Piyush Ranjan’s Post

Every few months, the same headline: AGI is just around the corner. AI will replace humans. Productivity will skyrocket overnight. Here’s the truth: AI doesn’t work like that in organizations. Businesses runs on experiential knowledge, the stuff only experts know: → The context behind a contract clause → The nuance in a medical file → The judgment call in a financial workflow This knowledge isn’t in training data. That’s why AI doesn’t succeed as a plug-and-play replacement. What actually works is context engineering. But it’s much harder than it looks. Knowledge is messy and scattered across Outlook, Teams, SharePoint, Jira. And then there’s the technical boundary: the model’s context window. You can’t just dump everything in. You have to measure, prioritize, and structure the right slices of context so the model can actually interpret it. And this is always the balancing act: → Too little context → the model hallucinates → Too much context → the model drowns in noise → Just enough context → the model behaves like a domain expert Scaling AI isn’t about deployment. It’s about expertise. You need compliance officers, HR specialists, and domain leaders to: ✅ Curate knowledge ✅ Define guardrails ✅ Evaluate outputs ✅ Feed back improvements That’s when AI stops being a demo and starts becoming reliable, trustworthy, and ROI-positive. AI doesn’t replace organizational work. It reshapes it. Frontload expertise, and the payoff compounds over time. 👉 AI + Expertise + Context = Adoption that actually works. ---- These slides are extracts from a keynote. A snapshot of the insights and hard-earned experience we’re building at Faktion | AI Solutions & Advisory. The full story goes much deeper. It’s just one of the ways we help you see past the hype.

I always love Jan Vanalphen's posts. Always pragmatic and reliable. AI is not a simple magic formula. It is not a simple "plug an API" and play. It is not a simple tool that will solve your sales, HR, coding, customer support or more in a simple click. Too many solutions are over-hyped, or oversold. Go through Jan's post for more details on how AI projects or implementation should be considered.

🤖 Can AI agents be trained? The short answer: yes — and it’s one of the reasons they’re becoming so powerful for businesses. AI agents aren’t just chatbots giving answers. They can be trained, fine-tuned, and improved to perform specialized tasks. Here’s how: 🔹 Custom Data Training – Feed them company knowledge, FAQs, or workflows so they provide accurate, context-specific responses. 🔹 Fine-Tuning & Reinforcement Learning – Agents adapt through user feedback and corrections, getting smarter over time. 🔹 Prompt Engineering & Rules – Structured prompts and playbooks can “train” agents without writing new code. 🔹 Continuous Learning – Advanced agents track outcomes, learning from successes and failures to optimize performance. This means an AI agent that starts by scheduling meetings can evolve to manage client interactions, analyze data, or even automate compliance tasks. 💡 The takeaway: Training makes AI agents more than just assistants — it turns them into scalable team members that learn and grow with your business. ❓What task would you train an AI agent to handle first? #AIagents #ArtificialIntelligence #Automation #FutureOfWork #MachineLearning #BusinessGrowth #ProductivityTools #AItraining #DigitalTransformation

🤖 Can AI agents be trained? The short answer: yes — and it’s one of the reasons they’re becoming so powerful for businesses. AI agents aren’t just chatbots giving answers. They can be trained, fine-tuned, and improved to perform specialized tasks. Here’s how: 🔹 Custom Data Training – Feed them company knowledge, FAQs, or workflows so they provide accurate, context-specific responses. 🔹 Fine-Tuning & Reinforcement Learning – Agents adapt through user feedback and corrections, getting smarter over time. 🔹 Prompt Engineering & Rules – Structured prompts and playbooks can “train” agents without writing new code. 🔹 Continuous Learning – Advanced agents track outcomes, learning from successes and failures to optimize performance. This means an AI agent that starts by scheduling meetings can evolve to manage client interactions, analyze data, or even automate compliance tasks. 💡 The takeaway: Training makes AI agents more than just assistants — it turns them into scalable team members that learn and grow with your business. ❓What task would you train an AI agent to handle first? #AIagents #ArtificialIntelligence #Automation #FutureOfWork #MachineLearning #BusinessGrowth #ProductivityTools #AItraining #DigitalTransformation

AI is a tool for your real business problems hiding in plain sight. You just need to know where to look. Most businesses are sitting on AI goldmines without knowing it. Here’s how to spot your next AI project: Repetition is your first clue. → If you do the same task 50 times a week, you’re in automation territory. → AI thrives on routine, rule-based work. → Think data entry, invoice processing, or sorting emails. Structured data is your second clue. → If your work lives in rows, columns, or databases, AI can help. → AI can analyse, connect, and act on this data faster than any human. → Sales reports, customer lists, inventory logs -these are all AI-ready. Decision logic is your third clue. → If you can explain how you make a decision, you can often teach AI to do it. → You’re not removing judgment -you’re codifying it. → AI can follow clear rules, spot patterns, and make recommendations. Low-impact tasks are your fourth clue. → If you stopped doing a task and nobody noticed, it’s a candidate for automation. → Free up your time for work that actually moves the needle. → Let AI handle the busywork. If you nodded along to any of these, you have an AI project waiting. Here’s how to get started: 1. List your most repetitive tasks. 2. Identify where you use structured data. 3. Write down the rules or logic you use to make decisions. 4. Flag any work that feels like a time sink. Now, match these to AI tools or automation platforms. Proof? Companies that automate routine work see faster turnaround, fewer errors, and more time for high-value projects. AI is already saving teams hours every week. Stop waiting for a sign. The opportunity is right in front of you. Start small. Automate one task. Watch the impact. Your business is ready for AI. Don’t let those hidden projects stay hidden.

