Real-Time Decision Making With AI Support

The future of real-time decision-making across industries lies in the intersection of AI/ML systems and ontology-driven semantic reasoning. Ontologies, particularly those leveraging Basic Formal Ontology (BFO) and Common Core Ontologies (CCO), are key to unlocking the full potential of aigenic systems across domains like healthcare, finance, energy, manufacturing, and intelligence analysis. The article, Paving the Way for AI and ML in Real-Time Clinical Decision Support, provides an excellent overview of how AI is revolutionizing healthcare. However, it leaves out a critical discussion about how ontologies, First-Order Logic (FOL), and object property assertions create the foundation for reasoning systems that are explainable, scalable, and trustworthy. These elements are essential for taking AI beyond pattern recognition and into actionable, logical decision-making. What’s Missing? 1. Explainability and Trust A key challenge with AI adoption is ensuring decision-makers can trust and understand the system’s reasoning process. Many machine learning models rely on probabilistic methods, introducing uncertainty and inconsistency in outcomes. Ontology-based systems, by contrast, use FOL axioms and object property assertions to ensure logical, repeatable reasoning. By formalizing relationships—such as “Treatment A alleviates Symptom B” or “Asset X operates within Constraint Y”—ontologies allow AI to deliver consistent and transparent decisions. For example: • In finance: An ontology-driven system can explain risk minimization based on explicit relationships between assets. • In energy: AI can justify maintenance decisions with sensor data aligned to predefined conditions. • In healthcare: Clinical recommendations can be traced back to explicit axioms and evidence encoded in the ontology, ensuring transparency for clinicians and patients. By removing probabilistic uncertainty, ontology-based systems ensure logical outcomes that are auditable, repeatable, and trustworthy—making them ideal for high-stakes decision-making across industries. 2. Scalability Across Domains Ontologies built on frameworks like BFO and CCO are designed to evolve with new knowledge. As industries generate more data, these ontologies can be updated to incorporate new relationships and rules, ensuring AI systems remain relevant and effective over time. Moving Forward By incorporating BFO, CCO, FOL, and object property assertions, we can transform AI/ML systems into powerful tools for real-time, domain-specific decision-making. These systems are not only intelligent but also trustworthy, adaptable, and future-proof. The article lays a great foundation for understanding the potential of AI in clinical decision-making, but to truly prepare for the future, we must address these missing elements. What do you think? Let’s connect to explore how ontologies can transform decision-making in your domain.

Edge computing is making a serious comeback in manufacturing—and it’s not just hype. We’ve seen the growing challenges around cloud computing, like unpredictable costs, latency, and lack of control. Edge computing is stepping in to change the game by bringing processing power on-site, right where the data is generated. (I know, I know - this is far from a new concept). Here’s why it matters: ⚡ Real-time data processing: critical for industries relying on AI-driven automation. 🔒 Data sovereignty: keep sensitive production data close, rather than sending it off to the cloud. 💸 Cost control: no unpredictable cloud bills. With edge computing, costs are often fixed and stable, making budgeting and planning significantly easier. But the real magic happens in specific scenarios: 📸 Machine vision at the edge: in manufacturing, real-time defect detection powered by AI means faster quality control, without the lag from cloud processing. 🤖 AI-driven closed-loop automation: think real-time adjustments to machinery, optimizing production lines on the fly based on instant feedback. With edge computing, these systems can self-regulate in real time, significantly reducing downtime and human error. 🏭 Industrial IoT (and the new AI + IoT / AIoT): where sensors, machines, and equipment generate massive amounts of data, edge computing enables instant analysis and decision-making, avoiding delays caused by sending all that data to a distant server. AI is being utilized at the edge (on-premise) to process data locally, allowing for real-time decision-making without reliance on external cloud services. This is essential in applications like machine vision, predictive maintenance, and autonomous systems, where latency must be minimized. In contrast, online providers like OpenAI offer cloud-based AI models that process vast amounts of data in centralized locations, ideal for applications requiring massive computational power, like large-scale language models or AI research. The key difference lies in speed and data control: edge computing enables immediate, localized processing, while cloud AI handles large-scale, remote tasks. #EdgeComputing #Manufacturing #AI #Automation #MachineVision #DataSovereignty #DigitalTransformation

