Algorithm-Driven Decisions

Enterprise leaders must update their 2026-2027 AI strategies. This year brings major changes: AI agents and automation are outpacing governance, sharply increasing risk. "Sticking an AI on it" is insufficient; leaders must redesign how we augment human decision making (humans-in-the-loop) and automate at scale (human-on-the-loop). Governance practices and platforms are essential to avoid costly mistakes. Gartner predicts the by 2027, 25% of ungoverned decisions using large language models (LLMs) will cause financial or reputational loss due to human biases, insufficient critical thinking, and AI sycophancy. This stems from users' over-trusting confident-sounding LLM outputs. Leaders must govern decisions more carefully, as automation often scales the risks just as fast as it scales the gains! Most clients I speak with still focus on human decision makers being “data‑driven” by dashboards, analytics, and data, etc. However, this fails to overcome human biases, does not prevent "AI sycophancy," nor does it make major decisions transparent and accountable (the black box problem). As #AIAgents increasingly automate part of our businesses, the data-driven dogma (dashboard watching humans) really breaks down. Gartner research shows clients evolving from “data‑driven” to “decision‑centric,” where the business decision is modeled, monitored, and governed - that is why we are hearing much more about decision intelligence in 2026. The Magic Quadrant for Decision Intelligence Platforms offers leaders three key benefits: 1️⃣ Clarity on essential technical capabilities like decision modeling, monitoring, and governance. 2️⃣ A framework for vendor evaluation based on combining AI agents, data, analytics, ML, knowledge graphs, and context for strategic and operational decisions. 3️⃣ Evidence that a decision-centric approaches deliver results; explicitly modeled decisions will be five times more trusted and 80% faster than ungoverned ones. For instance, a client (major bank) leveraged this research to secure their budget, adopt a decision-centric vision, transform a large team into a DI division, and select a platform for governing regulated decisions - boosting their influence and providing a safer path to scale AI. Using LLMs for decision making without governance is an enterprise risk. Becoming decision-centric is the safest way to connect AI to enterprise data. Q. Are you still data-driven, or adopting a #DecisionCentric vision to govern AI-enabled decisions? If "data-driven" is where you are at, this Magic Quadrant shows how connecting data-to-decisions explains the deeper value of data. If you're already exploring #DecisionIntelligence, then let's explore it together. Which capabilities and platforms are on your 2026 roadmap. Now you know why I say that in 2026, “D is for Decisions”. Clients are reading Gartner's new Magic Quadrant for Decision Intelligence Platforms 🔗 https://lnkd.in/eMq4gynh (requires log in)

After reviewing dozens of enterprise AI initiatives, I've identified a pattern: the gap between transformational success and expensive disappointment often comes down to how CEOs engage with their technology leadership. Here are five essential questions to ask: 𝟭. 𝗪𝗵𝗮𝘁 𝘂𝗻𝗶𝗾𝘂𝗲 𝗱𝗮𝘁𝗮 𝗮𝘀𝘀𝗲𝘁𝘀 𝗴𝗶𝘃𝗲 𝘂𝘀 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿𝘀 𝗰𝗮𝗻'𝘁 𝗲𝗮𝘀𝗶𝗹𝘆 𝗿𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗲? Strong organizations identify specific proprietary data sets with clear competitive moats. One retail company outperformed competitors 3:1 only because it had systematically captured customer interaction data its competitors couldn't access. 𝟮. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗼𝘂𝗿 𝗰𝗼𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗷𝘂𝘀𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀? Look for specific examples of fundamentally reimagined business processes built for algorithmic scale. Be cautious of responses focusing exclusively on efficiency improvements to existing processes. The market leaders in AI-driven healthcare don't just predict patient outcomes faster, they've architected entirely new care delivery models impossible without AI. 𝟯. 𝗪𝗵𝗮𝘁'𝘀 𝗼𝘂𝗿 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝗻𝗴 𝘄𝗵𝗶𝗰𝗵 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗿𝗲𝗺𝗮𝗶𝗻 𝗵𝘂𝗺𝗮𝗻-𝗱𝗿𝗶𝘃𝗲𝗻 𝘃𝗲𝗿𝘀𝘂𝘀 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰𝗮𝗹𝗹𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱? Expect a clear decision framework with concrete examples. Be wary of binary "all human" or "all algorithm" approaches, or inability to articulate a coherent model. Organizations with sophisticated human-AI frameworks are achieving 2-3x higher ROI on AI investments compared to those applying technology without this clarity. 𝟰. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗲𝘁𝗿𝗶𝗰𝘀? The best responses link AI initiatives to market-facing metrics like share gain, customer LTV, and price realization. Avoid focusing exclusively on cost reduction or internal efficiency. Competitive separation occurs when organizations measure algorithms' impact on defensive moats and market expansion. 𝟱. 𝗪𝗵𝗮𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗵𝗮𝘃𝗲 𝘄𝗲 𝗺𝗮𝗱𝗲 𝘁𝗼 𝗼𝘂𝗿 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗼 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘃𝗮𝗹𝘂𝗲 𝗼𝗳 𝗔𝗜 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀? Look for specific organizational changes designed to accelerate algorithm-enhanced decisions. Be skeptical of AI contained within traditional technology organizations with standard governance. These questions have helped executive teams identify critical gaps and realign their approach before investing millions in the wrong direction. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: V𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 own 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴.

