell ( 16 minutes)
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What is RAG ( 7 minutes)
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How does GPT work ( 9 minutes )
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What are AI Agents ( 13 minutes )
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AI Assistant and AI Agent ( 7 minutes)
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What can GenAI do with data
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Why GenAI hallucinates and gives a different answer ( 6 minutes)
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What is Open Source vs Closed Source
  • Open Source Models: These models are freely available for use, modification, and distribution. While there are no licensing fees, users must consider costs related to hardware, energy consumption, and maintenance for training and deploying these models.
  • Closed Source Models: Access to these models is typically provided through subscription services or APIs, with costs varying based on usage parameters such as the number of tokens processed or the duration of use. Pricing structures differ among providers and are subject to change.
Imagesource : Armand Ruiz, IBM

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Widely Used LLM's
DeepSeek R1
Developed by the Chinese AI company DeepSeek, R1 is an open-source model that has garnered attention for its efficiency and performance. It is freely available for use, with costs primarily associated with the necessary hardware and computational resources for deployment.
o3-mini
OpenAI's o3-mini is a smaller and more efficient version of its advanced AI model, designed to compete with models like DeepSeek’s R1. It offers advanced reasoning capabilities and is available for free to all users of ChatGPT, with limited query capabilities for free-version users.
DeepResearch
Unveiled by OpenAI, DeepResearch is an agent that performs extensive web browsing, data analysis, and synthesis, delivering comprehensive reports within a short timeframe. It leverages the capabilities of OpenAI's o3 model and is accessible through OpenAI's platform.

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A tailored AI experience is like having a conversation with a colleague who knows your professional world inside out. It's about creating a shared language where the AI understands not just the 'what' but the 'why' of your inquiries.

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Ted Talk by Mustafa Suleyman
Key insights from Microsoft AI CEO and leading AI architect on the nature and future of artificial intelligence:
Evolution of AI
AI has transformed from a fringe technology to a mainstream force, rapidly evolving with significant increases in computational power and model sizes
A New Digital Species
Suleyman proposes viewing AI as an emerging digital species to better understand its trajectory and potential
The Infinite Inventor
AI demonstrates unprecedented capability for continuous innovation and creation, acting as an infinite inventor
Reflection of Humanity
AI ultimately reflects human values and capabilities rather than being a truly separate species
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Week 2 Reflection Exercise
Explore different AI tools and compare their outputs through these hands-on exercises:
Job Description Challenge
Create a job description using different AI tools (ChatGPT, Claude AI, Microsoft Copilot, Perplexity, Gemini) and compare their outputs to identify the most effective prompts.
Image Generation Comparison
Generate and compare images using Midjourney and DALL-E to understand the strengths of different image generation models.
Multimodal AI Experience
Create content using Suno AI for music generation and Notebook LM for podcasts to explore multimodal AI capabilities.
Prompt Engineering Deep Dive
Experiment with different input types (image, sound, text) and use ChatGPT to plan challenging tasks. Document your most successful prompts to share with the group.

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Multimodal Cheatsheet for People Professionals

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When to Use Each Top AI Model Right Now.