“𝘝𝘪𝘤𝘵𝘰𝘳𝘺 𝘴𝘮𝘪𝘭𝘦𝘴 𝘶𝘱𝘰𝘯 𝘵𝘩𝘰𝘴𝘦 𝘸𝘩𝘰 𝘢𝘯𝘵𝘪𝘤𝘪𝘱𝘢𝘵𝘦 𝘵𝘩𝘦 𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘩𝘢𝘳𝘢𝘤𝘵𝘦𝘳 𝘰𝘧 𝘸𝘢𝘳, 𝘯𝘰𝘵 𝘶𝘱𝘰𝘯 𝘵𝘩𝘰𝘴𝘦 𝘸𝘩𝘰 𝘸𝘢𝘪𝘵 𝘵𝘰 𝘢𝘥𝘢𝘱𝘵 𝘵𝘩𝘦𝘮𝘴𝘦𝘭𝘷𝘦𝘴 𝘢𝘧𝘵𝘦𝘳 𝘵𝘩𝘦 𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘰𝘤𝘤𝘶𝘳.” – 𝘑𝘰𝘩𝘯 𝘉𝘰𝘺𝘥 Boyd’s OODA loop (𝗢𝗯𝘀𝗲𝗿𝘃𝗲 → 𝗢𝗿𝗶𝗲𝗻𝘁 → 𝗗𝗲𝗰𝗶𝗱𝗲 → 𝗔𝗰𝘁) revolutionized decision-making in fast-moving environments like aviation and combat. The same principles apply to AI-driven decision loops—except now, AI agents accelerate the cycle, allowing us to adapt in real-time rather than reacting after the fact. I like to visualize this concept with an infinity loop ♾️. Why? Because decision-making shouldn’t be linear or one-and-done—it should be a continuous cycle of data → insight → action → feedback, constantly learning and evolving. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝘄𝗶𝘁𝗵 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 Too often, we rely on static monthly or quarterly reports. We analyze trends after the fact, manually interpret the data, and then—maybe—take action. By the time we adjust, the situation has often already changed. 𝗧𝗵𝗲 𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗜𝗻𝗳𝗶𝗻𝗶𝘁𝘆 𝗟𝗼𝗼𝗽 With AI, this loop becomes continuous and dynamic: 🔢 Data: Signals are ingested in real time—no more waiting for static reports. 💡 Insight: The system identifies anomalies and emerging cost drivers as they happen. 💨 Action: AI suggests proactive steps before issues escalate—or opportunities vanish. 📣 Feedback: Every action generates new data, refining future recommendations. Instead of a report saying, “𝘊𝘰𝘴𝘵𝘴 𝘸𝘦𝘯𝘵 𝘶𝘱 𝘭𝘢𝘴𝘵 𝘲𝘶𝘢𝘳𝘵𝘦𝘳,” AI delivers real-time intelligence: “𝘛𝘩𝘪𝘴 𝘤𝘰𝘴𝘵 𝘥𝘳𝘪𝘷𝘦𝘳 𝘪𝘴 𝘦𝘮𝘦𝘳𝘨𝘪𝘯𝘨 𝘳𝘪𝘨𝘩𝘵 𝘯𝘰𝘸. 𝘏𝘦𝘳𝘦’𝘴 𝘩𝘰𝘸 𝘵𝘰 𝘢𝘥𝘥𝘳𝘦𝘴𝘴 𝘪𝘵.” 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗣𝗲𝗼𝗽𝗹𝗲, 𝗡𝗼𝘁 𝗥𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝗧𝗵𝗲𝗺 This isn’t about automating people out of the process—it’s about amplifying what HR teams, CFOs, and operations leaders can accomplish. The infinity loop represents a system that learns alongside the humans using it, transforming reactive problem-solving into proactive, strategic decision-making. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 (𝗘𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗹𝘆 𝗶𝗻 𝗛𝗥 𝗮𝗻𝗱 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀) Operations that are data-heavy—like HR benefits—stand to gain the most from this approach. When you close the loop continuously, you turn complex, thorny challenges into real-time, manageable decisions. AI agents provide a whole new way of automating to finally free people to do high impact work. That, in my mind, is where AI’s real power lies. Thoughts? Would love to hear how others are thinking about AI-driven decision loops in their domains.