Enterprise AI has entered a new phase. Deloitte’s 2026 State of AI in the Enterprise report finds that worker access to AI tools rose 50% in 2025, and that companies with at least 40% of AI projects already in production are expected to double within six months. For CFOs, General Counsel, and M&A Directors, this is no longer a story about experimentation — it is a story about scale, governance, and capital allocation. The strategic question has shifted from whether to adopt AI to how fast and how safely an organization can move from pilot to production.

The Scaling Gap: Adoption Outpaces Readiness

The most consequential data point for boards is not adoption itself, but the widening gap between usage and organizational readiness. Publicis Sapient’s Global Enterprise AI Report finds that while most enterprises now use AI in some form, only 10% describe it as core to their operations, and 42% admit their organizations are not structurally built to capture its value. This is a governance and digital strategy problem as much as a technology one.

Deloitte identifies the AI skills gap as the single largest barrier to scaling — ahead of budget, data quality, or infrastructure constraints. This matters for M&A due diligence: target companies claiming “AI-driven” operations should be assessed not just on tool deployment but on whether talent, governance frameworks, and production infrastructure actually support claimed capabilities. Overstated AI maturity is becoming a material representation-and-warranty risk in transaction documentation.

Regionally, Asia Pacific is leading early implementation of physical AI and production-grade deployment, creating a competitive tempo that European mid-market firms — often constrained by legacy systems and fragmented cloud migration efforts — risk falling behind on. This is not a call to move recklessly, but a signal that governed speed is now a competitive differentiator.

Operating Model Transformation, Not Tool Adoption

Publicis Sapient’s research reframes the challenge correctly: success no longer depends on which AI tools an enterprise licenses, but on whether legacy systems are modernized, workflows are connected, and the operating model itself is redesigned around AI-enabled decision-making. This aligns with what LLS observes across advisory engagements: digital transformation programs that stall at the pilot stage almost always share the same root cause — AI initiatives are bolted onto existing processes rather than integrated into a coherent digital strategy.

Three structural priorities separate scalers from stallers:

  • Data and cloud foundation: Production-grade AI requires clean, governed data pipelines and cloud migration maturity — not just storage, but interoperability across business units.
  • Governance-by-design: With the EU AI Act’s phased obligations now active for general-purpose and high-risk systems, compliance cannot be retrofitted. Risk classification, documentation, and human oversight need to be embedded in innovation management processes from the outset.
  • Talent and change management: The skills gap Deloitte flags is rarely closed through hiring alone; it requires structured reskilling tied to specific production use cases, not generic AI literacy training.

Partnership activity reinforces this shift toward infrastructure-level commitment. Infosys and Intel’s expanded collaboration on agentic AI services and high-performance compute platforms illustrates how enterprise AI adoption is increasingly being underwritten by long-term infrastructure bets rather than software subscriptions — a signal that emerging technology strategy is consolidating around fewer, deeper vendor relationships.

Implications for Decision-Makers

For CFOs, the capital allocation question is shifting from tool licensing costs to platform and talent investment with multi-year payback horizons. For General Counsel, AI governance frameworks — spanning the EU AI Act, sector-specific compliance, and M&A representations — require earlier involvement in transformation roadmaps, not after-the-fact review. For CTOs and M&A Directors, technical due diligence must now interrogate production readiness, not just proof-of-concept demonstrations.

Boards should request quarterly reporting on the ratio of AI projects in production versus pilot, alongside skills-gap remediation plans — the two metrics Deloitte identifies as most predictive of scaling success.

Key Takeaway

The enterprises pulling ahead in 2026 are not those with the most AI pilots, but those that have redesigned operating models, closed the skills gap, and embedded governance into their digital transformation strategy from day one. For mid-market firms, the window to close this gap — before scaled competitors and regulatory expectations both harden — is narrowing quickly.