Real-Time Decision Making With AI Support

Every CEO feels it — decisions can’t wait. 📉 The pressure: Strategy, investor updates, and operations now move faster than your data. When metrics live in silos, blind spots multiply and decisions slow. 🤖 How AI is changing the game: AI copilots connect systems, summarize insights, and generate real-time dashboards in plain English—turning data chaos into clarity. ⸻ 8 AI tools redefining the CEO workflow: • Mosaic — A financial planning copilot that connects your ERP, CRM, and HR data into one dynamic dashboard. It builds rolling forecasts and scenario plans automatically, letting you stress-test strategies in seconds. Mosaic helps CEOs replace static spreadsheets with continuous, forward-looking visibility. • Pigment — A collaborative FP&A platform that unifies financial, sales, and operational data. It enables real-time “what-if” modeling and board-ready reporting without Excel chaos. Pigment turns complex planning into a shared, living process for leadership teams. • Microsoft Power BI + Copilot — Microsoft’s analytics suite now includes generative AI that narrates dashboards in natural language. You can ask questions like “What’s driving revenue variance this quarter?” and get instant, visual explanations. It helps CEOs see and understand key trends across every business unit. • Notion AI — More than a workspace, Notion AI drafts meeting summaries, strategy docs, and executive notes automatically. It centralizes company knowledge, connects projects to goals, and produces clear action items. CEOs use it as their digital chief of staff for information synthesis. • ChatGPT Enterprise + Slack Integration — Combines the reasoning power of ChatGPT with real-time Slack access. It retrieves internal data, answers operational questions, and drafts communications instantly. The result: instant, secure intelligence across every department—right in your workflow. • Perplexity Pro — An AI research assistant that provides live, source-cited answers from across the web. It tracks macro trends, competitor updates, and industry moves in real time. CEOs rely on it for fast, verifiable insights when preparing for board meetings or press briefings. • Kore.ai — An AI platform that listens to voice and text interactions across your enterprise to uncover operational signals. It builds conversational analytics layers for service, HR, and customer ops. For CEOs, Kore.ai reveals friction points and efficiency opportunities hiding in daily operations. • Broadwalk .ai — A next-generation copilot that transforms unstructured data—news, filings, sentiment, and market signals—into actionable insights. It helps leaders move from data to direction, detecting early sentiment shifts across portfolios, markets, and competitors. Broadwalk equips CEOs and fund managers with clarity before the market reacts. ⸻ 💡 The best CEOs don’t wait for reports anymore — they converse with their data.

What if one of the most important medical decisions of your life came down to five rushed minutes, and incomplete data? In a recent conversation with Fidji Simo, CEO of Applications at OpenAI, she shared a moment that should give every healthcare leader, operator, and technologist pause. While hospitalized, she was about to be given a standard antibiotic for a routine infection. On the surface, it was the correct protocol. But by quickly cross-referencing the drug against her full medical history using AI, she uncovered a critical risk. It could have reactivated a serious past C. diff infection. The physician’s response was telling. “I have five minutes to make rounds. I can’t review years of records.” Modern healthcare is still operating on fragmented data, siloed specialties, and time-constrained decision-making. Even the best clinicians are forced to make high-stakes calls without full context. And this is where the opportunity becomes clear. We are entering a new era where AI is not replacing clinicians, but augmenting their ability to see the whole picture. By connecting longitudinal health data, labs, genomics, wearables, and medical history, we can move from reactive care to truly informed, real-time decision-making. In our full discussion, we explore what this shift means at scale: • Why most clinical errors are not about knowledge gaps, but missing context • How fragmented health systems create unnecessary risk and inefficiency • What it looks like when AI becomes a layer of intelligence across the entire patient journey • So much more This is a systems design problem, a data problem, and ultimately, a leadership problem. The organizations that solve for context, not just care delivery, will define the future of health. Listen to our full conversation here: https://lnkd.in/g_2FsR2q

Access to a real-time AI decision support tool during primary care visits in Nairobi cut diagnostic errors by 16% and treatment errors by 13%, with no added harm reported. 1️⃣ This study tested “AI Consult,” an LLM-powered tool integrated into EMRs at 15 Penda Health clinics in Kenya. 2️⃣ The tool ran in the background, issuing alerts only when needed (green/yellow/red), preserving clinician autonomy. 3️⃣ Across 39,849 visits, clinicians with AI support made 16% fewer diagnostic errors and 13% fewer treatment errors, as judged by blinded physician review. 4️⃣ Estimated annually, AI Consult could prevent 22,000 diagnostic and 29,000 treatment errors at Penda alone. 5️⃣ The largest error reductions were in history-taking (32% relative risk reduction) and treatment safety (NNT = 13.9). 6️⃣ Clinicians with the tool gradually made fewer mistakes even before receiving alerts, suggesting it helped build better habits. 7️⃣ All clinicians surveyed said AI Consult improved care; 75% said the improvement was “substantial.” 8️⃣ No safety events were attributed to AI Consult, and alert fatigue was mitigated through careful interface and threshold design. 9️⃣ Uptake increased after targeted deployment strategies: coaching, peer champions, and performance feedback. 🔟 The study underscores that success came not just from the model itself, but from aligning tech design with clinical workflow. ✍🏻 Robert Korom, Sarah Kiptinness, Najib Adan, Kassim Said, Catherine Ithuli, Oliver Rotich, Boniface Kimani, Irene King’ori, Stellah Kamau, Elizabeth Atemba, Muna Aden, Preston Bowman, Michael Sharman, Rebecca Soskin Hicks, MD, Rebecca Distler, Johannes H., Rahul K. Arora, Karan Singhal. AI-based Clinical Decision Support for Primary Care: A Real-World Study. 2025. DOI: 10.48550/arXiv.2407.12986

