From proof of concept to proof of performance: what the enterprises sustaining AI value figured out first.
Waiting for certainty has a cost. See how leading CEOs are redesigning their organizations for an AI-native future.
Building for the future that’s here
Let’s fast forward in time for a moment. Your AI strategy has been ambitious, well-funded and is generating real energy across the enterprise. You’ve built the case and launched pilots that show genuine promise. The foundation has been built.
Now for the harder question: how do you design for lasting value? How do you move from local wins to AI thriving at the center of your business? This is where many companies stall, and the ones that don’t aren’t just experimenting, they’re building connective tissue between disciplines and setting up for the long game: repeatability.
Dr. Dorottya Sallai
London School of Economics
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AI adoption is a cultural transition. ”
When wins stay small
A deployment works beautifully in one business unit but can’t transfer. An automation saves hours here and creates a bottleneck there. Without a shared definition of what progress looks like, confidence erodes and AI stays a collection of tools rather than an operating capability.
Scaling AI is an operational challenge and an economic one. As model portfolios grow and infrastructure demands multiply, cost discipline becomes inseparable from value creation. 21% of gen AI initiatives have already failed to scale because of cost alone.²
92% of CDOs say they need to focus on business outcomes to succeed, yet fewer than a third have clear measures for it.² When success is defined differently across functions, the enterprise can’t distinguish between a portfolio of wins and a portfolio of experiments.
This is up from only 41% in 2023. The foundation is improving fast.³ But platform maturity alone doesn’t produce value. The enterprises pulling ahead are connecting unified data to shared metrics and repeatable patterns so that every initiative reinforces the one that follows.
The companies coming out ahead design for integration from day one. They don’t see strategy, risk, culture and value as separate challenges, but as levers in a single system—one that learns faster, adapts more fluidly and compounds every investment it makes.
Why vision is make-or-break
When progress isn’t explicitly defined, AI adoption loses momentum and settles into a set of disconnected tools rather than becoming part of how the business runs.
We’ve seen this firsthand. Something launches cleanly in one business unit and looks solved. Try to move it elsewhere, and it starts to come apart. An automation streamlines work in one area, only to introduce friction elsewhere. What felt like early success might break down under the weight of the whole enterprise.
Making wins add up
When scaling breaks happen, they accumulate quietly. Integration is retrofitted, not designed. Compute costs climb and gen AI initiatives can’t scale.
Research shows AI pays off when placed at the center of business functions—operations, finance, supply—where integration discipline matters most. For AI to perform at scale, you need to treat it as a systematic capability. Those unwilling to take the leap can’t unlock the underlying promise.
IBM Client Zero, case study, 2025
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IBM unlocked USD 4.5 billion—and counting—in productivity gains through AI, hybrid cloud, automation and consulting expertise. ”
The intelligence ecosystem
Designing for integration—from day one—reliably creates lasting value. Organizations that succeed approach strategy, risk, culture and value as interdependent forces in a shared system.
And this system is more durable than a technology advantage. It learns faster, adapts more fluidly and compounds investment. We’ve worked alongside leaders navigating this transition, and what we’ve seen is that alignment, done well, doesn’t just close the gap between ambition and impact but opens up possibilities that weren’t visible before.
In 2023, only 41% of data leaders had the right platform.³ By 2025, 75% report having one that integrates across silos.¹ That shift matters because unified data, enforced quality standards and clear lineage determine whether AI delivers useful insight or expensive noise.
70% of executives say gen AI is a key driver of compute costs. On average, 21% of gen AI initiatives have failed to scale for this reason.¹ A portfolio-level architecture that balances flexibility with cost control helps you extract more value from your current investments.
When these functions operate independently it results in duplicated effort, inconsistent measurement and rising costs. CDOs who adopt hub-and-spoke or centralized models are seeing 36% higher performance.⁴
The sovereignty illusion: Why most enterprises don’t control what they think they do
79% of executives say AI will contribute to their revenue by 2030. Given this confidence, why are 95% of organizations seeing no measurable return?
The difference between high-return and low-return deployments often comes down to one question: did leadership align on what needs to change to win?
Culture isn’t alongside the AI strategy. It’s the infrastructure the strategy runs on. The organizations scaling sustainably are redesigning how work moves.
Footnotes
1 The CEO’s Guide to Generative AI: Cost of Compute, IBM Institute for Business Value, IBM, October 2024.
2 The 2025 CDO Study: The AI Multiplier Effect, IBM Institute for Business Value, IBM, 2024.
3 The 2023 CDO Study: Turning Data into Value, Global C‑suite Series, IBM Institute for Business Value, IBM, April 2023.
4 Solving the AI ROI Puzzle: How Chief AI Officers Cut Through Complexity to Create New Paths to Value, Research Insights, IBM Institute for Business Value, IBM, July 2025.