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Week 3- Data Privacy and Ethics in AI
AI Bias
Explore how biases can emerge in AI systems and strategies to identify and mitigate unfair algorithmic outcomes
Responsible AI
Discover frameworks for developing and deploying AI systems that prioritize human welfare, fairness, and transparency
Ethical AI Model
Examine ethical principles and governance models that guide AI development and implementation across organizations
Data Governance and Policy
Learn about critical policies for managing sensitive data, ensuring privacy compliance, and establishing ethical AI guidelines
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The Global Landscape of Artificial Intelligence Policies and Laws in 2025
The artificial intelligence regulatory environment has evolved significantly by 2025, with governments worldwide implementing varied approaches to address the challenges and opportunities presented by AI technologies. The global landscape reflects a complex interplay of comprehensive frameworks, sector-specific regulations, and ethical guidelines.
The EU AI Act represents the world's first comprehensive AI regulatory framework, with a risk-based approach and penalties up to €35 million or 7% of global turnover. Implementation is phased through 2027, with strictest provisions for "unacceptable risk" systems already in force.
The US employs a multi-faceted approach combining Executive Order 14110, voluntary industry commitments, and state-level initiatives rather than a single comprehensive law. The federal government has allocated significant funding for AI research institutes while states experiment with varied regulatory approaches.
China has implemented a comprehensive regulatory regime including the Interim Measures for Administration of Generative AI Services (2023) and provisions covering deep synthesis, ethical review, and algorithm recommendations. This positions China among the first nations to specifically regulate generative AI.
The United Nations General Assembly adopted the first global resolution on AI, co-sponsored by both the US and China along with 120+ nations. While non-binding, it establishes normative guidelines for human rights protection and risk monitoring.
The UK is reforming copyright laws for AI training and planning AI-specific legislation, while India has released AI Governance Guidelines emphasizing inter-ministerial coordination and is considering establishing an AI Safety Institute.
Efforts to regulate AI are emerging across Africa, with Mauritius, Kenya, and Nigeria taking leadership roles in developing national AI strategies. This reflects growing recognition of AI's importance for economic development and social welfare.
Many countries are developing AI regulations, with the U.S. proposing bills on chatbots. These target transparency and anti-deception for conversational AI.

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AI Atlas - Week 3 Plan
Data Privacy and Ethics in AI
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The AI Bias Before Christmas ( 5 minutes)
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Bias in AI and how to Fix it ( 4 minutes)
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Unvieling AI Bias - Real world Examples ( 4 minutes)
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How AI Image Generators Make Bias Worse ( 8 minutes)
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Introduction to Responsible AI ( 30 minutes)
by Google Cloud Skill boost for Partners (( check with your org if they have Partner ecosystem with Google) , If not , alternative is Microsoft Learn / Coursera courses offered by IBM for this topic)
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Five steps to Build AI Model ( 7 minutes )
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Important AI Trends in 2024
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Algorithmic Bias in AI - What It Is and How to Fix It ( 9 minutes )
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Data Governance Explained
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Supplementary Readings - Week 3
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Week 3 Reflection Exercise
Draft AI Ethics Guidelines
Create comprehensive ethics guidelines for your HR department, focusing on responsible AI use, fairness, and transparency principles.
Identify Privacy Issues
Analyze your current HR processes to identify potential privacy vulnerabilities and compliance gaps that need addressing.
US Executive Order Impact
Examine the US Executive Order on AI and determine its implications for your organizational policies. Research the California AI legislation and identify key provisions
UK AI Governance Law
Review the UK's approach to AI governance and compare with other regulatory frameworks.

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Get your AI-First Report Card
unknown link

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Week 4-6 : Automation in Action: From Microsoft Power Platform to Google Workspace and Beyond
This week, discover how leading automation tools are transforming the way we work. Explore hands-on examples and smart strategies to connect, streamline, and automate everyday business tasks.
Microsoft Power Platform
Build custom apps and automate workflows to supercharge productivity.
Google Workspace
Collaborate seamlessly by integrating automation with everyday tools like Gmail and Sheets.
Zapier & Air table
Create no-code automations that connect your favorite business apps in a few clicks.
Make.com & Gumloop
Design complex, multi-step automations without coding skills.
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AI Atlas - Week 4-6 Plan
Being a Microsoft Power Specialist
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Power Automate Tutorial ( 2.5 hours)
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Build Approval workflow using Power Automate ( 40 minutes)
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Design Power BI Reports (2 hours)
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Integrating Power Apps and Power Automate with Power BI ( 1 )
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AI Builder Overview ( ~3 hours)
This series covers various aspects of AI Builder, providing practical insights into building and deploying AI models within the Power Platform.
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✨ AI Feature Focus
  • Experiment with the “Describe it to Design it” feature in Power Automate.
  • Compare how Copilot builds flows vs manual logic creation.
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🧠 Reflection Exercise
  1. Create a cloud flow automating a task from your daily routine (e.g., email → spreadsheet log).
  1. Build a leave/expense approval workflow using Power Automate with dynamic routing.
  1. Try “Describe it to Design it” and compare its result with your manual build.
  1. Share screenshots and insights with your team on:
  • What worked?
  • Where did you modify it?
  • How did it impact your efficiency?
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AI Atlas - Week 4-6 Plan
Google Workspace Learner’s Track
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App Sheet - No code app builder using Google sheets, forms ( 20 minutes)
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Google Apps Script for Beginners ( 23 minutes)
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Looker Studio (2 hours)
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Integrating Power Apps and Power Automate with Power BI ( 1 )
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🧠 Reflection Exercise – Google Apps Script with AI
Try This:
Use Duet AI or Gemini for Workspace to generate a simple Apps Script that:
  • Sends an auto-reply when a specific label is added to an email or
  • Creates a calendar event from a form submission
Then Reflect:
  • What part did AI get right immediately?
  • Did you need to tweak the code manually? Why or why not?
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AI Atlas - Week 4-6 Plan
Other Platforms
Zapier
Connecting everyday apps (Gmail, Slack, Trello)
Make (Integromat)
Visual workflows and advanced routing
Airtable Automations
Spreadsheet-style project tracking + workflows
Gumloop 🧠
AI agents that automate complex tasks & build no-code tools fast
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My Gumloop Workflow

