Picture this: Your team spent six months and a meaningful chunk of the IT budget on an AI pilot. It worked. Leadership was impressed. Someone put together a deck. And then... nothing. The pilot didn't go anywhere. Eighteen months later, you're still pointing to the same demo as evidence of AI progress.
If that scenario feels familiar, you're not alone. New research from Infor confirms exactly how common it is.
The Gap Between Ambition and Execution Is Getting Wider
Infor's Enterprise AI Adoption Impact Index, a survey of 1,000 business decision-makers across the US, UK, Germany, and France, found that 49% of organizations are still stuck in the earliest stages of AI adoption: running pilots only, paused, or yet to start. Half the businesses surveyed, most run by leaders who believe they have what it takes to manage AI, haven't moved past the experiment phase.
The confidence numbers make this even more striking. Eighty percent of respondents said their organization has what it takes to run an AI implementation. That's not false humility at work. These are C-suite and director-level professionals who generally aren't prone to underestimating themselves. Yet confidence isn't translating into results.
When asked to name the single greatest barrier holding back their AI strategy, the survey revealed three culprits:
- Data security, sovereignty, and compliance concerns (cited by 36%)
- Lack of internal AI talent to configure and maintain systems (cited by 25%)
- Unclear business benefits or ROI (cited by 23%)
The data also tells a subtler story. Twenty-seven percent of respondents weren't sure their organization's data is mature and well-governed enough to support reliable AI. Nearly half (49%) said AI-generated insights require manual review by a subject matter expert before they can be trusted.
That's not AI working at scale. That's an expensive second-guessing machine.
Why Mid-Market Companies Get Hit the Hardest
A 5,000-person company with a dedicated AI team, a Chief Data Officer, and a multi-year transformation budget can absorb these problems. It has data governance specialists on full-time payroll, integration experts who've seen this before, and enough budget slack to fund a few experiments that go nowhere without anyone panicking.
Mid-market companies, typically ranging from $50M to $1B in revenue, don't have that luxury. IT teams are already stretched. Data often lives across a patchwork of ERPs, spreadsheets, and legacy systems that were never designed to talk to each other. When an AI pilot fails to scale, there's no dedicated team to diagnose why. There's just a frustrated ops leader and a vendor contract that's about to renew.
Research from TXI's AI Readiness Assessment found that 71% of foundational mid-market organizations have outdated data systems that cannot support modern AI deployment. Integration alone, just making the data plumbing work, can consume 30 to 40 percent of total AI project costs when organizations are running on disconnected systems.
For a mid-sized manufacturer or regional distributor, that tax is brutal.
You're not spending money on AI. You're spending it on duct tape.
The Three Root Causes (And Why They Almost Never Travel Alone)
1. Fragmented Data
AI doesn't fail because the algorithms are bad. It fails because the data feeding those algorithms is inconsistent, siloed, or incomplete.
A mid-market food and beverage company might have demand forecasting data in one system, supplier data in another, and logistics data in a spreadsheet managed by one person who's been with the company for 15 years. An AI model trained on part of that picture will produce outputs that are confidently wrong. Predictions stop reflecting how the business actually operates, and people stop trusting the system.
This is exactly what ABF Journal's analysis of midsize banks identified as the core vulnerability when organizations try to deploy agentic AI. Their framing was direct: if you're running multiple AI agents that need to coordinate — say, one agent evaluating a loan applicant's financials and another assessing collateral value — and those agents are pulling from fragmented, inconsistent data sources, "it's hard to get the agents to run well independently, much less as an agentic-AI team."
The fix isn't necessarily a full data warehouse rebuild. Enterprise data fabric solutions can sit on top of what you already have, standardizing how information is interpreted without requiring you to rip out legacy infrastructure. That's a faster and cheaper path than most mid-market teams expect.
2. Lack of Governance
Without governance, AI projects tend to sprawl — and when something goes wrong, nobody knows whose problem it is.
Different departments run different tools with different data. Compliance teams find out about AI deployments after the fact. And when a bad recommendation, a biased output, or a regulatory flag surfaces, there's no clear accountability structure to catch it or fix it. So it festers.
