Bot Nirvana

🤖 𝗧𝗵𝗲 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗳𝗮𝗺𝗶𝗹𝘆 𝗴𝗼𝘁 𝗮 𝗩𝗘𝗥𝗬 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗺𝗲𝗺𝗯𝗲𝗿... Meet the family: C: The respected grandfather. Built everything. C#: The overachiever. C's ambitious daughter. Java: The reliable corporate employee. Does the job. PHP: The web's workhorse. Gets things done quietly. Then there's RPA. RPA showed up to the family dinner with: • No code required • Drag-and-drop stuff • "I'll just record what humans do." How did we get here? Simple. Business moved faster than IT departments. People needed automation NOW. Not after 6 months of development. Not after learning C or C#. NOW. So RPA said, "I'll be the automation that business users can use." The result? RPA is the family rebel who: ✅ Enables non-technical people ✅ Delivers results in days, not months ✅ Doesn't care about "proper" programming ✅ Just gets most of the UI automation stuff done Sometimes the best solution isn't the most elegant one. It's the one that works for real people, solving real problems. What do you think? Is RPA or Low-code the future?

The Agentic Process Automation (APA) landscape is evolving rapidly, and we're keeping a close eye on it. Here's what we're seeing: ☁️ Cloud Providers: Microsoft, IBM, Amazon, etc., are integrating AI agent capabilities into their cloud platforms. 🤖 RPA Vendors: UiPath, Automation Anywhere, etc., are adding agentic capabilities to their RPA offerings. ⚙️ DPA/Low-code Vendors: Camunda, etc., are embedding AI agents into their process automation platforms. 🔗 iPaaS Vendors: Mulesoft (Salesforce), Workato, etc., are enhancing their integration platforms with AI agents. 🧠 AI-First Vendors: LangChain, Relevance AI, etc., provide frameworks for building and deploying AI agents. 🔗 Traditional automation players (RPA, BPA, IPaaS) are converging with APA. Are we missing any tools? Are you an APA player or know of any that's not mentioned, let's know in the comment below.

✨ 𝗧𝗵𝗲 𝘀𝗲𝗰𝗿𝗲𝘁 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗶𝘀 𝗵𝗮𝘃𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗱 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀. 🤖 However, getting controlled agents is difficult unless you have them embedded in your workflows. The best way to include agents in your workflows is to follow these five patterns shared by Anthropic👇🏻 ✍️ Prompt Chaining: Sequential steps. E.g. Content creation. 📞 Routing: Direct to experts. E.g. Customer service. 💡 Parallelization: Concurrent tasks. E.g. Financial analysis. 🤝 Orchestrator-Workers: Delegate and combine—E.g. Project management. 🎨 Evaluator-Optimizer: Refine outputs. E.g. Product design. Why do we need Controlled AI Agents? ✅ Reliable Outcomes: Predictable decision-making. 🔍 Error Tracking: Identify and address errors quickly. 💰 Cost Efficiency: Minimize expenses and maximize ROI. 📊 Audit Capability: Ensure compliance and accountability. 📈 Performance Metrics: Measure and improve performance. It's time to embrace Controlled AI Agents for enterprise automation! What do you think?

𝗣𝗹𝗼𝘁 𝘁𝘄𝗶𝘀𝘁: 𝗧𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝘀𝘁 𝗽𝗮𝗿𝘁 𝗼𝗳 𝗔𝗜 𝗶𝘀𝗻'𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗶𝗹𝗼𝘁𝘀. 𝗜𝘁'𝘀 𝗺𝗮𝗸𝗶𝗻𝗴 𝗶𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆. 🎯 AI pilots have a 90% success rate. AI production has a 90% stall rate. The gap is real. Here's what catches everyone off guard: 📊 Scale explodes: 100s → 10,000s of documents 👥 Users multiply: 10 friendly testers → 1,000s with opinions 🏗️ Infrastructure evolves: Shared resources → Dedicated fortress 🔒 Security shifts: "What could go wrong?" → "Everything WILL go wrong" Here's why this happens: AI in production isn't just "pilot at scale." It's a completely different beast requiring NEW approaches: 🔧 TECHNICAL: Robust monitoring, compliance, error handling, performance optimization 🎯 STRATEGIC: Governance frameworks, change management, dedicated teams, organizational alignment Bottom line: ✅ Pilots prove the AI works ✅ Production proves your ORGANIZATION works with AI Most companies nail the first part. The second part? That's where the magic (or chaos) happens. What's the biggest challenge you've seen getting AI to prod?

🚀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗳𝗮𝘀𝘁. 𝗜𝘀 𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗞𝗲𝗲𝗽𝗶𝗻𝗴 𝗨𝗽? Here's the evolution from simple scripts to fully autonomous systems, as described in the Agentic AI book. Note how capability and autonomy increase while human control decreases as we climb the ladder: 🤖 Level 1 - Rule-Based Automation: Simple rules, no intelligence. E.g., RPA copying data. 🧠 Level 2 - Intelligent Automation: AI-enhanced, handling unstructured data. E.g., Customer service bots based on the FAQ ⚙️ Level 3 - Agentic Workflows: Uses IA models, reasoning, and self-aware tool use. E.g., trading agents that execute complex financial transactions based on market conditions ✨ Level 4 - Semi-Autonomous Systems: Works independently, breaking down goals & learns from outcomes. E.g., Medical diagnosis agents that analyze patient data and recommend treatment plans 🌟 Level 5 - Fully Autonomous Systems: This is still theoretical, handling any goal. Example: Universal personal assistants. As we advance through these levels, we face a crucial dilemma: Greater capability enables more impressive outcomes, but comes with diminishing human oversight. The sweet spot? Finding the balance between the power of AI agents and maintaining meaningful control. What do you think?

