Leveraging AI for Decision Support

Effective AI augmentation of human decision-making requires clarity on the specific role of AI relative to humans. An interesting research study used two different AI agents - ExtendAI and RecommendAI - each optimized to play different roles in a financial investment decision process. The findings give useful insight into both the design of AI tools to augment human decisions, and how we deliberately choose to use AI to enhance our decision competence. 🧠 ExtendAI encourages self-reflection and informed decisions. Participants who used ExtendAI—an assistant that builds on users' own rationales—spent more time reflecting and revising their plans. They made 23.1% of trades that diverged from their original ideas, showing that feedback embedded in their own reasoning helped identify blind spots and improve diversification and balance. ⚡ RecommendAI sparks new ideas with low effort. RecommendAI, which directly suggests actions, led to a 45% adoption rate of its recommendations. It was perceived as more insightful (67% vs. 52% for ExtendAI) and easier to use, requiring half the time (8.6 vs. 17.5 minutes) compared to ExtendAI. 🧩 Feedback format impacts trust and comprehension. ExtendAI’s suggestions, interwoven into the user's rationale, were found easier to verify and interpret. Participants felt more in control (76% vs. 71% trust) and reported that it “supports how I’m thinking” instead of dictating actions. RecommendAI, by contrast, sometimes felt like a “black box” with unclear reasoning. 🌀 Cognitive load differs by interaction style. Using ExtendAI imposed more cognitive effort—an average NASA-TLX score of 57 vs. 52.5 for RecommendAI—due to the need for upfront reasoning and engagement with nuanced feedback. This reflects the trade-off between deeper reflection and ease of use. 💡 Users want AI insights to be both novel and relatable. Participants valued fresh insights but were most receptive when suggestions aligned with their reasoning. ExtendAI sometimes felt too similar to user input, while RecommendAI occasionally suggested strategies users rejected due to perceived misalignment with their views or market context. 🧭 Decision satisfaction and confidence diverge. Despite feeling more confident with RecommendAI (86% vs. 67%), participants reported higher satisfaction after using ExtendAI (67% vs. 43%). This suggests that while direct suggestions boost confidence, embedded feedback might lead to decisions users feel better about in hindsight. More coming on AI augmented decision making.

One reason AI initiatives stall? Few execs use AI in their own work. In 3 hours, I take leaders from “I don’t know” to a POV (co-developed with AI!) on how AI can support key strategic initiatives. To crack the code on exec adoption we: >> Focus on Strategic Use Cases that Click with Execs << To get experience with high value use of AI, we dive into cases that directly enhance executive decision-making and strategic thinking. This tends to be a major eye-opener—most leaders don't realize AI can elevate their highest-level work. Once executives experience immediate personal value, they better understand how AI can have immediate impact across the organization. >> Reframe Mental Models << Generative AI operates fundamentally differently from anything we've seen before, so we need to identify why and how digital change playbooks must shift to leverage this moment. I go straight to the heart of the silent organizational barriers that prevent productive adoption, and how to navigate a path forward. >> Start with the Business, Not the Tech << We don’t begin with AI—we begin with your business. We anchor the process with the breakthroughs that will drive real impact—and to get there, we go analog with brainstorming, whiteboards, and post-its, working to envision what advancement could look like. What could be possible if cognitive limits were lifted? What long-standing friction could finally be overcome? This surfaces a library of meaningful, business-driven opportunities. Then, using proven filters and frameworks, we zero in on the highest-impact places to start applying AI. >> Use AI to Develop AI Strategy << We then—on the spot—collaborate with AI to develop executive viewpoints on how AI can accelerate those strategic priorities. This is hands-on work with AI tools to co-create a path forward, often culminating in each group sharing a lightning talk (co-developed with AI) with the broader team. This approach fast tracks execs to: 1️⃣ Build readiness: Gain deep understanding of the new landscape of use cases today’s AI offers, and the organizational structures needed to effectively harness it. 2️⃣ Map use cases: Develop a prioritized library of strategic use cases ready for immediate collaboration with technology and data teams. 3️⃣ Accelerate alignment: Establish common language and jump-start cross-functional alignment on tackling high-impact opportunities. 4️⃣ Hands-on understanding: Acquire hands-on experience with AI tools they can immediately apply to their most challenging strategic work. What do my clients say about this approach? That their teams shift from skepticism to enthusiasm—hungry for more, and from uncertainty to clarity about the next steps. It’s a remarkable change, especially in a few hours. ➡️ Want to learn more? Let’s talk. #AIworkshop

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

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.

