\u21e8 Many businesses are enthusiastic about AI and believe they have the skills to scale it, but a large number are still struggling to move beyond early-stage deployments.<\/p>\n\n\n\n
\u21e8 Successful AI adoption depends on more than technology. Stable ERP processes, reliable data, governance, and clear business objectives are becoming essential foundations.<\/p>\n\n\n\n
\u21e8 CIOs and ERP leaders are under increasing pressure to build stronger data governance, develop AI capabilities across teams, and establish clear measures for ROI.<\/p>\n\n\n\n
Organisations are moving quickly to experiment with artificial intelligence, but many are finding that turning promising pilots into large-scale business capabilities is much harder than expected.<\/p>\n\n\n\n
Infor’s Enterprise AI Adoption Impact Index highlights a growing disconnect between how confident companies feel about their AI capabilities and how successfully they are actually deploying AI across their operations.<\/p>\n\n\n\n
The research, based on responses from 1,000 decision-makers across the United States, United Kingdom, Germany, and France, found that many organisations remain stuck in the early stages of AI adoption.<\/p>\n\n\n\n
What makes the findings particularly interesting is that while a significant number of businesses struggle to scale AI, around 80 percent of respondents believe their organisation already has the internal capability to implement it.<\/p>\n\n\n\n
For CIOs, ERP leaders, and transformation teams, this points to a familiar problem.<\/p>\n\n\n\n
The challenge is often not getting an AI pilot to work. The real challenge is integrating AI into everyday business processes in a way that is secure, governed, measurable, and scalable.<\/p>\n\n\n\n
The index focuses less on whether organisations are experimenting with AI and more on whether those experiments are creating meaningful operational impact.<\/p>\n\n\n\n
According to the findings, nearly half of organisations remain in the early stages of deployment, despite having already developed proofs of concept or launched initial AI initiatives.<\/p>\n\n\n\n
This is the stage many businesses now describe as “pilot purgatory.”<\/p>\n\n\n\n
AI experiments may generate excitement and demonstrate potential, but they never become deeply integrated into the workflows that employees use every day.<\/p>\n\n\n\n
There are several reasons behind this.<\/p>\n\n\n\n
Data security was identified as the biggest challenge, followed by a shortage of AI talent and uncertainty around return on investment.<\/p>\n\n\n\n
These issues highlight why AI adoption is increasingly becoming an enterprise transformation challenge rather than simply an IT project.<\/p>\n\n\n\n
Technology leaders now need to answer difficult questions around data ownership, security, governance, skills, accountability, and business value before AI can move into core operational systems.<\/p>\n\n\n\n
Infor’s findings suggest that many organisations are running multiple AI experiments without connecting them to end-to-end processes, consistent data models, or operating structures that would allow them to scale.<\/p>\n\n\n\n
The result can be a growing collection of AI tools that create more complexity without delivering meaningful improvements in productivity, customer experience, or operational performance.<\/p>\n\n\n\n
AI cannot scale effectively on top of unstable business processes.<\/strong><\/p>\n\n\n\n For ERP and transformation teams, AI readiness starts with the fundamentals.<\/p>\n\n\n\n Organisations need reliable processes, strong data quality, clear governance, and defined responsibilities before introducing AI into critical workflows.<\/p>\n\n\n\n The focus should not simply be on launching more pilots. It should be on identifying where AI can become part of an existing business process and deliver a measurable improvement.<\/p>\n\n\n\n The difference between experimentation and real value often comes down to how closely AI is connected to operational workflows.<\/p>\n\n\n\n Infor points to examples where AI has been applied to specific business problems rather than being deployed as a general-purpose technology experiment.<\/p>\n\n\n\n In one example, warehouse optimisation helped reduce employee travel distance by 25 percent. In another, automation improved order entry processes and allowed employees to spend more time on higher-value work.<\/p>\n\n\n\n These examples are important because they demonstrate what successful enterprise AI often looks like.<\/p>\n\n\n\n It is not necessarily a dramatic transformation overnight.<\/p>\n\n\n\n Instead, value can come from solving specific operational problems, reducing repetitive work, improving decision-making, and making existing processes more efficient.<\/p>\n\n\n\n When AI is connected directly to ERP, warehouse, supply chain, finance, or workforce workflows, it becomes easier to measure whether it is actually creating business value.<\/p>\n\n\n\n Infor is responding to these challenges with new capabilities across its Velocity Suite and an enhanced version of its Agentic Orchestrator.<\/p>\n\n\n\n The goal is to help organisations move beyond isolated AI experiments and toward more structured, governed automation.<\/p>\n\n\n\n Velocity Suite brings together tools, accelerators, and industry-focused approaches designed to help customers deploy and modernise Infor’s cloud solutions more efficiently.<\/p>\n\n\n\n For ERP teams, this could help address an important problem.<\/p>\n\n\n\n AI initiatives are much harder to scale when they are introduced on top of fragmented systems, inconsistent data, or poorly integrated processes.<\/p>\n\n\n\n Creating a more standardised foundation for migration, configuration, and integration can make it easier to introduce AI into business workflows later.<\/p>\n\n\n\n The enhanced Agentic Orchestrator takes this a step further by focusing on how multiple AI agents can work across different business functions.<\/p>\n\n\n\n These agents can support workflows across areas such as supply chain, finance, and workforce management while operating within defined governance and security frameworks.