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📚Supplementary Resources – Learn Beyond the Tools
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A concise industry report on how AI is transforming HR functions, from recruiting to learning, and the shift towards augmented HR roles. Great primer for understanding the business impact of automation.
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A hands-on learning portal to explore how to build AI-powered tools using Gumloop. Try the “HR Bot,” “Feedback Collector,” or “Simple Workflow Builder” tutorials to get started quickly—no coding needed.
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Dive into use cases like form processing, sentiment analysis, and prediction models using Power Platform’s AI Builder. Use this with the YouTube series for deeper context.
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Browse real-world apps for leave management, project tracking, and incident reporting. Pick one and explore how it’s built—then try recreating a simplified version.
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Templates for dashboards using Google Sheets, GA4, or BigQuery. Great for HR analytics like attrition trends, engagement scores, or DEI metrics.

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🧠✨ Vibe Check: Are You AI-Ready to Build?
No pressure. Just play, explore, and check what your AI journey already looks like. 🎯
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Let’s turn “what if” into “look what I made.”?
TIME TO VIBE CODE !!
Your Go-To Tools now !!

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My First Vibe Coding Journey !

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How AI Actually thinks?

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How AI actually Thinks
What does the AI "hear"? (The Digital Eavesdropping Phase)
Your anxious midnight rambling gets converted into data the AI can actually work with3. It's like having a universal translator that turns your "I'm-probably-dying" English into AI-speak. The AI doesn't experience your 2 AM panic – it just sees text that needs processing. The work parallel? When you dump a messy employee complaint email on an AI and ask it to "make sense of this chaos," it's doing the same thing – converting human emotional word into structured data it can analyze.
What does it understand? (The Digital Detective Work)
The AI breaks your sentence into bite-sized pieces: "chest pain" (red flag alert!), "short of breath" (another red flag!), "should I be worried" (human seeking reassurance). It maps these to meanings and relationships – figuring out that this combo of symptoms typically warrants serious attention. Your HR reality check: Think about when AI analyzes exit interview feedback. It's spotting patterns like "management issues" + "no growth opportunities" + "considering leaving" and connecting those dots to predict turnover risk.
How does it decide what to do? (The Internal Committee Meeting)
Here's where it gets fascinating. The AI runs your words through layers of artificial "neurons" – imagine tiny digital specialists all weighing in. One layer flags the medical urgency, another considers your emotional state, and a third thinks about the most helpful response that won't either dismiss something serious OR send you into full panic mode. The business angle: When you ask AI to draft a performance improvement plan, it's balancing being constructive vs. being too harsh, being specific vs. being overwhelming. Same internal committee, different topic.
What action does it take? (The Moment of Truth)
The AI calculates possible responses: "This sounds serious, seek immediate medical attention" vs. "Here are some questions to help assess severity" vs. "Try some relaxation techniques." It picks the most appropriate response based on safety and helpfulness1. Your daily work life: When AI helps you respond to a difficult client email, it's weighing tone, urgency, and professionalism to craft something that addresses concerns without escalating the situation.
How does it keep learning? (The Plot Thickens)
As you respond with more details ("Actually, I just remembered I had five cups of coffee today"), the AI adjusts its assessment. It's like a conversation where each exchange refines understanding. The workplace connection: This is why AI gets better at understanding your company's communication style the more you use it. It learns that your team prefers direct communication over corporate speak, or that certain phrases trigger compliance concerns.

