Tag: agentic systems
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The Intelligence Paradox
Most enterprises have more intelligence embedded in their operations than ever before. They have better forecasts, faster analytics, more automation, and increasingly autonomous systems. Decisions are informed by models that are more accurate, more adaptive, and more responsive than anything that came before. Yet many organizations report that operating the business feels harder, not easier.…
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Beyond the ‘AI-Centric Imperative’: The Orchestration-Native Enterprise
← From AI-Centric to Orchestration-Native | PART 8 / 8 If you’ve stayed with this series, you’ve traveled a fair distance: Let’s end by zooming out. There’s a growing consensus – from McKinsey and others – that the next era of software will be AI-centric: agents, copilots, and embedded intelligence reshaping products and workflows. That’s true,…
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Proof Under Pressure: How Orchestration Handles Real-World Disruption
← From AI-Centric to Orchestration-Native | PART 7 / 8 It’s one thing to talk about architecture, metrics, and roadmaps in calm conditions. It’s another thing to ask a harder, more honest question: What happens when the world really goes sideways? That’s the real test of any ‘AI-centric’ or ‘orchestration-native’ vision. In this article, we’ll…
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What Changes When You Become Orchestration-Native
← From AI-Centric to Orchestration-Native | PART 6 / 8 Up to this point, we’ve stayed mostly in the world of architecture, economics, and metrics: But technology is only half the story. The real test of an orchestration-native enterprise is what it does to the day-to-day reality of work: This article is about that human side:…
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Adoption Without Disruption: A Practical Path to Orchestration
← From AI-Centric to Orchestration-Native | PART 5 / 8 By now, we’ve painted a pretty ambitious picture: If you’re an executive or transformation leader, you might be nodding along – and still thinking: “This all sounds great. But how do we actually get there without a giant, risky transformation project?” That concern is rational.…
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From Dashboards to Decisions: New Metrics for an Orchestrated Enterprise
← From AI-Centric to Orchestration-Native | PART 4 / 8 If you’ve been following this series, we’ve made three moves so far: That naturally raises a harder question: If we change how decisions are made, how should we measure whether the system is actually getting better? Most enterprises are still instrumented for an older era…
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The Economics of Intelligence: How to Stop AI from Becoming a Cost Spike
← From AI-Centric to Orchestration-Native | PART 3 / 8 In the first two articles, we made a simple argument: That all sounds promising. But if you’re a CFO, COO, or P&L owner, there’s another question sitting underneath all of this: Will this actually make us more efficient – or just more expensive? Recent analysis…
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What an Orchestration-Native System Actually Looks Like (in Plain Language)
← From AI-Centric to Orchestration-Native | PART 2 / 8 In the first article, we argued that “AI-centric” is only a half-step. Adding copilots and assistants into individual tools is helpful, but it doesn’t solve the hardest problem large enterprises face: coordination. Sales, supply, logistics, finance, and operations all get “smarter” – but who keeps them…
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Autonomy Is Not the Breakthrough… Accountability Is
The HBR article “When Supply Chains Become Autonomous” captures an important inflection point: reasoning-capable AI agents can now outperform humans in tightly scoped, simulated supply chain environments. The central question is no longer whether agents can make decisions, but whether organizations can prove – across time, scenarios, and constraints – that those decisions are safe, consistent, and aligned…
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When Supply Chains Become Autonomous (HBR)
Generative AI models, particularly those with advanced reasoning capabilities, can autonomously manage supply chains, outperforming human teams in simulations like the MIT Beer Distribution Game. These models can learn, adapt, and make cross-functional decisions with minimal human oversight, reducing supply chain costs by up to 67%. Key factors for success include selecting capable models, implementing…