The Infor research puts a number to this: 31% of respondents said they're uncomfortable with autonomous agents executing critical business processes. That's not irrational technophobia. It's a reasonable reaction to the absence of guardrails.
The ABF Journal's framework for midsize banks describes what functional governance looks like: "Establishing centralized AI management and governance regimes led by top management." The emphasis on top management matters — governance that lives only in IT has no authority over the decisions that actually need governing. It takes C-level sponsorship to shape how AI outputs get reviewed, challenged, and acted on across the organization.
For mid-sized companies, the governance function doesn't need to be a large team. It needs to answer three questions clearly: Who approves new AI use cases? Who reviews AI outputs for accuracy and compliance? And who gets the call when something goes wrong?
3. Poorly Mapped Processes
Clean data and solid governance still won't save you if you've deployed AI in the wrong places or without understanding how work actually flows.
The Manchester Enterprise's reporting on deliberate AI adoption captures the dynamic clearly: smart mid-sized companies are slowing down before they sprint. They're mapping what they actually do, process by process, function by function, before dropping AI into the mix. Because AI applied to a broken or poorly understood process doesn't fix anything. It just makes the broken process run faster.
The ABF Journal makes the same point in banking: "The most important facet of a midsize bank's strategic AI development is understanding what the house is actually doing, process by process, function by function." Business process management software can help here — process mining tools that show how work actually flows, versus how a workflow diagram says it should, are increasingly accessible to mid-market organizations and often surface efficiency opportunities that have nothing to do with AI.
The diagnostic question is simple, even if answering it takes work: How is this department currently doing the thing AI is supposed to improve, and where exactly does the data for that process live?
What Scaling Actually Looks Like
Scaling AI isn't dramatic. It doesn't look like a press release. It looks like a dairy company in Brazil that can finally answer supply chain questions without hunting down the one person who knows where the data lives.
That's essentially what Tirol, a leading dairy producer in Brazil, accomplished using Gemini Enterprise. They built an interactive knowledge base that made supply chain data accessible to any worker in the organization, rather than locked away in scattered systems or the memory of a few veteran employees. The ROI isn't a flashy number. It's fewer bottlenecks, faster decisions, and no longer losing critical knowledge every time someone retires.
Think of mid-market AI scaling less as a moonshot and more as expanding who in the company can actually access and act on real data. When that value compounds across every routine decision in operations, procurement, and logistics, you've stopped piloting and started operating.
Infor's Nucleus Research data points to similar patterns among companies that get data consolidation right: 37% lower integration and maintenance costs, AI deployments up to 30% faster, and 10 to 20 percentage point improvements in model accuracy. These aren't outcomes reserved for enterprise giants. They're what happens when the underlying foundations get fixed first.
A Diagnostic You Can Run This Quarter
Moving from pilot to production rarely comes down to budget size. It comes down to honest diagnosis of what's actually broken before investing further. Run this internally before your next AI budget conversation:
Step 1: Map your data reality, not your data aspiration. Pick one AI use case you want to scale. Trace every data input that use case requires back to its source. Is it in one system or five? Who owns it? How often is it updated? How is it defined, and is that definition consistent across departments? This exercise alone will tell you whether scaling that use case is a three-month project or a three-year one.
Step 2: Audit who actually owns your AI decisions. Answer these three questions: Who in your organization currently approves new AI deployments? Who reviews AI outputs before they influence a significant business decision? If an AI system produces a wrong answer that affects a customer or a regulatory obligation, who finds out, and how quickly? Vague answers mean you have a governance gap. Fixing it doesn't require a new department — it requires clarity on accountability.
Step 3: Process-audit before you automate. For the use case from Step 1, interview the people who actually do that work today. Not their managers. The practitioners. Understand the exceptions, the workarounds, the steps that only exist because of one person's institutional knowledge. AI will inherit those quirks and amplify them. Fix the process first, or at minimum understand it well enough to know which parts AI should and shouldn't touch.
Most mid-market AI failures aren't technical. They're diagnostic. Companies keep funding the next pilot before they've understood why the last one stopped moving. The three questions above won't feel urgent until the fourth pilot stalls.
Run them now.