What if AI's most profound gift to RevOps isn't efficiency but truth? I recently observed something unexpected while advising a growth-stage SaaS company. Their new AI forecasting system certainly accelerated processes, but its most valuable contribution was revealing data patterns that had silently distorted their revenue projections for years. The RevOps leader—brilliant and meticulous—sat with me reviewing the findings, his expression shifting from confusion to clarity. "We've been making decisions on a foundation of invisible assumptions," he said quietly. This moment crystallized something I've been contemplating about AI's role in revenue intelligence. Beyond the automation headlines lies a more nuanced revolution: AI as an instrument of unprecedented transparency. For executives navigating today's landscape, this represents a profound strategic pivot. The question isn't simply how to deploy AI for efficiency but how to harness its capacity to illuminate blind spots in our revenue operations—blind spots humans naturally create through our biases and limitations. If you're considering this approach, here are three practical steps I've seen work: - First, use AI to audit your existing data before building new models. The most successful teams spend 4-6 weeks solely on uncovering hidden patterns and inconsistencies. - Second, create a "truth-seeking" review process where AI findings are regularly examined by cross-functional leadership. This prevents defensive reactions and transforms revelations into opportunities. - Third, measure not just efficiency gains, but "clarity metrics"—counting the number of previously unknown insights discovered and subsequently addressed. Looking ahead 12-18 months, I see a divergence forming. Organizations using AI merely to accelerate existing processes will capture incremental gains. But those using AI as a lens for deeper organizational self-awareness will achieve something more valuable: decision-making rooted in reality rather than comfortable illusions. I'm curious about your experiences. What unexpected insights has AI revealed in your revenue operations that human analysis missed? Has this changed your approach to strategic decision-making? The wisdom in this community consistently amazes me—I look forward to learning from your perspectives.

🌐 Data Literacy is Table Stakes. Decision Literacy will be the Superpower of the next decade. In an age of information overload, knowing how to read data isn’t enough. The real differentiator? Decision Literacy - the ability to turn insights into bold, strategic action. While data literacy helps you read, understand, and analyze, decision literacy goes beyond: 🔹 Synthesizing insights 🔹 Weighing trade-offs 🔹 Acting confidently in complexity and uncertainty As AI, automation, and acceleration reshape industries, decision literacy is becoming the critical skill for leaders, teams, and organizations to stay competitive. How to Build AI-Powered Decision Literacy Muscle? ✅ Ask smarter, AI-augmented questions: Leverage AI to surface hidden patterns, but always challenge the "why" behind the insights. ✅ Think in human-AI context: Blend machine-generated insights with human judgment, experience, and situational awareness. ✅ Act boldly with AI support: Use AI to simulate scenarios and forecast outcomes, but own the decision. AI is your co-pilot, not the pilot. ✅ Iterate faster, learn smarter: Build continuous decision loops where AI feeds real-time feedback, enabling faster pivots and smarter adjustments. The future belongs to those who turn data into decisions that create real-world impact. Are you building this superpower? #DecisionIntelligence #Leadership #DataDriven #FutureOfWork #AI

AI isn’t replacing leaders — it’s refining them. Over the past few months, I’ve been deeply immersed in how AI can serve as both a thought partner and an operational tool in leadership, strategy, and decision-making. Here are a few insights we explored recently in a hands-on session: ✅ AI as a strategic partner – From running multiple solution scenarios to simulating advisory personas (think: conservative board member vs. aggressive operator), AI gave us new dimensions of perspective. ✅ Prompt engineering is a leadership skill – We experimented with “master prompts,” collaborative prompt libraries, and cheat sheets to make AI more actionable for strategic planning and execution. ✅ Productivity, efficiency, and innovation – AI can enhance all three. Whether breaking down a “double revenue in 24 months” goal or preparing a business case, AI enabled smarter, faster iteration. ✅ Collaboration + personalization – We tested how AI could tailor outputs to different personality types, learning styles, and even team roles. The possibilities are wide—and widening. ✅ It’s not about the tool. It’s about the model – The most successful adoption came when leaders actively modeled use of AI, not just endorsed it. Most powerful insight? AI is the great equalizer. What used to require an army of analysts is now available to a solo operator with the right mindset and well-crafted prompts. If you’re experimenting with AI in your business or leadership journey, I’d love to hear what’s working (or not) for you. Let’s build this future — thoughtfully, together. #AILeadership #PromptEngineering #StrategicThinking #FutureOfWork #DecisionMaking #ArtificialIntelligence #ProductivityTools

