Everyone Is Racing for AI But Few Are Racing the Right Way
Everyone is obsessing over the wrong AI race. We are fixated on who has the most advanced models, the fastest chips, or the highest-paid data scientists. That is not the real competition. The true race is one of organizational architecture, how fast an institution can absorb, deploy, and scale intelligence, and the U.S. is dangerously underprepared.
The U.S.–China narrative is framed as a high-tech arms race, but this misses the crucial operational layer. China’s 2017 AI plan explicitly treats it as a complex systemic project, an R&D, applications, and industry trinity. Their advantage is not just in raw innovation; it is in a state-steered model designed for rapid integration and deployment.
This is not a new threat. The U.S. has been here before, outpaced not in technology but in capability. When we last fell behind in execution, we did not just try harder. We fundamentally reinvented how our organizations worked..
History Repeats Itself: The Baldrige Precedent
In the 1980s, the U.S. faced a wake-up call. Japanese manufacturing was dominating, and U.S. industry was seen as bloated and slow. This wasn't just a perception; it was a quantifiable crisis. The U.S.-Japan productivity gap had closed, and poor quality was costing American companies as much as 20% of their sales revenues.
The response was systemic. Congress passed the Malcolm Baldrige National Quality Improvement Act of 1987, a public-private partnership managed by the National Institute of Standards and Technology (NIST). The genius of the Baldrige program was that it didn't reward a specific product; it recognized the excellence of an entire management system.
The program's 7 Criteria became a roadmap for organizational reform, with millions of copies distributed. It created a shared language of performance from the factory floor to the C-suite. And it worked. A NIST stock-market study found that Baldrige winners outperformed the S&P 500 by more than 5 to 1. It wasn't better products that saved American industry, it was better architecture. AI needs the same.
Today’s Mirror: The AI Readiness Gap
On paper, America looks dominant. In practice, the data tells a different story.
On the visible scoreboard, the U.S. appears to be leading. It dominates private AI investment and produces the vast majority of top-tier AI models.
But these are output metrics, not readiness metrics. The invisible race is where the U.S. is failing. The structural barriers to scaling AI are less about talent and more about structure. The primary bottlenecks are organizational and cultural:
Data Fragmentation: 74% of U.S. data leaders identify data integration as the number one inhibitor to scaling AI.
Governance Gaps: 31% of U.S. boards cite uncertain ROI as a key hurdle and 62% of firms operate with reactive governance focused on compliance rather than proactive enablement.
System Misalignment: Firms are AI-aware (exploring pilots) but not "AI-integrated.
China's structural advantage is its state-steered integration. It uses top-down mandates and centralized data mobilization to align policy, research, and industry. They aren't just acquiring talent; they are re-engineering industrial processes.
The U.S. has a world-class innovation ecosystem. Innovation is there; integration isn’t. This is the same structural lag that preceded the Baldrige era.
The Architecture Gap: Inside the Enterprise
This national-level gap is mirrored inside our companies. We see a gen AI paradox: while nearly 80% of companies report deploying generative AI, a similar percentage report no material impact on their earnings.
Just as Japanese manufacturers once outpaced U.S. firms through systemic discipline, today’s AI lag is a reflection of fragmented internal architecture. AI is being installed as a pilot or innovation hub—it isn’t embedded in core decision-making.
I feel boards and executives are misreading AI as a technical investment, not an organizational design shift. You can't bolt AI onto a company designed for linear, siloed decision-making. The real value, as McKinsey notes in its report The state of AI: How organizations are rewiring to capture value, comes from 'rewiring how companies run', which requires a top-down mandate from the C-suite.
The Baldrige movement worked because it forced leaders to adopt a systems perspective and manage the whole organization, not just its parts. The fragmented initiatives stalling AI today are a direct recurrence of the TQM failures Baldrige was created to fix.
The Next Baldrige Moment: What It Would Take
A fragmented, firm-by-firm approach is creating a national-level vulnerability, as every company is left to invent its own definition of 'AI readiness'.
In my opinion, what's needed is a second Baldrige moment: a voluntary, public-private framework focused on AI Organizational Readiness. This framework wouldn’t regulate algorithms; it would establish standards and metrics for the organizational capabilities required for effective, responsible, and scalable AI deployment. This is already beginning: NIST is integrating AI into its 2025-2026 Baldrige Criteria and has published its AI Risk Management Framework (AI RMF). A new framework would shift the focus from "Is the algorithm trustworthy?" to "Is the organization capable of deploying trustworthy AI at scale?".
This would require a coordinated public-private partnership, just like the original. Policymakers could authorize a body like NIST to manage the framework and leverage federal procurement as an incentive. Industry consortia would co-design practical standards. Corporate leaders would use the framework for self-assessment, shifting investment from just buying tools to the essential work of re-architecting the business. This rewiring could start by tying executive scorecards to AI readiness metrics or redesigning a core workflow (like customer service) around AI-driven insights rather than simply bolting on a chatbot.
The Stakes
The AI race won't be won by the smartest algorithm, but by the smartest organization.
We don't just need faster models; we need smarter systems. That shift begins not in labs, but in boardrooms. The blueprint exists—the only question is whether today’s leaders act before the race is decided.