Priya Sarkar

𝐓𝐡𝐞 𝐍𝐞𝐰 𝐀𝐈 𝐇𝐢𝐫𝐢𝐧𝐠 𝐑𝐨𝐚𝐝𝐦𝐚𝐩: The Roles Companies Will Actually Need Hiring for AI is no longer about finding one AI engineer. 𝐈𝐭'𝐬 𝐚𝐛𝐨𝐮𝐭 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐞𝐚𝐦𝐬 𝐰𝐢𝐭𝐡 𝐜𝐨𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐫𝐲 𝐀𝐈 𝐫𝐨𝐥𝐞𝐬. As AI adoption grows, organizations are moving beyond a single "Head of AI." Three distinct hiring tracks are emerging. 1. 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 These roles define AI strategy and governance. Examples: → Chief AI Officer → Head of AI → AI Product Manager → AI Risk & Governance Specialist Their job is to align AI with business goals, compliance, and long-term strategy. 2. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 AI is becoming part of every business function. Examples: → AI HR Business Partner → AI Customer Success Manager → AI Business Intelligence Analyst → AI Financial Reporting Specialist These roles combine domain expertise with AI-powered decision making. 3. 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 This is where AI systems are built and maintained. Examples: → AI Architect → AI Application Developer → AI/ML Engineer → AI Cybersecurity Analyst → Prompt Engineer They build, integrate, secure, and optimize AI solutions across the organization. The biggest takeaway? The future of AI hiring isn't about replacing existing jobs. It's about creating AI-specialized versions of them. The companies that succeed won't hire one AI expert. They'll build teams that combine strategy, business expertise, and technical execution. 𝐈𝐟 𝐲𝐨𝐮 𝐜𝐨𝐮𝐥𝐝 𝐡𝐢𝐫𝐞 𝐣𝐮𝐬𝐭 𝐨𝐧𝐞 𝐀𝐈 𝐫𝐨𝐥𝐞 𝐭𝐨𝐝𝐚𝐲, 𝐰𝐡𝐢𝐜𝐡 𝐰𝐨𝐮𝐥𝐝 𝐜𝐫𝐞𝐚𝐭𝐞 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐢𝐦𝐩𝐚𝐜𝐭: 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲, 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬, 𝐨𝐫 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥? ---------- I’m Priya Sarkar | Founder of TechWorm Consulting UAE & ThriveMark. We help SMEs, solo business owners, and leadership teams cut through AI noise and implement practical Agentic AI + Claude systems across Marketing, Sales, and Finance teams. And for elite AI Leaders, we help build a LinkedIn presence that reflects your leadership and attracts the right clients. #AI #ArtificialIntelligence #AIJobs #FutureOfWork #AILeadership #EnterpriseAI #AICareers #DigitalTransformation #GenerativeAI #AIStrategy

The AI Agent Roadmap Explained: 𝐅𝐫𝐨𝐦 𝐈𝐝𝐞𝐚 𝐓𝐨 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 Most teams start with frameworks. The best teams start with 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐭𝐡𝐞𝐲'𝐫𝐞 𝐭𝐫𝐲𝐢𝐧𝐠 𝐭𝐨 𝐬𝐨𝐥𝐯𝐞. Building an AI agent isn't about choosing the latest framework. It's about building the right workflow. Here's a practical roadmap. 1️⃣ 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐣𝐨𝐛 Start with one clear responsibility. Examples: → Summarize customer calls → Draft first-response emails → Triage IT tickets A focused agent is far more useful than trying to build an "AI employee." 2️⃣ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐞 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫 Think beyond prompts. Define the agent's role, instructions, context, and boundaries. This determines how it behaves in real-world scenarios. 3️⃣ 𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐦𝐨𝐝𝐞𝐥 The best model isn't always the most powerful. Choose one that fits your workflow, latency, reasoning needs, and budget. 4️⃣ 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐲𝐨𝐮𝐫 𝐭𝐨𝐨𝐥𝐬 This is where a chatbot becomes an AI agent. Integrate with CRMs, databases, Slack, ticketing systems, or internal APIs to perform real work. 5️⃣ 𝐀𝐝𝐝 𝐦𝐞𝐦𝐨𝐫𝐲 𝐚𝐧𝐝 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 Memory helps the agent retain context. Orchestration enables reliable multi-step workflows as complexity grows. 6️⃣𝐓𝐞𝐬𝐭 𝐛𝐞𝐟𝐨𝐫𝐞 𝐲𝐨𝐮 𝐬𝐜𝐚𝐥𝐞 Measure accuracy. Latency. Cost. Failure cases. The strongest AI agents improve through continuous evaluation. The biggest takeaway? Most AI agents don't fail because of the model. They fail because the problem, workflow, or governance wasn't clearly defined. 𝐖𝐡𝐢𝐜𝐡 𝐬𝐭𝐚𝐠𝐞 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐭𝐞𝐚𝐦𝐬 𝐮𝐧𝐝𝐞𝐫𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐞 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭: 𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠, 𝐩𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠, 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬, 𝐨𝐫 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧? --------- I’m Priya Sarkar | Founder of TechWorm Consulting UAE & ThriveMark. We help SMEs, solo business owners, and leadership teams cut through AI noise and implement practical Agentic AI + Claude systems across Marketing, Sales, and Finance teams. And for elite AI Leaders, we help build a LinkedIn presence that reflects your leadership and attracts the right clients. #AI #ArtificialIntelligence #AgenticAI #AIAgents #LLM #EnterpriseAI #AIArchitecture #MachineLearning #Automation #GenerativeAI