I believe disruption isn’t a threat. It’s a signal. A catalyst. With the right intelligence layer, the right tools, and a culture of continuous reinvention, we’re not just navigating volatility. Predict Disruption. Fuel Growth. In the logistics industry, we operate in a world where disruption is constant. Geopolitical instability, climate volatility, and economic uncertainty can cripple operations overnight. Traditional playbooks can’t keep up. But what if, instead of reacting to volatility, we could anticipate it—and use that foresight to drive growth? We’re entering a new phase in supply chain leadership: one defined by intelligent orchestration powered by generative AI, cloud-native infrastructure, and real-time data. This isn’t theoretical. It’s already reshaping how the most forward-thinking organizations operate—and we intend to lead from the front. From Reactive to Predictive: Enabling AI Decision Support In the Supply Chain industry, we’re leveraging generative AI not just to answer questions but to inform decisions. AI copilots are helping our teams process vast volumes of structured and unstructured data in real time, surfacing high-value insights from across our network. Need to know which supplier is driving delays? What external risk—weather, macroeconomics, labor, transport—is most likely to impact a lane or warehouse? AI assistants can pull those signals instantly and suggest next-best actions. This is how we reduce cycle time from insight to execution. Operational Intelligence at Scale Our strategy goes beyond dashboards. We’re embedding gen AI directly into our operational layer. These AI agents don’t just observe—they act. They automate routine workflows, flag anomalies, and suggest process redesigns based on transaction history, past outcomes, and evolving KPIs. This creates a self-optimizing loop—one where supply chain intelligence is continuous, and workflows dynamically adjust to changing realities on the ground. Simulating the Future, Not Just Reporting the Past Through virtual modeling and digital twins, we can simulate scenarios before they occur. Picture this: real-time data flowing in from drones, robotics, IoT, and WMS systems, visualized across a geo-aware orchestration layer. We can watch disruptions unfold in real time—or simulate future disruptions and test mitigation strategies in advance. This capability is invaluable not just for fulfillment accuracy but also for product lifecycle visibility, waste reduction, and meeting sustainability targets. GXO isn’t just optimizing for today—we’re engineering the supply chain of tomorrow. Putting Disruption to Work So what do we do with this capability? We operationalize it. We define what success looks like (not vanity metrics—true operational impact). We identify friction points between analysis and action. We evaluate architectural gaps continuously. We align AI-powered supply chain transformation with commercial outcomes & customer expectations.

𝗘𝗺𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗔𝗜 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗚𝗿𝗼𝘄𝘁𝗵 📈 AI and data analytics are not just tools—they're strategic assets that bridge the gap between uncertainty and opportunity. Leveraging these technologies isn't just about crunching numbers; it's about uncovering patterns, predicting outcomes, and gaining valuable insights that empower organizations to make more informed decisions. 🎯 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗜𝗻𝗳𝗼𝗿𝗺𝗲𝗱 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝗔𝗻𝘁𝗶𝗰𝗶𝗽𝗮𝘁𝗲 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 AI's predictive capabilities allow businesses to forecast trends and disruptions, enabling proactive strategies that adapt to market changes before they occur. This approach transforms decision-making from reactive problem-solving to forward-thinking innovation. 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 Data analytics streamlines processes by providing real-time, actionable insights. This speed ensures that organizations make informed choices quickly, reducing delays. 𝗨𝗻𝗰𝗼𝘃𝗲𝗿 𝗛𝗶𝗱𝗱𝗲𝗻 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 Advanced analytics help detect patterns and insights that may not be immediately apparent to the human eye. This enables businesses to identify new markets, refine product offerings, or optimize pricing strategies. 𝗗𝗲𝗲𝗽𝗲𝗻 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 AI-powered analytics enable a 360-degree view of customer behaviors, preferences, and pain points. This understanding allows for hyper-personalized strategies, enhancing customer satisfaction and loyalty. 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 Data-driven optimization of workflows, resource allocation, and supply chain management eliminates inefficiencies, reducing costs and improving productivity. 𝗙𝗼𝘀𝘁𝗲𝗿 𝗮 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 True transformation happens when teams at every level integrate AI and data analytics into their decision-making processes. This requires training, collaboration, and transparency to empower employees and ensure alignment. 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗘𝘁𝗵𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 As AI continues to influence strategic choices, companies must prioritize ethical considerations like data privacy and transparency. Responsible AI adoption fosters trust while driving innovation. #DecisionMaking #Leadership #AI #DataAnalytics #BusinessGrowth