🎯 𝗧𝗵𝗲 𝗚𝗲𝗻 𝗔𝗜 𝗽𝗮𝗿𝘁𝘆 𝗶𝘀 𝗼𝘃𝗲𝗿. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗔𝗜 𝘄𝗼𝗿𝗸 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗯𝗲𝗴𝗶𝗻𝗻𝗶𝗻𝗴. The new Gartner Hype Cycle reveals what we suspected: Gen AI is sliding into the trough of disillusionment. But here's what's rising to the top: - AI-Ready Data - AI TRISM - Sovereign AI - ModelOps - AI Agents Translation: Customers now want "AI-ready infrastructure," “AI orchestration,” and “AI agents,” not just another chatbot. Drawing on this, here are 5 pillars every AI-mature enterprise must master: 1. AI Agents: Autonomous, goal-oriented systems that learn and adapt 2. AI-Ready Data: Governed, cataloged, real-time streams that fuel decisions 3. AI Orchestration: ModelOps, workflow automation, and intelligent resource allocation 4. AI TRISM: AI Trust, Risk, and Security Management - focus on reliability and accountability 5. Sovereign AI: Localized models that meet regulatory, data residency, and privacy requirements Why this shift matters: → Infrastructure outlasts hype → Orchestrated agents > isolated AI apps → Governance beats speed-to-market always What this means for your AI strategy: Instead of asking "How do we use our internal ChatGPT or CoPilots?" Start asking "How do we orchestrate intelligent agents?" What do you think? Are you ready to ditch the Gen AI craze and architect dependable AI value?

𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗿𝗻𝗲𝘁 𝗳𝗲𝗲𝗱𝘀 𝗔𝗜. 𝗔𝗜 𝗳𝗲𝗲𝗱𝘀 𝘁𝗵𝗲 𝗜𝗻𝘁𝗲𝗿𝗻𝗲𝘁. 𝗦𝗲𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? 🔄 Model collapse is imminent: → Bad data goes in → Amplified bad data comes out → That o/p becomes training data for the next → The cycle continues, getting worse each time The solution? Start w/data responsibility Your AI is only as good as what you feed it. Remember: The companies winning at AI aren't collecting the most data. They're collecting the RIGHT data. What do you think? Image credit: Ralph Aboujaoude Diaz

🚀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗳𝗶𝗻𝗮𝗻𝗰𝗲 — 𝗮𝗿𝗲 𝘄𝗲 𝗿𝗲𝗮𝗱𝘆? IBM's report uncovers the urgent need for change in finance. (also applicable to other businesses) 🔍 𝗟𝗲𝗴𝗮𝗰𝘆 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝘀 𝗰𝗮𝗻'𝘁 𝗸𝗲𝗲𝗽 𝘂𝗽: Real-time governance is crucial. 🧠 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗿𝗶𝘀𝗸 𝗶𝗻 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀: Misalignment causes drift and deception. 📚 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮 𝗱𝗼𝘂𝗯𝗹𝗲-𝗲𝗱𝗴𝗲𝗱 𝘀𝘄𝗼𝗿𝗱: Enforce strict reset and audit policies. 🔍 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹: Prevent bias and data misuse. 📊 𝗔𝗴𝗲𝗻𝘁 𝗿𝗲𝗴𝗶𝘀𝘁𝗿𝗶𝗲𝘀 𝗼𝘃𝗲𝗿 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀: Track every agent like a microservice. 🔧 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗯𝘆 𝗱𝗲𝘀𝗶𝗴𝗻: It's imperative for AI deployment success. Are we leading AI design or reacting to its outcomes? 𝗗𝗲𝗹𝘃𝗲 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 𝗮𝗻𝗱 𝘀𝗵𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀!

𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗶𝗱𝗲𝗮𝘀? 💡 Here are 100 ideas. Download more formats and in high resolution here: https://lnkd.in/gBgE2NSJ Are you ready to integrate AI agents, and if so, where are you at?

Is your vendor choice based on outdated information? It’s time for a change! In an AI-first world, static quadrants mislead you on what's possible next. Gartner realizes this and now has an "Emerging market quadrant": ⏳ Garner "aims" to update this new quadrant every 4 weeks 📊 This new quadrant plots vendors based on features and future potential. 🔄 Vendors are required to submit information more frequently to stay relevant. 🚀 They are starting with "generative AI" vendors. (which is almost everyone? 😀) In terms of AI & Automation vendors: 📈 Microsoft leads the quadrant, showcasing the most features and future potential. 🚀 UiPath & AA follow behind and almost have the future potential of Microsoft. 🤔 📊 BPM players like Appian & Pega are "Emerging leaders" as well. (Are they stronger in a converging world?) 👀 Keep an eye on emerging players like Glean, Writer, etc. This new quadrant represents an evolving concept. A step in the right direction, but tracking such a diverse range of vendors within one quadrant may not be easy. What are your thoughts on this approach?