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Business leaders invested heavily in artificial intelligence throughout 2025, yet frustration mounted as promising pilots failed to deliver widespread results. A striking 62% of organizations remained confined to experimental stages, unable to transition technologies into core operations that drive revenue.[1] This bottleneck exposed vulnerabilities in data readiness, talent shortages, and unclear returns, leaving companies vulnerable to competitors who cracked the code. Now, in 2026, fresh research illuminates actionable strategies to bridge this gap.
Why Most AI Efforts Remain Experimental
Organizations poured resources into AI trials last year, but scaling proved elusive. According to analysis from McKinsey’s “The state of AI in 2025,” 62% of firms could not advance beyond initial testing phases.[1] Common hurdles included fragmented data systems and legacy infrastructure that hindered integration.
Executives reported high confidence in adoption – around 80% believed their teams could handle implementations – but reality diverged sharply. Nearly half of AI-generated outputs demanded manual oversight to align with regulations, eroding efficiency gains. These persistent issues underscored a broader truth: experimentation alone yields limited value without structured execution.
Revealing Barriers Through Global Surveys
Infor’s Enterprise AI Adoption Impact Index, based on a March-April 2026 survey of 1,000 decision-makers across the US, UK, Germany, and France, pinpointed precise obstacles. An alarming 49% of respondents operated solely in pilot mode, had paused efforts, or had yet to begin.[2] Data security, sovereignty, privacy, and compliance topped the list at 36%, followed by talent shortages at 25% and ambiguous ROI at 23%.
A complementary YouGov Pulse Survey echoed these findings, with 70-75% expressing capability in AI management yet citing similar constraints like data governance in 32-45% of cases.[3] Infrastructure limitations, rather than tool access, emerged as the core impediment. Businesses in retail, manufacturing, and logistics faced acute pressures to modernize without disrupting operations.
- Data security and compliance concerns (36% of respondents)
- Internal talent gaps for AI maintenance (25%)
- Uncertain business benefits or ROI (23%)
- Data maturity issues (27% uncomfortable with governance)
Three Steps to Unlock AI Value
Infor outlined a streamlined framework in its eBook, “The Simplest Path to Achieve Value with AI,” designed to address these pain points directly. The guide details three straightforward steps for organizations to generate measurable impact, drawing on customer success stories and practical technologies.[1] It emphasizes overcoming implementation barriers through embedded AI and automation tailored to industry needs.
Readers gain insights into leveraging Infor AI agents and GenAI solutions for enterprise rollout. The resource shifts focus from hype to outcomes, helping firms create reliable workflows that require minimal intervention. Early adopters reported faster decision-making and cost reductions, proving the approach’s viability.
“At Infor, agentic AI isn’t a feature we bolted on. It’s the culmination of two decades of deliberate foundation building… That specificity is what allows us to clearly articulate the ROI, and deliver on it.”
Kevin Samuelson, CEO, Infor[2]
Infor Velocity Suite: Tools for Enterprise Scale
Infor Velocity Suite stands at the forefront of this transition, integrating AI agents, orchestration, and factory tools into industry-specific platforms. Features like Value+ Solutions offer pre-built automations within CloudSuites, while Prescriptive AI Use Case Packs target roles and processes.[2] A new add-on for warehouse management optimizes pick paths, cutting travel by up to 25% and boosting speed by 15% in tests.
The Agentic Orchestrator enhances coordination for complex workflows, ensures interoperability across systems, and provides observability for trust. With predictable pricing valued by 87% of leaders, the suite aligns with demands for secure, autonomous operations. IDC’s Mickey North Rizza noted clients achieve “sustained economic value” through this path.[2]
As AI matures, companies that prioritize governed, contextual deployment will pull ahead. The choice between prolonged trials and tangible gains rests on embracing proven frameworks now, ensuring investments translate to lasting competitive edges.
