Marcello Benati

An important article I wrote that differentiates LLM assistance and agentic AI. Please read. 🔗 https://lnkd.in/eb8mJyjJ A note for those applying to jobs: This matters for your career too. When you're in an interview and someone asks what you built, be precise. If you used an LLM to help you write code faster, say that, don't call it an agent. If you built a RAG pipeline, call it retrieval-augmented generation, don't call it agentic. If you built a chatbot with a system prompt, own it for what it is. Hiring managers who know the difference will respect your precision. Hiring managers who don't know the difference need you to teach them. Either way, you win by naming it correctly. Overselling what you built doesn't just hurt your credibility when someone digs deeper, it tells the room you don't actually understand the architecture you're describing. Know what you built. Know what it's called. Know why it matters at that level. That's what gets you hired. #AI #AgenticAI #LLM #NLP #SportsAnalytics #DataScience #CareerAdvice #MachineLearning

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At Scalytics, we're committed to using open models like Mistral, Llama, Gemma, and DeepSeek to create cutting-edge tools. Learn how below.

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2026: The Year Enterprise AI Gets a "Nerve Center" ➡️ The debate between LLMs and structured data is officially over. As we move into 2026, the real winners aren't just "using AI"—they are orchestrating it. I just finished an insightful piece by Bill Tanner featuring leaders from Graphwise and Propel, Inc , and the roadmap for the next 12 months is clear: Context is King, but Infrastructure is the Kingdom. Here are my 3 biggest takeaways on how the enterprise landscape is shifting: 1. Hybrid AI is the New Gold Standard We are moving away from the "neural intuition" of models alone. The future is Hybrid. By blending the creative power of LLMs with the precision of Knowledge Graphs, enterprises are finally solving the "hallucination" problem. In 2026, a model that can’t explain why it made a decision is a liability, not an asset. 2. The Rise of "Agentic" Ecosystems The era of standalone software is fading. We’re seeing a shift from static bots to multi-agent ecosystems that reason and collaborate. But here’s the kicker: these agents are only as good as their "shared memory." Knowledge graphs are becoming the digital nerve center that allows agents to act with traceable, auditable logic. 3. B2A: Business-to-Algorithm 📈 This is a fascinatng shift. Commerce is being redefined as AI takes over vendor discovery and selection. If your product data isn’t "algorithm-ready" (clean, structured, and semantic), your business might effectively become invisible to the digital buyers of 2026. Trust isn't just a feature anymore; it’s infrastructure. Companies that treat data governance as a strategic asset will turn their service departments from cost centers into high-margin revenue engines. What’s your take? Are you prioritizing agentic workflows this year, or is your focus still on foundational data cleanup? Let’s discuss in the comments. 👇 #EnterpriseAI #AgenticSoftware #KnowledgeGraphs #SaaS #DigitalTransformation #FutureOfWork #TrendsShaping2026 https://lnkd.in/gw6JNXfQ

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Autonomy is accelerating into operational authority faster than our institutions are adapting to govern it. The strategic inflection point is about architecting systems that preserve accountability, resilience, and decision legitimacy at scale.

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Another breakthrough in #AI was published earlier today by Sapient Intelligence, and the linked headline by VentureBeat says it all. https://lnkd.in/giYJtEb9 TL;DR, the new #HierarchicalReasoningModel (#HRM) reasons more like a human by quickly thinking about the best way to solve a problem and then thinking more slowly to solve the problem. This new method can be trained on an order of magnitude fewer training examples and can solve multi-step puzzles that simply befuddle #CoT #LLM s, like #ChatGPT and #Claude, ultimately requiring far fewer resources.

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A great piece explaining how knowledge graphs ground LLMs. LLM hallucination isn’t exactly a flaw, rather it’s a phenomenon of the architecture. It likewise is very expensive and difficult to retrain or teach LLMs new things ( which makes sense if you think about their insanely sophisticated transformer architecture). Knowledge graphs help cover these failing and works as an excellent foundation for RAG with context and more directed queries.

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From my upcoming book: AI-Powered Digital Twins (Wiley 2026) The technical hurdles for large scale AI deployments are mostly related to security, interoperability, and reliability. We address those throughout the book. Given enough investment and brain power, technical hurdles are bound to melt away. Human hurdles, on the other hand, need to be acknowledged and addressed early on, since no amount of technical prowess can overcome the human element, and it shouldn’t, since an AI-powered digital twin is built by humans and in service of humans. Thankfully, human hurdles, once identified, are not insurmountable. Any strategy for an AI-powered digital twin deployment must prioritize the human side: * Human legacy * Fear of the unknown * Intimidation due to lack of technical expertise * Lack of alignment between top down and bottom up * Limited human resources * Limited financial resources * Territory * Large organizational culture * Human Error Photo: Hirshhorn, DC

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"Companies that can train on exclusive datasets gain an advantage competitors can’t replicate." Just consider for a moment what exclusive datasets are you sitting on? That is a valuable, and therefore a financial, asset to leverage. If you are not leveraging that asset in an M&A or monetisation strategy, you are leaving money on the table.

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