Enterprises continue to grapple with persistent gaps between AI ambitions and execution, creating an opening for partners to expand their services.
Despite growing confidence, most organizations lack the operational discipline to deliver measurable returns on AI investments, according to ERP vendor Infor. The company surveyed 1,000 business decision-makers across the U.S., UK, Germany and France.
Four in 5 respondents said they believe their organization has the internal capability to manage an AI implementation. Yet nearly half — 49% — are waiting on pilots, paused efforts or projects that have not yet started.
The disconnect points to a widening execution shortfall, Infor said. Businesses are moving past AI experimentation, even as they lack the data, governance, integration and skills to scale the technology across core workflows.
“Our Index found that 36% of enterprises cite data security and sovereignty as their top barrier, and nearly half of AI-generated insights still require manual review,” Rick Rider, Infor’s SVP of AI innovation, told Channel Dive. “That tells us that the underlying data environments and processes simply aren't ready to support autonomous AI at scale.”
The issue is not limited to data quality. Rider said the larger challenge ties back to process maturity, “where customizations have ultimately created the data gaps we see today.”
Addressing adoption barriers requires organizations to consider how their technology accounts for process standards and data governance, he said. But enterprises can’t afford to wait until every system, workflow and data set is fully optimized before they begin proving AI’s value.
That is where channel partners have an opening. The readiness gap, as Rider called it, gives MSPs, VARs and SIs a clear services roadmap — from data modernization and governance to workflow redesign, change management and ongoing optimization.
Customers need help cleaning up data and processes before AI can work reliably, Rider said. From there, workflows need to be redesigned so AI can operate within them. Organizations also need ongoing support to tune models, activate new use cases and demonstrate ROI over time.
“Partners who can support customers beyond go-live optimization are positioned to build deep, recurring relationships rather than one-time project engagements,” Rider said. “It’s actually a whole new business model that requires continuous collaboration, co-development and maintenance that is different than typical SaaS solutions.”
More than one quarter of respondents to the Infor survey cited a lack of internal AI talent as a barrier, reinforcing the channel opportunity. Customers may believe they can manage AI implementation, but many still need third-party support to convert AI rollouts into scalable solutions.
The survey publication coincided with updates to Infor’s Velocity Suite, which now includes Infor Industry AI Agents, Agent Orchestration and Agent Factory, along with prescriptive AI use case packs organized by role, process and industry. The release also includes a new Velocity Suite add-on for Infor Warehouse Management System and limited availability of an enhanced Agentic Orchestrator, which coordinates multi-step workflows, connects AI models to data across Infor and non-Infor applications and provides observability features.
The expanded offering requires more than generic AI deployment support — a potential boon for partners. Infor’s research found industry-focused AI use cases ranked among respondents’ top three priorities for long-term AI success, right behind data security and autonomous capabilities.
“Industry specificity, and thus deep AI context, is the differentiator,” Rider said. “Generic AI applied to industry-specific problems produces generic results.”
Partners who can assess readiness, modernize data environments and tie AI work to industry-aligned processes will have the edge against those leading with tools alone, Infor pointed out.
“The partners who position themselves as the guide through the readiness gap, not just the implementer, will define the next chapter of enterprise AI,” Rider said. “What customers need is a partner who will meet them where they are.”