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Future is here !
MCP
Model Context Protocol (MCP) gives AI access to verified databases rather than general knowledge. In healthcare, MCP connects AI to medical guidelines and evidence-based assessments. At work, this means AI accessing your specific company policies and regulations instead of generic advice.
AI Agents
AI "specialists" work together like a team: one analyzes symptoms, another assesses urgency, while a third reviews your medical history. In business, this translates to specialized agents handling different tasks—candidate screening, interview scheduling, and performance analysis—all coordinating seamlessly.
AGENTIC AI
Agentic AI takes this further by proactively suggesting actions: calling your doctor or finding urgent care. At work, it identifies hiring bottlenecks, suggests interview slots, and automatically follows up with references.

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Learn more on MCP here !

Model Context Protocol

Introduction - Model Context Protocol

Get started with the Model Context Protocol (MCP)

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AI in HR - 2 minutes Talk

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🎯 What's in the Context-Engineered Library:
The difference? The second prompt gets specific, actionable insights because it understands your reality.
✅ Context Frameworks – How to feed AI your company size, industry, constraints, and current tech stack
✅ Role-Based Lenses – Wear different stakeholder hats (CFO, IT, employee, manager) for complete perspective
✅ Situational Modifiers – Adapt prompts for your timeline, budget, complexity, and risk tolerance
✅ Chain Prompting Strategies – Start broad, narrow down, then apply to your specific context

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🛡️ SAFE CONTEXT PRACTICES
✅ Safe to Include:
  • Company descriptors: "500-person tech startup," "enterprise manufacturing"
  • Industry classifications: "healthcare," "financial services," "SaaS"
  • General challenges: "complex approval workflows," "global compliance needs"
  • Public requirements: "GDPR compliance," "SOC 2 certification," "HIPAA requirements"
  • Workforce patterns: "remote-first," "hybrid," "seasonal hiring"
  • Tech categories: "Microsoft ecosystem," "cloud-first," "legacy systems"
  • Timeline constraints: "Q4 implementation," "90-day deadline"
  • Budget parameters: "enterprise budget," "startup constraints," "mid-market range"
❌ Never Include:
  • Employee personal data: names, salaries, performance details
  • Specific contract terms: actual pricing, negotiated rates, vendor discounts
  • Internal metrics: revenue figures, profit margins, confidential KPIs
  • Proprietary processes: trade secrets, competitive advantages, internal methodologies
  • Security details: specific system vulnerabilities, access credentials, architecture details
  • Legal matters: ongoing litigation, compliance violations, internal investigations
  • Competitive intelligence: acquisition plans, strategic initiatives, market positioning
🔄 Context Sanitization Tips:
  • Generalize specifics: "large enterprise" instead of exact employee count
  • Use ranges: "6-figure budget" instead of exact amounts
  • Describe patterns: "high-growth phase" instead of specific growth metrics
  • Reference public info: "publicly traded" instead of stock performance
  • Focus on requirements: "need mobile access" instead of current mobile usage stats
💡 Pro Context Tips:
  • Test with fake data first – verify your prompt works before adding real context
  • Use job titles, not names – "our CFO" instead of "John Smith"
  • Describe roles, not individuals – "skeptical manager type" instead of specific people
  • Public benchmarks only – industry standards, not internal comparisons
Remember: Rich context helps AI understand your situation. Smart boundaries keep your data secure. You can have both.

