Five Levers to Scale AI

Many enterprises recognize that an overriding barrier to scaling AI is that their data isn’t “AI-ready.” While poor data quality, governance, and semantics undeniably matter, the latest research shows that data readiness is one of five reasons enterprises fail to capture value from AI. Scaling requires a multi-dimensional strategy that balances leadership, workflows, technology, data governance, and infrastructure. This paper outlines five interlocking levers that enterprises must activate to scale AI responsibly and effectively. 

1. Leadership & Governance Oversight 

McKinsey’s 2025 global survey found that CEO-owned AI governance is the single strongest predictor of enterprise-level AI value. Without CEO & board-level oversight, AI initiatives remain fragmented and fail to embed into enterprise operating models. Scaling requires clear executive accountability, governance committees, and metrics tied directly to financial and operational outcomes. 

2. Workflow Redesign & Change Management 

AI doesn’t create value in isolation; it creates value when humans adopt it into workflows. Studies from MIT Sloan/BCG show that organizational learning and role redesign are critical to converting pilots into scaled systems. This requires robust change management programs, new incentive structures, and ongoing training. 

3. Productization Discipline & Technical Debt Management 

The NeurIPS “Hidden Technical Debt in ML Systems” paper demonstrates that fragile glue code, pipeline jungles, and configuration debt often kill AI projects at scale. To move beyond pilots, enterprises must invest in robust MLOps, monitoring, version control, and automated testing to ensure reliability and maintainability. 

4. Data Readiness & Semantic Foundations 

High-quality, well-governed data remains essential. Enterprises need ontology-driven approaches that establish common definitions, lineage, and semantics across fragmented systems. Data readiness is not a silver bullet, but without it, every other lever weakens. The focus must be on living data supply chains, not static warehouses. 

5. Capacity & Infrastructure Scaling 

Scaling AI also faces hard physical constraints: compute availability, power density, and data center capacity. McKinsey estimates trillions in required infrastructure investment by 2030. Enterprises must plan now for sustainable, cost-effective compute and energy strategies or risk hitting scaling walls regardless of data quality. 

Enterprises don’t fail to scale AI because they haven’t become AI-ready organizations. Leaders must activate all five levers together: governance, workflow redesign, productization discipline, semantic data foundations, and infrastructure planning. Only then can AI deliver reliable, enterprise-wide impact.