Data released from Infor’s Infor Enterprise AI Adoption Impact Index reveals persistent, shared barriers preventing enterprises from launching complex AI initiatives, even among companies with strong ambition to scale.
In fact, while 80% of business decision-makers globally believe their organization has the internal capability to manage an AI implementation, significant structural barriers like data security, sovereignty, and compliance (36%), lack of internal AI talent (25%), and unclear ROI (23%) remain as major obstacles and prevent organizations from advancing their AI strategy.
Infor
Key takeaways:
· Despite widespread confidence in capability, nearly half of organizations globally (49%) are still in the early stages of AI deployment, with many yet to move beyond pilots or partial rollouts.
· One in four businesses cite lack of internal AI talent as a top barrier to scale AI.
· 32% of business leaders rank the ability for AI to perform tasks autonomously as a Top 3 priority for AI success.
- 80% of respondents believe their organization has the internal capability to manage an AI implementation. However, that confidence isn't necessarily converting into results: 49% are still stuck in the AI early stages — running pilots only, paused, or yet to start.
- When asked to name the single greatest barrier to advancing their AI strategy, respondents ranked data security, sovereignty/privacy, or compliance (36%) first, followed by lack of internal talent to configure and maintain AI (25%) and unclear business benefits or return on investment (23%).
- 27% of respondents were unsure or disagreed that their organization's data is mature and well-governed enough to support reliable AI.
- 31% were very or slightly uncomfortable with autonomous agents executing critical business processes.
- On average, nearly half (49%) of AI-generated insights and workflows require manual review by a subject matter expert to ensure accuracy against industry regulations and processes.
- When asked about their Top 3 priorities for ensuring long-term AI success, respondents ranked enhanced data security and sovereignty (37%), the ability for AI to perform tasks autonomously (32%), and industry-specific AI use cases (28%) the highest.
- 87% of respondents say fixed and predictable AI pricing is important, meaning cost transparency ranks highly as a capability when committing to long-term AI investment.
Infor also introduced a new Velocity Suite add-on for Infor warehouse management system (WMS) focused on improving day-to-day warehouse operations. The pick path optimization use case leverages machine learning to guide warehouse workers along the most efficient routes when picking items for orders.
Infor also announced the limited availability of a newly enhanced update, which will operate across three critical capability areas:
Orchestration: Advanced coordination between Supervisor Agents enables Infor GenAI Assistant to perform complex, multi-step workflows, integrating specialized task agents from planning to deployment. Supervisor Agents maintain context across relevant tools and are pre-trained to flag anomalies, freeing up employee time while ensuring a human remains in the loop where needed.
Interoperability: Enterprises currently spend an estimated 30-40% of their total budget on integration. With Infor Agentic Orchestrator, customers don't have to choose between cost and time savings: Infor's Model Context Protocol (MCP) servers standardize how AI models securely access data and take action across Infor applications, and because MCP is an open standard, they work alongside connections to non-Infor applications too. Additionally, third-party MCP tools and agents can be accessed through the Infor ecosystem.
Observability: New visibility features are divided into three updated capabilities — Inline Thoughts, Evaluation Framework, and Focus Mode — that allow users full control and oversight.
