AI's Winners Pull 3.8x Ahead (While 95% Still Fail)

⏱️ Your Morning Brief (TL;DR)

Welcome back!

This week, we're diving into McKinsey's latest research on how operations leaders are pulling ahead and how Microsoft’s VP of AI product development is evolving with AI. , and how marketing teams are shifting from prompt engineers to AI orchestrators.

From BCG's latest on B2B software economics to EY's eye-opening research on AI-first organizations, we're unpacking what matters for your bottom line.

Let’s dive in 🤖

🧠 Microsoft's VP AI on the Death of Org Charts

The GPTLDR

Asha Sharma, Microsoft's VP of AI Platform overseeing 80,000+ enterprise customers, dropped a masterclass on how AI fundamentally rewires product development, team structures, and strategic planning. Her key insight? Products are no longer static artifacts—they're living organisms that learn and evolve. And your org chart? It's about to become irrelevant as agents outnumber employees by 2026.

The Details

• The Living Product Revolution Forget shipping features and calling it done. Sharma reveals that Microsoft's 15,000+ enterprise customers are shifting their primary KPI from "features shipped" to what she calls the "metabolism of a product team"—its ability to continuously digest data, refine reward models, and generate outcomes. The new competitive advantage? Post-training existing models with your proprietary data rather than building from scratch. It's economically smarter and gives you the steering wheel on model behavior.

• The Polymath Renaissance Traditional role boundaries are dissolving faster than sugar in hot coffee. Sharma's seeing a resurgence of "full-stack builders" who can iterate across functions at breakneck speed. The mantra? "Focus on the loop, not the lane." Whether you're a PM, engineer, or designer, you better be obsessed with efficiency metrics, reward system design, and UX—because those functional silos are already obsolete.

• From Org Charts to Task Networks Perhaps the most mind-bending shift: hierarchical reporting structures are morphing into task-based opportunity networks. With capable agents handling execution, humans become the strategists deciding what AI should work on and how it should be applied. Employees won't manage teams—they'll orchestrate "agent stacks" that exponentially multiply their output.

• "Seasons" > Roadmaps Throw away your 6-month roadmaps. Microsoft now operates in "seasons"—3-12 month periods defined by secular changes (currently: "the advent of agents"). This provides a north star without the rigidity of traditional planning. They complement this with loose quarterly OKRs and 4-6 week squad goals, always leaving "slack in the system" for the inevitable disruption.

Why It Matters

• Your product strategy is already outdated: If you're still thinking about products as things you ship rather than organisms you nurture, you're playing yesterday's game. Start building continuous learning loops into your product DNA now.

• Hire polymaths, not specialists: The days of siloed expertise are numbered. Your next hire should be someone who can code, design, analyze data, and understand business strategy—because that's table stakes when everyone has an AI copilot.

• Agents aren't coming—they're here: Companies deploying agent workforces will have exponential advantages over those clinging to traditional team structures. The question isn't if you'll have more agents than employees, but how quickly you can get there.

🎯 The AI Performance Gap Is Getting Wider

The TLDR

McKinsey and MIT's latest research reveals the AI performance gap has widened from 2.7x to 3.8x between leaders and laggards. The winners aren't just throwing money at AI—they're fundamentally rewiring their organizations with executive sponsorship, mature partnerships, and serious data investments.The Details

The Details

• Executive Muscle Matters: 77% of AI leaders have C-level sponsorship driving projects, with 44% sponsored directly by the CEO or board (double the rate of bottom performers). As one CTO put it: they kept projects alive through 3+ years of development with "uncertain timelines and returns" purely on conviction.

  Partnership Evolution: Leaders are moving away from experimental academia collaborations (down from 83% to 50%) toward mature vendor relationships. They're not just outsourcing—they're orchestrating. Smart leaders start partnerships with co-authored success documentation and end with thorough knowledge transfer.

  The Data Reality Check: 58% of leaders collect data from more than half their equipment vs. significantly less for laggards. A cement manufacturer's centralized data team grew from 3 to 30+ people, enabling predictive maintenance and energy optimization that returned 5x their investment.

  Cross-Functional Collaboration: The biggest barrier? Not technology—it's getting Operations Technology (OT) and Information Technology (IT) to play nice. Leaders create Centers of Excellence or dedicated cross-functional teams that bridge the traditional silos.

Why It's Important

• Payback periods have collapsed: From 18-24 months to just 6-12 months for both leaders and laggards

• The gap is accelerating: Performance differences increased by 40% in just two years

• It's not too late: Off-the-shelf solutions and MLOps automation are lowering barriers to entry—but the window is closing

📚 Interesting Reads

• MIT study claiming 95% of AI pilots fail? Don't believe the hype - Marketing AI Institute debunks the viral MIT study's methodology.

• MIT report misunderstood - While 95% of enterprise AI pilots fail, 90% of employees successfully use personal AI tools daily for work, creating a "shadow AI economy".

• Enterprise leaders say recipe for AI agents is matching them to existing processes—not the other way around - Block and GSK executives share how they're implementing AI agents by fitting them to current workflows rather than forcing process changes.

• 3 ways AI can improve team meetings - HBR explores how AI transforms meetings to help teams focus on discussion rather than documentation.

• How a 16-year-old company is easing small businesses into AI - Netstock's AI "Opportunity Engine" proves AI adoption success comes from empowering less-senior staff with prosaic insights they can trust.

• Why your AI product needs a different development lifecycle - A framework explaining how AI's non-deterministic nature requires starting with high-control, low-agency features and gradually earning autonomy through real-world validation.

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 ➜ Until Next Week

The companies that win in the AI era won't be those with the best models—they'll be those who reimagine products as living systems, dissolve organizational boundaries, and embrace the chaos of continuous evolution. Time to stop building artifacts and start breeding organisms.

Stay curious,

The GPTLDR Team