73% of C-suite decisions now involve AI input. Yet only 31% of executives fully understand the algorithms they rely on. The WEF AI in Action report backs up what I've been seeing with my clients that AI has moved from doing basic office tasks to helping make big company decisions. The shift is happening faster than most realize. Leadership teams are now using AI to shape strategic direction in their company: → Market analysis that spots patterns humans miss → Risk assessments that calculate factors beyond human capacity → Revenue forecasts with surprising accuracy But this creates tensions I witness regularly in executive meetings: The CFO trusts the 15-year industry veteran but the AI suggests a contradictory investment strategy. The CMO presents a campaign based on decades of expertise, but the AI proposes targeting an entirely different demographic. This new reality forces us to ask uncomfortable questions: ↳ When should human judgment override AI recommendations? ↳ How do you justify AI-influenced decisions to stakeholders? ↳ At what point does AI cross from being a tool to a decision-maker? The companies getting this right implement a structured "AI+Human" framework: 👉 They require AI systems to explain recommendations in plain language  👉 They establish clear override protocols to balance the AI’s expertise with human experience  👉 They track decision outcomes to improve both human and AI performance over time. I've seen this approach cut failed initiatives by 37% while speeding up decision cycles by half. ☑️Have you experienced this tension between AI recommendations and human judgment in your leadership team? ♻️Repost and follow Dev Mitra 🇨🇦 for more actionable content. #ExecutiveLeadership #AIStrategy

What Machine Learning Can—and Cannot—Do for Policymaking Artificial intelligence is increasingly part of policy debates. Within that broad umbrella, machine learning is the workhorse. Machine learning is a subset of AI focused on one task: learning patterns from data to make predictions. Instead of relying on a fixed economic model, algorithms let the data guide the prediction—especially when datasets are large, complex, and high-dimensional. That is exactly why machine learning has become so attractive for governments. It can help answer practical questions like: Which firms are most likely to default? Which households are vulnerable? Where are risks building up? In these cases, better prediction can materially improve targeting and resource allocation. But here is the distinction the economics literature keeps emphasizing: prediction is not policy. Machine learning—by design—looks backward to predict forward. It is good at telling us what is likely to happen given past patterns. It is far less reliable, on its own, at answering what will happen if we change policy. Most policy questions are causal. They are about interventions, incentives, and behavior. Algorithms trained to recognize patterns can easily confuse correlation with cause—producing results that look precise, data-driven, and objective, yet are misleading for decision-making. What I appreciate in the work of Susan Athey and Guido Imbens is its balance. It places machine learning firmly within AI’s promise, but insists it be disciplined by economics—by causal thinking, institutional context, and clear policy objectives—rather than used as an off-the-shelf solution. A simple way to think about it: Targeting and forecasting? Machine learning (within AI) shines. Policy design and evaluation? Economics still has to lead. Machine learning strengthens policymaking when used in the right place. It doesn’t replace judgment. The real question is: are we using AI to support policy decisions—or slowly outsourcing them to algorithms? https://lnkd.in/eqzZWx2g #ArtificialIntelligence #MachineLearning #PublicPolicy #EconomicPolicy #DataForPolicy #CausalInference #AIandPolicy

AI doesn’t replace human judgment, it sharpens it. Organizations everywhere are turning to AI-driven decision support systems. The promise is big: faster analysis, fewer biases, better outcomes. Some teams even report working 30% faster with AI by their side. However, machines don’t understand context, causality, or accountability. Left unchecked, they create blind spots: ✓Data quality issues skew decisions. ✓Correlation, not causation limits true insight. ✓Automation bias makes people trust AI too much or not enough. The real advantage emerges when AI augments, not replaces, human expertise. AI excels at pattern recognition and objective analysis. Humans bring intuition, ethics, and contextual judgment. Together, they make decisions both faster and wiser. That’s why governance, transparency, and trust calibration are no longer “nice-to-haves.” They’re the backbone of responsible decision systems. Without them, efficiency gains come at the cost of accountability. 👉 The future of decision-making isn’t about choosing between people and machines. It’s about building symbiotic systems that harness the strengths of both. 📩 In Issue #11 of Meaningful AI, I break down the challenges, governance frameworks, and psychological factors shaping human-AI collaboration in decision-making. Do you think organizations today trust AI too much or not enough?