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🎯 HOW TO USE THIS LIBRARY
A systematic approach to using AI for smarter vendor decisions
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STEP 1: IDENTIFY YOUR EVALUATION PHASE
Pick the phase that matches where you are in your vendor selection process:
  • 🔍 DISCOVERY PHASE → Use Competitive Intelligence + Cost Analysis prompts
  • 📋 EVALUATION PHASE → Use User Experience + Implementation Reality prompts
  • 🤝 DECISION PHASE → Use ROI & Value + Stakeholder Alignment prompts
  • 📝 CONTRACTING PHASE → Use Scenario Testing + Future Planning prompts
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STEP 2: CHOOSE YOUR STAKEHOLDER LENS
Select based on who you're preparing for or whose concerns you need to address:
  • • 👑 Executive Presentation → ROI & Value Realization
  • • 💻 Technical Deep Dive → Implementation Reality + Scenario Testing
  • • 👥 Change Management → User Experience + Stakeholder Alignment
  • • 💰 Budget/Procurement → Cost Analysis + Competitive Intelligence

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🎯 HOW TO USE THIS LIBRARY
STEP 3: START WITH THE "POWER TRIO"
These 3 prompts work for any vendor evaluation:
Cost Reality Check:
"Act like a CFO's analyst for a {company size} {industry} company with a {budget range} annual HR tech budget. Review {Vendor Name}'s pricing page and identify 3 potential hidden costs that could impact our budget, considering our {specific use case} and {employee count} users."
User Experience Validator:
"Act like an employee experience researcher for a {company type} with {workforce description - remote/hybrid/in-office}. Based on user reviews, identify the top 3 frustrations with {Vendor Name} that would affect our {specific employee personas - managers/individual contributors/new hires} and suggest demo questions to address these concerns."
Implementation Risk Assessor:
"Act like an implementation project manager who specializes in {industry} companies. Create a realistic timeline for implementing {Vendor Name} at a {company size} organization with {current tech stack} and {complexity factors - global/multi-location/compliance requirements}. Identify 3 potential roadblocks specific to our setup and suggest mitigation strategies."

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🎯QUICK START GUIDE
First Time Using This Library?
  1. Start with the Power Trio (above)
  1. Pick 2-3 additional prompts from your current evaluation phase
  1. Customize with your specific context (company size, industry, current tools)
  1. Chain prompts together for deeper insights
🎯 PROMPTING BEST PRACTICES
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Be Context-Specific:
  • Include your company profile – Eg. "...for a fast-growing SaaS company," "...in a highly regulated industry," "...with seasonal workforce fluctuations"
  • Mention your tech ecosystem – "Microsoft-heavy environment," "Salesforce + Slack workflow," "legacy HRIS integration"
  • Reference constraints – "within 90 days," "under $50K budget," "must comply with GDPR"
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Structure Your Outputs:
  • Request organized formats – tables, bullet points, scoring systems for easy comparison
  • Ask for focused lists – "Top 3" or "Top 5" for clarity
  • Use formatting phrases – "Present in two columns," "Create a summary table," "Rank by priority"
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Chain Your Prompts:
  • Start broad: "Compare these vendors on key capabilities"
  • Narrow down: "Focus specifically on integration with Salesforce"
  • Apply: "Based on this analysis, what demo questions should I prioritize?"
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Include Confidence Indicators:
  • Rate reliability: "Rate your confidence in this analysis (1-10) and explain why"
  • Identify gaps: "What additional information would strengthen this assessment?"
  • Flag assumptions: "What assumptions are you making in this analysis?"
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Use Real Data:
  • Feed actual materials: vendor pricing pages, feature lists, case studies
  • Include user feedback: real reviews from G2, Capterra, app stores
  • Reference documentation: contract terms, SLA documents, technical specs
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Role-Play Multiple Perspectives:
• Run the same question through different stakeholder lenses
• Consider both optimistic and pessimistic scenarios
• Include voices of actual end users, not just decision makers