<\/p>\n\n\n\n Infor says AI-driven actions can be logged, policies can be enforced, and human oversight can remain in place for higher-impact decisions.<\/p>\n\n\n\n This is becoming increasingly important as enterprise AI moves beyond simply answering questions.<\/p>\n\n\n\n The next generation of AI systems is expected to recommend actions, trigger workflows, handle exceptions, and potentially complete tasks with varying levels of autonomy.<\/p>\n\n\n\n The next major AI battleground will be orchestration.<\/strong><\/p>\n\n\n\n Businesses are unlikely to rely on a single AI tool to manage every process.<\/p>\n\n\n\n Instead, they may increasingly use multiple specialised AI agents working across different business functions.<\/p>\n\n\n\n The challenge will be managing how those agents interact with data, systems, workflows, and employees.<\/p>\n\n\n\n Platforms that can provide strong orchestration, security, logging, governance, and human oversight could therefore become increasingly valuable.<\/p>\n\n\n\n One of the most important messages from the Infor index is that organisations should assess their operational readiness before scaling AI.<\/p>\n\n\n\n An organisation may have talented employees and access to powerful AI technology, but that does not automatically mean it is ready to deploy AI across critical business processes.<\/p>\n\n\n\n ERP and architecture teams will increasingly need to evaluate areas such as:<\/p>\n\n\n\n These areas will become essential parts of enterprise AI roadmaps.<\/p>\n\n\n\n For organisations running complex environments involving multiple ERP systems, cloud platforms, supply chain applications, and HR systems, the challenge becomes even greater.<\/p>\n\n\n\n AI will need to operate across connected processes rather than within isolated applications.<\/p>\n\n\n\n That will require much closer collaboration between ERP teams, data leaders, IT departments, business units, and employees who actually use these systems every day.<\/p>\n\n\n\n One of the biggest barriers identified in the research was uncertainty around ROI.<\/p>\n\n\n\n This may become one of the most important factors separating successful AI programs from endless experimentation.<\/p>\n\n\n\n Organisations that define success only after deploying AI will struggle to determine whether an initiative has actually worked.<\/p>\n\n\n\n Instead, transformation leaders need to establish measurable outcomes before scaling.<\/p>\n\n\n\n Those metrics could include:<\/p>\n\n\n\n The most successful AI initiatives will likely be the ones that connect technology directly to a measurable business outcome.<\/p>\n\n\n\n Measurement discipline will be critical to scaling AI successfully.<\/strong><\/p>\n\n\n\n AI enthusiasm alone will not create business value.<\/p>\n\n\n\n The organisations that define clear objectives, establish baseline performance, and measure outcomes consistently will be in a stronger position to decide which AI initiatives deserve further investment.<\/p>\n\n\n\n The Infor index highlights an important reality for enterprise leaders.<\/p>\n\n\n\n The biggest obstacle to AI adoption may not be choosing the right model or finding the latest technology.<\/p>\n\n\n\n The bigger challenge is creating an environment where AI can operate safely and effectively within real business processes.<\/p>\n\n\n\n For ERP leaders, that means the foundation matters more than ever.<\/p>\n\n\n\n Strong data governance, stable processes, reliable integrations, clear accountability, and measurable outcomes are becoming prerequisites for successful AI adoption.<\/p>\n\n\n\n The companies that move beyond pilot purgatory will not necessarily be the ones experimenting with the most AI tools.<\/p>\n\n\n\n They will be the ones that successfully embed AI into the way their business already operates and can clearly demonstrate the value it creates.<\/p>\n\n\n\n As AI becomes more deeply connected to ERP, supply chain, finance, and workforce systems, the conversation is likely to shift from “What can AI do?” to a much more important question:<\/p>\n\n\n\n “Where can AI create measurable value, and are our systems ready to support it?”<\/strong><\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" Key Takeaways \u21e8 Many businesses are enthusiastic about AI and believe they have the skills to scale it, but a large number are still struggling to move beyond early-stage deployments. \u21e8 Successful AI adoption depends on more than technology. Stable ERP processes, reliable data, governance, and clear business objectives are becoming essential foundations. \u21e8 CIOs […]<\/p>\n","protected":false},"author":1,"featured_media":38,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14],"tags":[],"class_list":["post-37","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-infor"],"_links":{"self":[{"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/posts\/37","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/comments?post=37"}],"version-history":[{"count":3,"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/posts\/37\/revisions"}],"predecessor-version":[{"id":55,"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/posts\/37\/revisions\/55"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/media\/38"}],"wp:attachment":[{"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/media?parent=37"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/categories?post=37"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/erp-news.com\/index.php\/wp-json\/wp\/v2\/tags?post=37"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Where Embedded AI Is Already Delivering Results<\/h2>\n\n\n\n
Infor Focuses on Moving AI From Pilots to Execution<\/h2>\n\n\n\n
What This Means for ERP Leaders<\/h3>\n\n\n\n
ERP Readiness Will Become AI Readiness<\/h2>\n\n\n\n
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Measuring ROI Will Separate Real AI Programs From Hype<\/h2>\n\n\n\n
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What This Means for ERP Leaders<\/h3>\n\n\n\n
The Bigger Challenge Is Not AI Technology<\/h2>\n\n\n\n