Your AI agents will probably fail in production. Well, at least statistically speaking. According to Gartner and MIT, many AI agent projects will never make it to production, which is why AI projects fail to impact P&L. Over the last few months I wrestled real agents into BI and analysis workflows. What I learned might be the most obvious thing: agentic workflows are workflows. Programming systems with agents in mind is still... Programming systems. I see lots of business running DIY agents, they even celebrate building quick demos. But once they get closer to a production launch, the ever-daunting questions come back like a boomerang: what about deployment? Security? Access to data and context? AI data governance? PII and AI privacy? Ownership? AI agent guardrails? The list of topics and risks is long. For example, your agents must get enough context to be helpful, but this context must be well-governed, providing the least necessary levels of permission. The system needs to be ruled by clear, auditable policies. The data layer requires identifiable agents with secure and short-lived access to all the information and capabilities they need to do real work, including least privilege for AI agents and row-level security (RLS) where appropriate. What is your experience with running AI agents in production? Any important warnings for other practicioners?

This is absolutely the key to the future of software development. The key is going to be building AI prompts, and delegating work effectively to AI.

Hot take: your AI rollout isn’t a tech project. It’s a change-management project with a little bit of code. We keep overcomplicating it: trying to build a “second brain” that quietly becomes a second job. Meanwhile, the point of AI is simple: dump the boring work so humans can do the human work. None of this is new. I remember a 2004 leadership book saying the same thing (yeah, there were automation and AI in use back then): Tools change; psychology doesn’t. People don’t resist AI, they resist chaos, unclear “why,” and extra steps disguised as innovation. Also: less is more. Sam Altman still defaults to a paper notebook. Because clarity beats complexity. If you’re rolling out AI in a service business, try this: Start with people • Define the “human moments” you will not automate (sales calls, conflict, craft). • Explain the why: time back for service, not headcount cuts. Hunt the boring work • Map 2–3 repeatable workflows burning hours (intake, quoting, reporting). • Standardize inputs (forms, fields) before you automate anything. Make it more boring • Ship the smallest automation that saves ≥30 min/week. • Add one rule: “When in doubt, go manual.” (Trust beats throughput.) Reinvest the time • Move the reclaimed hours to customer touchpoints, QA, and follow-ups. • Measure what matters: response time, NPS, error rate—not “# of prompts.” AI that makes your company more human > AI that makes a slick demo. Want my Automation 1-Pager? Nah. No generic docs here. Your mess is unique. Tell me the one workflow you hate mos or your bottleneck in 10 words below.

If you’re a financial service provider, AI can multiply your effectiveness by automating routine work, deepening client insights, and sharpening investment decisions. Below are some AI tools and skills to learn, organized by use case. 1. Client Intelligence & Relationship Management Learn: • How to integrate CRM APIs with OpenAI or Azure Cognitive Services. • Prompt engineering for personalized communications. • Building client persona models from CRM + external data. 2. Investment Research & Portfolio Optimisation Learn: • Python for financial data analysis. • Prompting LLMs to summarize research reports. • Fine-tuning AI models on historical portfolio data. 3. Financial Planning & Advisory Automation Learn: • Prompt workflows to auto-generate client reports. • AI compliance monitoring (anomaly detection, language flagging). 4. Presentation, Reporting & Client Communication Learn: • AI report summarization and narrative generation. • Automating PowerPoint/Excel workflows with AI. For some, these tools may present a technical runway to master. Others might be thinking of adopting AI apps to produce the desired outcome pertaining to individualised cases. In a nutshell AI could be an additional assistance, to add aplenty values by generating leads and increase book size.

→ Have you ever wondered what truly powers seamless AI interactions behind the scenes? The answer often lies in the hidden frameworks we rarely notice. Enter the 𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 (𝐌𝐂𝐏), the bridge connecting AI models with context-rich data across systems. • 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐂𝐏? MCP stands for Model Context Protocol. It allows AI models to fetch, update, and interact with contextual data from multiple sources efficiently. Think of it as a universal translator between your AI and the world of information it needs. • 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 𝐌𝐂𝐏 𝐒𝐞𝐫𝐯𝐞𝐫𝐬 𝐘𝐨𝐮 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰: • GitHub MCP Server - Fetch and manage code context for AI-assisted development. • Perplexity MCP Server - Enhance AI reasoning with contextual knowledge from large datasets. • Slack MCP Server - Integrate team communication history to provide context-aware responses. • File System MCP Server - Access local files and structure AI workflows with system data. • Google Drive MCP Server - Retrieve and update cloud documents for collaborative intelligence. • Docker MCP Server - Connect containerized applications with AI context effortlessly. • Puppeteer MCP Server - Extract web page context dynamically for smarter AI actions. • 𝐖𝐡𝐲 𝐢𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: By leveraging these MCP servers, developers and enterprises can unlock real-time contextual AI insights, automate workflows, and bridge the gap between raw data and actionable intelligence. → The power of AI isn’t just in the model - it’s in how it understands the context around it. With MCP, context becomes the superpower your AI never had before. 👉𝐃𝐌 𝐦𝐞 𝐟𝐨𝐫 𝐜𝐚𝐫𝐞𝐞𝐫 𝐠𝐮𝐢𝐝𝐚𝐧𝐜𝐞/ 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐬𝐞𝐭 𝐮𝐩 👉Join the community to stay updated on new 𝐆𝐞𝐧𝐀𝐈-𝐀𝐠𝐞𝐧𝐭𝐢𝐜𝐀𝐈 advancements:- link in the comments section Follow Ujjyaini Mitra for more insights on Enterprise Gen AI