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39 Prompts to Evaluate HR Tech Vendors
💰 COST & CONTRACT ANALYSIS
Use when: Budget planning, contract negotiations, CFO presentations
  • Act like a CFO's analyst. Review this vendor's pricing page and identify 3 potential hidden costs or unclear pricing terms that could impact our budget.
  • Act like a procurement specialist. Compare the contract terms of {Vendor A} vs {Vendor B} for data portability, termination clauses, and liability. Highlight red flags.
  • Act like a budget analyst. Calculate the 3-year total cost of ownership for {Vendor} including licenses, implementation, training, support, and potential API overages for {X employees}.
  • Act like a vendor negotiator. Based on {Vendor}'s recent funding/market position, what leverage do we have in pricing discussions? Suggest 3 negotiation strategies.
🔧 IMPLEMENTATION REALITY CHECK
Use when: Technical feasibility assessment, IT stakeholder alignment
  • Act like an implementation project manager. Based on this vendor's case studies, create a realistic timeline and identify 3 potential roadblocks for our {size/complexity} organization.
  • Act like a change management expert. Analyze this tool's learning curve and create a user adoption strategy for employees who are {describe your workforce}.
  • Act like an IT security officer. Review {Vendor's} security documentation and flag any gaps for organizations handling {specific data types}.
  • Act like a systems integration specialist. Map out how {Vendor} will integrate with our existing tech stack: {list current tools}. Identify potential data flow issues.
👤 USER EXPERIENCE VALIDATION
Use when: Employee adoption concerns, demo preparation, UX assessment
  • Act like an employee experience researcher. Based on user reviews, identify the top 3 employee frustrations with {Vendor} and suggest questions to ask during the demo.
  • Act like a UX analyst. Compare the mobile experience of {Vendor A} vs {Vendor B} based on app store reviews. Which is better for our mobile-first workforce?
  • Act like an HR business partner. Translate this vendor's feature list into actual employee benefits. What tasks will employees no longer have to do manually?
  • Act like a frontline manager. How will {Vendor} change my daily workflow? What new tasks will I need to learn vs. what gets eliminated?
🕵️ COMPETITIVE INTELLIGENCE
Use when: Market research, vendor stability assessment, strategic planning
  • Act like a market researcher. Analyze recent news, funding, and leadership changes for {Vendor}. What does this tell us about their stability and product roadmap?
  • Act like a vendor analyst. Based on recent product updates, is {Vendor} prioritizing enterprise customers like us or focusing on SMB market?
  • Act like a due diligence specialist. Search for any compliance violations, data breaches, or legal issues involving {Vendor} in the last 24 months.
  • Act like a market intelligence analyst. Based on recent product updates from {Vendor A} vs {Vendor B}, which is innovating faster in areas that matter to our business?
📈 ROI & VALUE REALIZATION
Use when: Business case development, success metrics planning, executive buy-in
  • Act like a finance partner. Based on {Vendor}'s promised outcomes, what are 3 measurable KPIs we can track post-implementation to validate ROI?
  • Act like a people analytics lead. Build a framework to compare employee engagement metrics pre- and post-deployment. Suggest leading indicators within 90 days.
  • Act like an HR transformation lead. What are the short-term (6 months) and long-term (2 years) cost offsets from automating {X HR function}? Present in a 2-column breakdown.
  • Act like a productivity analyst. Calculate time savings if {Vendor} eliminates {specific manual process}. Show weekly and annual impact across our {X} person organization.
👥 STAKEHOLDER ALIGNMENT
Use when: Change management planning, communication strategy, resistance handling
  • Act like an internal communications lead. Draft a brief announcing this tool to employees, highlighting "what's in it for me" and addressing known pain points.
  • Act like a skeptical people manager. What resistance might I raise during {Vendor} rollout? How would you address concerns with data and proof points?
  • Act like an HRBP. Translate {Vendor}'s strategic benefits into direct team-level value for engineering, sales, and support leaders.
  • Act like an employee advocate. What concerns might employees have about {Vendor} regarding job security, privacy, or workload? Draft FAQ responses.
🧪 SCENARIO-BASED STRESS TESTING
Use when: Risk assessment, business continuity planning, global considerations
  • Act like a business continuity planner. What happens if {Vendor} goes down for 48 hours during peak {season}? Map out impact, backup plan, and communication strategy.
  • Act like a global HR lead. How does {Vendor} support use cases across multiple geographies and time zones? List gaps for teams in {specific regions}.
  • Act like a tech audit committee. Simulate a quarterly review 6 months post-implementation. What metrics and red flags would you track?
  • Act like a crisis management lead. If {Vendor} has a data breach, what's our incident response plan? Include legal, communication, and remediation steps.
🔮 FUTURE-STATE PLANNING
Use when: Long-term strategic planning, scalability assessment, contract terms
  • Act like a future-state planner. If we choose {Vendor}, what happens in 3 years when our headcount doubles? Does their model scale with us?
  • Act like a vendor relationship manager. What negotiation leverage do we have based on {Vendor}'s customer base and competitive position?
  • Act like a technology strategist. How does {Vendor}'s roadmap align with emerging HR tech trends? Will this solution be relevant in 5 years?