𝐈 𝐬𝐩𝐞𝐧𝐭 𝐬𝐨𝐦𝐞 𝐭𝐢𝐦𝐞 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐚𝐭 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐯𝐚𝐥𝐮𝐞 𝐰𝐢𝐭𝐡 𝐀𝐈. 𝐖𝐡𝐚𝐭 𝐬𝐮𝐫𝐩𝐫𝐢𝐬𝐞𝐝 𝐦𝐞 𝐰𝐚𝐬𝐧'𝐭 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲. It was how similar their execution playbooks looked. Whether it's NVIDIA, Amazon, Google, JPMorganChase, Walmart, Adobe, or Tesla... They're not treating AI as a side project. They're rebuilding core workflows around it. A few examples: ⚡ NVIDIA, Amazon, Google Building the infrastructure powering the AI economy. 💳 JPMorganChase, Visa, Walmart Using AI to improve fraud detection, pricing, risk, and operations. 🎨 Adobe, Canva, HeyGen Transforming how content gets created and distributed. 🚗 Tesla Embedding AI directly into products and decision-making. Different industries. Same strategy. ✓ Find a high-value workflow ✓ Rebuild it around AI ✓ Scale what works The biggest lesson? The companies winning with AI aren't running more experiments. They're making AI part of how the business operates. If you could redesign one workflow in your company with AI this year, what would it be? ---------- I’m Priya Sarkar | Founder of TechWorm Consulting UAE & ThriveMark. We help SMEs, solo business owners, and leadership teams cut through AI noise and implement practical Agentic AI + Claude systems across Marketing, Sales, and Finance teams. And for elite AI Leaders, we help build a LinkedIn presence that reflects your leadership and attracts the right clients. #AI #ArtificialIntelligence #GenerativeAI #EnterpriseAI #BusinessTransformation #DigitalTransformation #FutureOfWork #Innovation #AIStrategy #Technology

𝐓𝐡𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐒𝐭𝐚𝐜𝐤 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝: 𝐌𝐂𝐏, 𝐑𝐀𝐆, 𝐀𝐧𝐝 𝐒𝐤𝐢𝐥𝐥𝐬 As AI agents become more capable, three technologies are showing up in almost every architecture. - 𝐌𝐂𝐏. - 𝐑𝐀𝐆. - 𝐒𝐤𝐢𝐥𝐥𝐬. They often appear together. But they solve completely different problems. Here's the difference. - 𝐌𝐂𝐏 MCP is the 𝐭𝐨𝐨𝐥 𝐠𝐚𝐭𝐞𝐰𝐚𝐲. It provides a standardized way for AI agents to connect with APIs, databases, CRMs, and enterprise applications. Instead of integrating with every system individually, the agent communicates through MCP. - 𝐑𝐀𝐆 RAG is the 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐠𝐚𝐭𝐞𝐰𝐚𝐲. It retrieves relevant information from documents using embeddings and vector databases. It doesn't perform actions. It gives the model the right context to generate accurate responses. - 𝐒𝐤𝐢𝐥𝐥𝐬 Skills are the 𝐚𝐜𝐭𝐢𝐨𝐧 𝐠𝐚𝐭𝐞𝐰𝐚𝐲. They package repeatable tasks into reusable capabilities. Examples include: - Draft a contract - Summarize a meeting - Create a support ticket - Check an account balance Instead of writing new prompts for every workflow, agents invoke predefined skills. The interesting part? Enterprise AI rarely uses just one of these. A real workflow might look like this: → 𝐑𝐀𝐆 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐞𝐬 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐩𝐨𝐥𝐢𝐜𝐢𝐞𝐬. → 𝐌𝐂𝐏 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐬 𝐭𝐨 𝐲𝐨𝐮𝐫 𝐂𝐑𝐌 𝐨𝐫 𝐩𝐚𝐲𝐦𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦. → 𝐒𝐤𝐢𝐥𝐥𝐬 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐞 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐟𝐫𝐨𝐦 𝐬𝐭𝐚𝐫𝐭 𝐭𝐨 𝐟𝐢𝐧𝐢𝐬𝐡. Different components. Different responsibilities. One intelligent system. The biggest mistake isn't choosing the wrong technology. It's asking one layer to solve a problem another layer was designed for. Which one do you think is most misunderstood today: 𝐌𝐂𝐏, 𝐑𝐀𝐆, 𝐨𝐫 𝐒𝐤𝐢𝐥𝐥𝐬? ---------- I’m Priya Sarkar | Founder of TechWorm Consulting UAE & ThriveMark. We help SMEs, solo business owners, and leadership teams cut through AI noise and implement practical Agentic AI + Claude systems across Marketing, Sales, and Finance teams. And for elite AI Leaders, we help build a LinkedIn presence that reflects your leadership and attracts the right clients. #AI #AgenticAI #ArtificialIntelligence #LLMs #MCP #RAG #AIArchitecture #EnterpriseAI #SoftwareEngineering #AIAgents