Enterprise artificial intelligence is no longer a strategic aspiration — it is a competitive imperative. Yet despite record levels of investment and near-universal executive endorsement, a striking paradox has emerged at the heart of corporate digital transformation: most organisations cannot scale what they have already built. New proprietary research from Infor, surveying 1,000 business decision-makers across the United States, United Kingdom, Germany, and France, confirms that more than half of enterprises remain unable to move AI from controlled experimentation into operational production. For CFOs, General Counsel, and technology leaders navigating this terrain, the implications are both strategic and structural.

The Execution Gap: From Ambition to Operational Reality

The Infor Enterprise AI Adoption Impact Index, published in April 2026, gives quantitative weight to what many board members have sensed anecdotally: the distance between AI ambition and AI execution is widening, not narrowing. This is not a technology problem in isolation. It is an organisational readiness problem — one rooted in misaligned governance structures, undertrained workforces, and implementation strategies that were designed for pilots rather than scale.

Deloitte’s parallel research reinforces this picture. Worker access to AI tools rose by 50% in 2025, and companies now expect the number of AI projects in production to double within six months. The acceleration from experimentation to deployment is real — but the infrastructure required to support that transition at enterprise scale remains underdeveloped in the majority of organisations surveyed.

For European enterprises in particular, the challenge is compounded by regulatory complexity. The EU AI Act, which entered into force in August 2024 and began applying its high-risk provisions in 2025, imposes material obligations around transparency, human oversight, and data governance. Organisations that have not embedded compliance architecture into their AI operating models from the outset face significant rework costs — and legal exposure — as they attempt to industrialise their deployments.

Industry-Specific AI and the Shift Away from Generic Platforms

One of the most consequential trends emerging from this data is the accelerating shift toward vertical, role-based AI solutions over generic enterprise platforms. Infor’s latest release — encompassing Industry AI Agents, Value+ Solutions, and Prescriptive AI Use Case Packs embedded within its Velocity Suite — reflects a broader market recognition that horizontal AI tools, while powerful, create significant integration and adoption friction when applied to sector-specific workflows.

This matters enormously for mid-market companies, which have historically lacked the internal capability to customise general-purpose models for their operational context. Pre-built automation catalogs and managed AI services — such as Infor’s CareFor offering — compress time-to-value and reduce dependency on scarce AI engineering talent. The enterprise AI market is projected to grow from $24 billion in 2024 to between $150 and $200 billion by 2030, representing a compound annual growth rate exceeding 30%. Nearly one-third of Fortune 500 companies and one-fifth of the Global 2000 are already deploying what analysts classify as real enterprise AI — not prototypes, but production systems with measurable business impact.

For M&A directors and strategic advisors, this shift has direct valuation implications. Target companies with embedded, industry-specific AI capabilities — particularly those with proprietary training data and documented governance frameworks — command meaningful premiums in current transaction markets.

Governance, Compliance, and the Responsible AI Imperative

Perhaps the most significant structural development of the past twelve months is the institutionalisation of AI governance as a board-level concern. Over 70% of organisations now deploy generative AI across business functions, yet the frameworks governing data privacy, model explainability, and accountability remain inconsistent — particularly across multinational operations subject to divergent regulatory regimes.

General Counsel and Chief Compliance Officers face a dual mandate: enabling the innovation velocity that competitive positioning demands, while ensuring that AI systems meet the explainability and audit requirements increasingly expected by regulators, investors, and institutional counterparties. The EU AI Act’s risk-tiered approach provides a useful compliance architecture, but operationalising it requires cross-functional alignment between legal, IT, and business operations that many organisations have not yet achieved.

Implications for Decision-Makers: A Structured Path Forward

For executives responsible for digital strategy and innovation management, the data points to three immediate priorities:

  • Governance before scale: Establish AI governance frameworks — including data lineage, model risk management, and human oversight protocols — before expanding production deployments. Retrofitting compliance is materially more expensive than designing it in.
  • Vertical over horizontal: Evaluate industry-specific AI solutions and pre-built use case libraries as the primary route to accelerated value realisation, particularly where internal AI engineering capacity is constrained.
  • Measure ruthlessly: Transition from activity-based AI reporting (models deployed, pilots launched) to outcome-based metrics — cost reduction, cycle time compression, revenue attribution — that satisfy CFO and board scrutiny.

Key Takeaway

The enterprise AI adoption gap is not a technology deficit — it is an execution and governance deficit. With the market projected to grow sixfold by 2030 and regulatory frameworks tightening across Europe and beyond, the organisations that will capture disproportionate value are those that treat AI scaling as a structured programme discipline, not an innovation experiment. The pilot phase is over. The question now is whether your operating model is built for production.