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Common Prompt Combinations
Budget Meeting
Cost Analysis + ROI & Value prompts
Demo Prep
User Experience + Competitive Intelligence prompts
Implementation Planning
Implementation Reality + Scenario Testing prompts
Executive Presentation
ROI & Value + Stakeholder Alignment prompts
Remember: AI accelerates research and uncovers blind spots. Use these prompts to ask better questions, not to replace due diligence.

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AI for People Leaders - MAVEN

maven.com

Build AI Tools That Save HR Leaders 10+ Hours Weekly

Every week, you lose 10+ valuable hours to manual HR work that AI could automate instantly. You're drowning in resume reviews, policy explanations, and routine communications while knowing there's a better way. The gap isn't technical knowledge—it's having a systematic approach to identify your biggest time-drains and eliminate them effectively.

maven.com

AI for People Leaders by Q Hamirani and Sandhiya Thiruvengadam on Maven

Master proven AI frameworks to automate HR tasks, build ethical policies, and lead strategic implementation that drives measurable impact.

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When LLM meets Analytics

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When LLMs Meet Analytics - A Non Tech Edition Part 2 | Sandhiya Thiruvengadam

𝐘𝐨𝐮𝐫 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦 𝐜𝐚𝐧 𝐟𝐢𝐧𝐝 𝐩𝐨𝐥𝐢𝐜𝐢𝐞𝐬 𝐛𝐞𝐚𝐮𝐭𝐢𝐟𝐮𝐥𝐥𝐲. 𝐁𝐮𝐭 𝐜𝐚𝐧 𝐢𝐭 𝐟𝐢𝐧𝐝 𝐚𝐥𝐥 𝐭𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫𝐬? As I close out my last newsletter of 2025, it feels right that it’s not about a new tool or prediction — but about a question that changed how I think about AI analytics. Few weeks back , while discussing Part 1(https://lnkd.in/e_5cEr6G) , Madina Gbotoe asked a follow-up that made all of us pause: 🧠 “I’m comfortable with RAG for words — but what about

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When LLMs Meet Analytics - A Non Tech Edition Part 1 | Sandhiya Thiruvengadam | 10 comments

𝘠𝘰𝘶𝘳 𝘈𝘐 𝘥𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥 𝘴𝘢𝘺𝘴 𝟮𝟯 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘦𝘴 𝘢𝘳𝘦 𝘢𝘵 𝘧𝘭𝘪𝘨𝘩𝘵 𝘳𝘪𝘴𝘬. 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘬𝘯𝘰𝘸 𝘪𝘵 𝘥𝘪𝘥𝘯'𝘵 𝘮𝘪𝘴𝘴 𝟭𝟱 𝘮𝘰𝘳𝘦? Last week in our Women Defining AI community, a single question from Nisha Pillai sparked one of the most important conversations we’ve had: “𝗜𝗳 𝗟𝗟𝗠𝘀 𝗮𝗿𝗲 𝗻𝗼𝗻-𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰, 𝗵𝗼𝘄 𝗱𝗼 𝘄𝗲 𝘁𝗿𝘂𝘀𝘁 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘁𝗵𝗮𝘁 𝘂𝘀𝗲 𝘁𝗵𝗲𝗺?” It made me pause. This week, I'm publishing Part 1 of

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Thank you.
This deck will be updated monthly now with my automation & vibe coding journey , so be sure to check back for upcoming content and topics.
The goal of this presentation is to build your AI literacy and provide an overview of the current landscape.
#sandysnotesonAI

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