Sitting with CTOs from 16 major lenders last week, I asked one question: "How well does your LOS handle complex decisioning?" Average score: Below 7. Not because their systems are broken. But because loan origination systems were never built to be decision engines. Here's what Rafi Goldberg from Sapiens explained on the Power House podcast that changed my perspective: AI decisioning isn't about replacing your underwriters. It's about competing on decisions. Think about what actually differentiates your lending: • That 20-year underwriter who knows when to make exceptions • The processor who catches patterns others miss • The branch manager with instincts you can't explain That institutional knowledge is your competitive advantage. Except it's trapped. The technical challenge isn't automation—it's translation. How do you convert decades of human pattern recognition into decision logic that scales? This is where the architecture matters: Traditional business rules approaches fail over time. They become brittle and inflexible, an albatross of technical debt unable to meet business needs. AI decisioning changes that paradigm. Combining declarative decision models with analytics and AI, your experts’ decision can now be converted to business assets at scale, with no loss in business intent and all the observability and adaptability you’ve come to need and expect. One CTO today said it perfectly: "Our LOS manages transactions. But our decisions happen in Excel sheets and email chains." That's the gap. While everyone races to perfect their point-of-sale experience, the real differentiator is decision velocity and precision. Your best people make hundreds of micro-decisions daily. Each one based on experience you can't hire off the street. When they retire, that knowledge disappears. Unless you capture it now. The mortgage industry keeps focusing on the wrong automation. We digitize applications. We automate verifications. We streamline workflows. But decisions? Those still happen in silos. What if your junior underwriter could access your senior team's pattern recognition? What if every loan officer could tap into your best performer's instincts? That's not replacing human judgment. It's amplifying it. The lenders who win the next decade won't have the slickest UI or the fastest application. They'll be the ones who turned their tribal knowledge into scalable, intelligent decision engines. Every lender in that room today knew their LOS wasn't built for this. The question is: Who's going to fix it first?

“Can we govern it—safely, transparently, and fairly—at scale?” Discovering how to shift black boxes to glass boxes: In a new open-access BMJ Health & Care Informatics peer-reviewed research article, our team details a method to discover the reasoning pathways of reinforcement learning (RL) AI algorithms used for decision support in Medicaid population health care management services across Washington, Virginia, and Ohio (July 2023–June 2025)—and build practical guardrails for deployment. What we did (in plain English): We treated the RL system like an aircraft control system: not just “does it perform,” but why does it recommend what it recommends, and when should humans override it? We ran a retrospective interpretability audit on 250,000 intervention decisions, including blinded clinician review of “divergent” cases. We implemented a conformal safety envelope to separate decisions that can be “algorithmically cleared” from those that must be flagged for review. We built an error taxonomy and added fairness-aware constraints to reduce subgroup disparities. Key results that matter operationally: The calibrated harm model achieved AUROC 0.80 and cleared ~89.5% of decisions. Mechanistic interpretability surfaced seven clinically coherent “reasoning motifs”—linking social determinants to clinical cascades (e.g., housing–respiratory exacerbation; food insecurity–diabetes control; transportation–specialty access). In divergent cases, the dominant failure modes weren’t “mysterious AI logic”—they were actionable: -Premise errors (48%): missing/incorrect facts in the data available at decision time -Calibration failures (27%) -Contextual blind spots (25%) Divergence was higher in telehealth and among behavioral health patients— where you’d want more structured oversight. Fairness optimization reduced race-group disparity by ~37% and sex-group disparity by ~28%, with essentially stable policy value. Why this matters for health systems & plans: This is a concrete blueprint for moving from “AI as a black box” to AI as auditable decision support: A tiered oversight model (clear low-risk decisions; escalate higher-risk ones) An operational error taxonomy teams can use for QA, training, and data improvement Fairness metrics with confidence intervals—not just aspirational equity language A governance scaffold aligned with where regulation is heading: transparency, safety envelopes, and monitored residual risk—not just accuracy. https://lnkd.in/gV8qhuUh Josh Patten Todd Schwarzinger Ali Khan, MD, MPP Alice Hm Chen Anand Shah Ruben Amarasingham, MD Pat Lee, MD Ashley Thurow Lucas Hopkins S. Monica Soni, MD Ben Rogers Daniel Brillman Valerie Rohrbach Lehman Asaf Bitton Dr. Kedar Mate Robert Wachter Alejandro Schuler Nigam Shah Ethan Goh, MD James Zou Paulius Mui, MD Bob Phillips, MD MSPH Russ Phillips Bruce E. Landon Ishani Ganguli Mitzi Hail Hochheiser Aneesh Chopra Kate McEvoy Vineeta Agarwala, MD PhD Hui Cheng Deena Shakir