What does it actually take to deploy AI for the physical world? This isn’t AI in a sandbox. It’s AI inside physical, regulated, safety-critical systems that are already running at scale—often at the edge, where decisions must be made autonomously and in real time. You’re not predicting outcomes offline. You’re making operating decisions on machines and processes worth billions—under uncertainty, with real consequences. The AI most people think of today—LLMs trained on clean, abundant internet data—was never built for this environment. They complete sentences. They automate low-risk tasks. They assume stable data, constant connectivity, and low cost of error. AI for the physical world is fundamentally different. It must: operate with sparse, noisy, indirect data reason explicitly under uncertainty, not ignore it adapt continuously as real-world conditions change coordinate decisions across many large, tightly coupled systems run in real time, often on edge devices operate autonomously, without human-in-the-loop explain decisions well enough for engineers to trust and act on them improve operations without disrupting them This is not design-phase AI. It’s not simulation-only AI. And it’s not cloud-only AI. Most approaches stop at either simple ML models trained on sensor data, or high-fidelity models or faster simulation. That’s valuable—but it doesn’t operate the system. Geminus is built for autonomous decision-making in the loop. It is built to scale to never before seen use cases without teams of Phds and months of customizations. Our models are deployed directly into operating environments—often embedded on edge infrastructure—where they continuously assimilate data, quantify uncertainty, and optimize decisions in real time. The platform combines: physics-based models multi-source information fusion probabilistic reasoning and uncertainty quantification large-scale, multi-system optimization —so AI can run the system, not just analyze it. We just completed our first year of real-world deployments at scale—not pilots. Geminus is now operating on assets that cost tens of billions of dollars to build, delivering material performance improvements in weeks, not years. We’ve proven speed. We’ve proven scale. And we’ve proven that AI for the physical world can operate autonomously—where it actually matters. This is where the category is going. We’re already there. 2026 here we come!

Kafka + AI = Real-Time Reasoning The New Nervous System of Intelligent Apps Kafka has long been the bloodstream of digital enterprises — moving events between microservices, apps, and analytics engines with relentless speed. But until now, Kafka has only transported data. It hasn’t understood it. That changes with AI-driven stream reasoning. Today, machine learning models can subscribe to Kafka topics in real time, interpret event patterns, and trigger autonomous decisions as the data flows. This is not dashboards. This is not batch analytics. This is inline intelligence. 🚨 Fraud detection that reacts in milliseconds Models can score risk the moment a transaction appears on the stream — instantly routing anomalies to compliance and preventing losses before humans even see them. 🔧 IoT systems that predict failures before they occur Sensor data flows through Kafka → AI models infer degradation → maintenance teams get alerted before the breakdown. 💹 Financial and operational systems with zero lag The latency between detection and action collapses. Kafka streams provide continuity; AI provides context. Together, they behave like the neural network of the modern enterprise. From Event Pipelines → Cognitive Pipelines Once Kafka streams carry not just data but meaning, enterprises move from: reacting → to anticipating monitoring → to reasoning processing → to autonomous decisioning Real-time data finally gets the real-time intelligence it deserves. #Kafka #AI #StreamingData #EventDrivenArchitecture #MachineLearning #OpenSource #DataEngineering #LLM #MLOps

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗖𝗼𝘂𝗹𝗱 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗦𝘂𝗽𝗽𝗼𝗿𝘁. 𝗛𝗲𝗿𝗲'𝘀 𝗪𝗵𝗮𝘁 𝗧𝗵𝗮𝘁 𝗠𝗶𝗴𝗵𝘁 𝗟𝗼𝗼𝗸 𝗟𝗶𝗸𝗲. This week, OpenAI released a visual tool for building multi-agent workflows(1). I've been curious about agents for a while, but never had time to learn. Playing with this tool got me thinking about a longstanding challenge: how do we translate complex clinical pathways into effective CDS tools? 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝘄𝗶𝘁𝗵 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗖𝗗𝗦 Today's EHR-based decision support faces a fundamental tradeoff. Tightly scripted rules are reliable but inflexible. Loosely scripted ones give clinicians room to adapt but sacrifice consistency. Multi-agent AI workflows might offer a way out of this bind. 𝗔 𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 To explore this, I translated Children's Hospital of Philadelphia's Suicide Risk Assessment Pathway (2) into a multi-agent workflow. Here's how it works: • Input: Clinician's risk formulation, screening results, risk and protective factors • Acuity script: Determines patient acuity level • Intervention agent: Recommends response level • Response agents: Four specialized agents provide tailored clinical guidance based on severity 𝗔 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗞𝗶𝗻𝗱 𝗼𝗳 𝗖𝗗𝗦 Now imagine this in your EHR. Instead of rigid decision trees, you'd have multiple specialized LLMs that can: • Access relevant patient data • Provide contextual guidance • Answer follow-up questions in real time • Adapt to clinical nuance while maintaining evidence-based standards Some extras that make this promising are the ability to use MCP and RAG! This isn't just automation. It's augmentation that preserves clinical judgment while providing robust support.   Bimal Desai MD, MBI, FAAP, FAMIA