Mitchell Bregman

Enterprise software didn't treat every workflow equally. It never could. For twenty years, IT prioritized by transaction volume. High frequency systems got maintained. Screens stayed current. Processes reflected reality. Everything else got a document. Partner programs. Emerging market operations. Acquisition integrations. Performance cycles. Budget submissions. The workflows where strategic impact is highest and transaction volume is lowest. They lost the IT prioritization battle every time. Not because they didn't matter. Because the way enterprise software measured what matters was never designed to see them. The result is a three-tier class system hiding inside every large enterprise. - Tier one: Transaction systems. Maintained, current, well-served. - Tier two: High-impact, low-frequency workflows. Underserved by design. - Tier three: Periodic internal processes. Running on institutional memory and documents that degrade the moment they're written. Most enterprise leaders know this instinctively. Few have named it. Agentic architecture is the first thing that actually breaks it. Not because agents automate tasks faster. Because agents don't have a queue. The partner certification workflow that runs quarterly gets the same process autonomy as the system your team uses every hour. Strategic weight finally determines software quality instead of transaction count. That is not an incremental improvement. That is the class system dismantling. I wrote the full argument in this week's NextTurn. If you run a partner program, manage workflows across emerging markets, or have ever written a process change document and hoped for the best, this one is for you. https://lnkd.in/giw26MH3 #AI #Reimagination #Workflows

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LLMs are becoming commodities. Everyone has access to the same models. The same APIs. The same benchmarks. So where’s the real edge? Not in the model. In the logic above it. The companies that win won’t be the ones calling GPT, Gemini, or Claude. They’ll be the ones designing orchestration, memory, routing, and control layers on top. Stop building on models. Start building above them. Build on infrastructure, not APIs. Build with Chirpn. #AIInfrastructure #EnterpriseAI #AIOrchestration #AgenticAI #AIArchitecture

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🚀 Unlock the Power of LLMs Inside Snowflake — A Scalable AI Pipeline Tutorial Are you looking to integrate Large Language Models (LLMs) into your data workflows—without leaving Snowflake’s secure and scalable environment? Our latest deep-dive explores Snowflake’s end-to-end pipeline for CSV-to-LLM processing, enabling seamless AI-driven tasks like: ✅ Text Summarization ✅ Sentiment Analysis ✅ Named Entity Recognition ✅ Text Generation & Translation ✅ Code Generation ✅ Question Answering How it works: 1. Ingest CSV data into Snowflake tables 2. Use Snowflake Cortex functions (SUMMARIZE, CLASSIFY, COMPLETE, TRANSLATE) to process text directly in SQL 3. Automate with Streams & Tasks for real-time, scheduled LLM processing 4. Support external LLMs (like OpenAI) via Snowpark Python for extended flexibility Example SQL snippet for dynamic task routing: ```sql SELECT id, task, input_text, CASE WHEN task = 'Summarization' THEN SNOWFLAKE.CORTEX.SUMMARIZE(input_text) WHEN task = 'Sentiment Analysis' THEN SNOWFLAKE.CORTEX.COMPLETE('Analyze sentiment: ' || input_text) ELSE 'Unsupported Task' END AS llm_output FROM llm_tasks; ``` Why this matters: 🔹 No external API calls needed when using Snowflake Cortex 🔹 Fully governed, scalable, and automated within Snowflake 🔹 Reduces manual effort and accelerates insight generation 🔹 Ideal for batch or real-time AI workloads At [Your IT Academy/Consultancy Name], we help teams design, implement, and scale such intelligent data pipelines. Whether you’re starting with AI or optimizing existing flows, we provide training, architecture guidance, and end-to-end implementation support. 👉 Interested in building LLM-powered analytics inside Snowflake? Let’s connect! DM us or visit our website for consultancy and training programs. #Snowflake #AI #LLM #DataPipeline #Cortex #DataEngineering #AIIntegration #CloudComputing #TechConsultancy #ITAcademy #LearnAI #Automation

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LLMs don’t behave like traditional software – yet many teams still evaluate them as if they do. Single-run tests, static benchmarks, and deterministic QA leave product teams blind to real-world quality and user impact. At ODSC AI East 2026, Shane Butler, Principal Data Scientist at Ontra, shares how to design AI evaluation systems that reflect how products actually behave in production. In “How to Build Real-World AI Evaluation for Product Development,” Shane introduces a practical framework for evaluating AI features end to end – from inputs and model behavior to user experience and business outcomes. If you’re building AI-powered products and need evaluation methods you can trust to guide product decisions, this session gives you a clear mental model for measuring what matters – before issues reach users. 📅 April 28–30, 2026 🔗 Register here: https://hubs.li/Q041d_Wd0 #ODSCAI #AIEvaluation #LLMs #GenerativeAI #ProductAI #ProductionAI

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