Is your AI doing what everyone thinks it's doing? Most organizations don't know what their AI systems actually optimize for. They know what they paid for, what the vendor promised, what the dashboard shows. But ask them to trace a specific decision through its real-world impact, and watch the confidence evaporate. This isn't a technical problem—it's a strategic blind spot that can create massive risk. AI systems promise precision but can just as easily deliver surprises. A recommendation engine boosts engagement while customers grow frustrated. A screening algorithm speeds processing while missing valuable opportunities. A pricing model maximizes short-term revenue while competitors gain ground. AI systems optimize for what they're measured on, which is not necessarily what businesses need. Too often, standard AI validation and AI governance focus on whether systems work as programmed, not whether they work as intended. This creates a dangerous gap between algorithmic behavior and business value. Technical teams understand how algorithms work but miss market implications. Business leaders grasp competitive dynamics but struggle with algorithmic complexity. Legal experts navigate regulations without understanding technical constraints. When these gaps persist, problems multiply. Legal teams worry about liability from decisions they can't explain. Business leaders face reputational damage from systems that technically perform but practically harm. Operations teams field complaints about decisions that follow perfect logic but defy human reasoning. Organizations getting this right treat AI deployment as ongoing optimization, not one-time implementation. They ask whether specifications serve strategic objectives, not just whether systems meet them. They monitor for drift between intended and actual outcomes and adapt quickly when unintended consequences emerge. The promise of AI is objectivity. The reality is human assumptions automated at scale—with compound interest. Every training decision, every incentive, every gap between specification and intention gets amplified thousands of times per day. Small deviations can become systematic issues. Subtle misalignments can become strategic headaches. This compounding effect makes AI governance fundamentally different from traditional technology management. A poorly configured database affects the queries it handles. A misaligned AI system affects every decision it touches, every customer it evaluates, every opportunity it surfaces or buries. The winners won't just build systems that work—they'll master the ongoing challenge of ensuring those systems work as intended. In a world where algorithms increasingly drive business outcomes, that distinction will determine who thrives and who gets blindsided by their own automation.

AI is creating a once-in-a-generation opportunity to scale professional judgment. Agentic reasoning is already transforming how work gets done — supporting human judgment directly and delivering accelerating efficiency gains. At the same time, without proper governance, organizations are already losing the ability to explain and defend decisions as AI becomes embedded in decision-making. Governance, accountability, and trust are becoming more fragile. For decades, enterprise software has been built around workflows — tasks, handoffs, and approvals. That model assumed humans did the thinking and systems tracked execution. AI breaks that assumption. As AI becomes embedded in workflows, judgment is no longer formed solely through human reasoning. It is supported by models, prompts, context, and agentic reasoning that most enterprise systems were never designed to govern. Optimizing execution alone is no longer sufficient. What matters now is whether the system in which work happens is built to make decisions defensible. This is a broader, cross-industry shift. As AI-assisted reasoning becomes widely accessible, value increasingly derives from systems that make it possible to understand how decisions are made, who is authorized to make them, and how those decisions can be reviewed and defended. This shift becomes visible earlier in regulated environments. When decisions must withstand scrutiny — from clients, regulators, or courts — the gap becomes immediately apparent. We need a better abstraction for this than “workflow.” A decision environment is a governed system in which decisions are defensible. It is where context is preserved, standards are embedded as executable logic, authority is explicit, and AI participates within defined boundaries. Decisions formed in such an environment are not just efficient — they are reviewable, explainable, and supportable. A workflow determines what happens next. A decision environment defines the appropriate governance framework and guardrails of a decision, who can make the decision, and when — all built on the foundation of a robust audit trail. Data can be exported easily. Decision legitimacy cannot. When decisions are formed outside the system where standards, authority, and review are enforced, decision quality, traceability, and defensibility degrade.  In regulated professions, AI’s long-term value is most reliably realized in systems that embed methodology and effectively support judgment. This does not preclude experimentation with AI elsewhere. That experimentation is essential. But decisions that carry real risk, accountability, or regulatory exposure still need a place where they can be governed, reviewed, and defended. This is the future we’re building toward — delivering the global, AI-powered platform of choice for accounting professionals, unlocking a once-in-a-generation opportunity to scale professional judgment.