AI Adoption Gap: What It Means for Mid-Sized Businesses

On April 22, 2026, Infor released the Enterprise AI Adoption Impact Index: 1,000 decision-makers from the US, UK, Germany, and France, 49 percent of organizations are stuck in the early deployment phase, although 80 percent claim to have the internal capabilities to implement AI. In parallel, Infor presented new AI orchestration tools on April 22, which specifically address this scale gap. For German SMEs, three key figures from the index are immediately relevant for the current budget rounds.

What the Index Specifically Shows

What is the Enterprise AI Adoption Impact Index? The index is an industry study published by Infor on April 22, 2026, with 1,000 decision-makers from the US, UK, Germany, and France. It does not measure the presence of AI projects, but rather the maturity of scaling and value contribution. The methodological strength: the study asks for both self-assessment and hard indicators (productive deployments, ROI measurement, data management maturity) and contrasts both. The result is one of the sharpest pictures of the execution gap in enterprise AI available in 2026.

The central statement: more than half of all surveyed organizations do not get beyond early deployment. Two-thirds of respondents have at least one AI pilot project, but only one-third have productive AI in a core business process in use. This is a number that contradicts the 80 percent who claim to have the internal capabilities to implement it. The difference between capability and actual scaling is the gap that will be the focus in the coming twelve months.

For German SMEs, the index acts as a wake-up call in two directions. Firstly, the numbers match what we have observed in DACH consulting mandates since the beginning of the year: the pilot wave is through everywhere, the scaling wave is stalling. Secondly, the barriers (data security, talent, ROI) are not the ones that are solved by buying another model. They are organizational and data structural. Anyone who primarily buys new licenses in Q2 and Q3 instead of working on these three topics will fall back into the 49 percent category according to the index.

Three key figures from the index that belong in the executive round

View Transitions API sounds like niche tinkering. Until you’ve integrated it into a real product page for the first time and can’t live without it. Enterprise AI sounds like hype. Until you’ve cleanly integrated it into an ERP for the first time and can’t live without it.

Three steps for mid-sized businesses by July 2026

The practical consequence of the index is not “spend more money.” The index reveals that the bottleneck is structural in nature. Three concrete steps that can be achieved in the next three months and have a direct impact.

Step 1: Honest Scale Status Check

The 80 percent self-overestimation trap from the index can be easily tested within your own organization. Count the AI systems that are productively running in core business processes (not pilots, not experiments), count the number of users, count the time-to-productivity per ongoing initiative. If you have fewer than two productive systems with more than 100 users each, you’re in the 49 percent class. If you have four or more, you’re in the maturity class. The scale check takes half a day and is the basis for everything that follows.

Step 2: Data homework before buying new models

The index shows data security and compliance as the number one barrier. In consulting practice, this translates into three concrete topics that should be clarified before the next model purchase: Which data classes are allowed to flow into which model, how is this documented in the data lineage system, and what audit trail runs for each model inference? If you can’t answer these three questions in writing, you should spend the next 90 days on data infrastructure instead of model licenses. The calculation is simple: Without a clean data foundation, no model scales, no matter how expensive.

Step 3: ERP Integration as a Scale Platform

Infor’s approach with ERP-integrated AI is one of several possible scaling paths. Other providers (SAP Joule, Microsoft Dynamics Copilot, Oracle AI Agents) are pursuing similar strategies. The common thesis: AI scales best where data is already structured and processes are already defined. For mid-sized businesses already operating within Infor, SAP, or Microsoft ecosystems, the ERP-integrated AI option is a natural path. It reduces integration work because data mapping, authorization systems, and workflow engines are already in place. For greenfield organizations, the platform decision requires broader consideration.

What Infor’s new tools aim to achieve

Infor has simultaneously released a series of new AI orchestration tools with its Index Report, targeting the scaling gap. The core building blocks include: an orchestration layer between ERP data and various model providers, a central prompt library for reusable use cases, and a compliance framework that checks model calls against data protection and industry regulations. For Infor customers, this is a logical evolution. For non-Infor organizations, it’s a signal that major ERP providers will offer scale platforms by 2026, not just models. The subsequent SAP and Microsoft announcements in the coming weeks will likely showcase similar architectures.

Frequently Asked Questions

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Network: Read more on MyBusinessFuture

• Detailed homework in the AI data maturity analysis on the five homework assignments
• Foundational look at the AI failure rate 2026 and the structural question
• Regulatory classification on the Franco-German AI report and IPCEI AI homework