Tag: governance

  • Beyond the ‘AI-Centric Imperative’: The Orchestration-Native Enterprise

    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,…

  • Proof Under Pressure: How Orchestration Handles Real-World Disruption

    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…

  • What Changes When You Become Orchestration-Native

    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:…

  • Adoption Without Disruption: A Practical Path to Orchestration

    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.…

  • From Dashboards to Decisions: New Metrics for an Orchestrated Enterprise

    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…

  • The Economics of Intelligence: How to Stop AI from Becoming a Cost Spike

    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…

  • What an Orchestration-Native System Actually Looks Like (in Plain Language)

    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…

  • Beyond the ‘AI-Centric Imperative’: Why the Next Frontier Is Orchestration

    Beyond the ‘AI-Centric Imperative’: Why the Next Frontier Is Orchestration

    ← From AI-Centric to Orchestration-Native | PART 1 / 8 If you work in enterprise software or supply chain, you’ve probably read some version of the same message by now: AI agents are coming. Products, pricing, go-to-market, operations, infrastructure, and talent will all have to adapt. McKinsey’s recent “AI-centric imperative” paper captures that moment well. It…

  • i5 Series: From AI-Centric to Orchestration-Native

    i5 Series: From AI-Centric to Orchestration-Native

    Many enterprises are now chasing an AI-centric imperative1: copilots in tools, agents in workflows, intelligence embedded everywhere. That’s progress – but it doesn’t solve the hardest problem large organizations face: Some parts may be getting smarter. The overall system is not. Sales, supply, logistics, finance and operations can each have their own AI. Without a coordination…

  • The Trust Deficit in Enterprise AI

    The Trust Deficit in Enterprise AI

    Enterprises are accelerating their adoption of AI, but face a widening trust deficit. While models grow more powerful, most systems still operate as opaque black boxes – difficult to govern, risky to procure, and vulnerable to regulatory scrutiny. Procurement teams demand documentation, regulators require oversight, and boards insist on explainability. Yet current remedies – explainable AI,…