Infor Turns Industry Process Knowledge Into an AI Advantage

Infor’s Industry AI strategy combines industry process knowledge, process mining, AI agents, implementation support, and governance to help CloudSuite customers identify and scale high-value use cases. Its differentiation lies in the operational context embedded across industry workflows, data models, and security controls. Buyers should assess whether this depth improves accuracy, reduces implementation effort, and supports repeatable business value.

Infor’s strategy starts with the process, not the chatbot

This process-first position matters because the rest of Infor’s strategy depends on using operational context to decide where AI should act, how it should be governed, and how value should be measured. Infor’s October 2026 Enterprise AI Adoption Impact Index, based on a survey of 2,111 business leaders across seven countries, describes a post-adoption gap in which AI deployment is increasing faster than realized value. It attributes that gap to three issues: industry fit, cost predictability, and governance as agents gain greater autonomy.

• 68 percent of respondents said off-the-shelf AI did not meet their business needs.
• 59 percent expected AI investment to increase over the next 12 months.
• 88 percent considered predictable costs important.
• 33 percent cited data security, sovereignty, privacy, or compliance as their greatest barrier.

Taken together, the results suggest that buyers are willing to invest more, but not in generic AI with uncertain economics or weak controls. Infor is using that gap to position industry context, predictable pricing, and governance as requirements for moving from adoption to measurable operational value.

What Velocity Suite is, and who it is for

Velocity Suite is Infor’s AI and process-improvement package for CloudSuite customers. It combines industry AI agents, generative AI, process mining, automation, implementation expertise, and governance in one offering. Its promise is to help customers diagnose process friction, prioritize suitable use cases, deploy improvements, and expand them with more predictable cost and support. Organizations that already run important transactions in a multitenant CloudSuite are the clearest fit because their process models, security controls, data context, and execution environment are connected.

Infor’s adoption model connects AI to operational context

• Customers receive industry-focused AI agents and automation connected to CloudSuite workflows.
• Infor provides a library of industry AI use cases and agents that customers can adapt to their processes, data, roles, and governance requirements.
• Process mining and industry process context help identify delays, rework, and automation opportunities.
• Governance controls support traceability, auditability, and human oversight of agent actions.
• Implementation and ongoing support help customers progress from initial deployment to repeatable use.
• Forward-deployed engineers work with customer teams to refine, build, and expand AI use cases as part of an ongoing delivery model.
• Infor tailors applications to each user. A conversational interface embedded across most enterprise resource planning workflows lets users move from chat to detailed application views, while role-based workspaces highlight daily priorities.

Customer case study

Team Air Distributing. Infor’s published case study describes how the HVAC distributor applied agents and automation to credit management, inventory sourcing, accounts payable, and employee knowledge sharing after expanding from five to 18 branches in less than three years. Infor reports that credit inquiries were resolved 80 to 95 percent faster, inventory sourcing searches were completed 80 to 90 percent faster, and employees saved more than 2,000 hours annually. These vendor-published results illustrate one customer’s experience and should not be treated as a general performance benchmark.

From process evidence to prioritized use cases

Infor’s strategy starts with a process model, uses operational evidence to identify delay and rework, and matches those findings to its library of industry AI use cases and agents. The ontology provides the business context needed to adapt each use case to the customer’s processes, data, roles, and rules. Customers remain responsible for the financial assumptions and the decision to automate.

Process mining narrows the backlog to workflows where measured delay, exception volume, or rework may justify intervention. The business case is strongest when customers supply their own assumptions for volume, margin, risk, and labor.

Reported Benefits

• Prioritization. Process evidence and industry context create a disciplined backlog based on observed needs.
• Problem fit. A workflow-led approach aligns AI capabilities with the operating requirements of each process.
• Repeatability. Shared process context, governance, and implementation support reduce the effort required to expand from one use case to the next.

Architecture

Infor’s architecture is built around an industry ontology, a shared model of business entities, relationships, rules, and processes. This layer gives agents a consistent understanding of concepts such as customers, orders, inventory, approvals, and roles before they retrieve data or take action. Infor combines that ontology with industry process models and a semantic layer, then uses orchestration to coordinate work across applications and existing security controls to manage access. The agents are delivered and governed through Infor Velocity Suite, but they can orchestrate workflows across connected third-party applications. Those external processes still require secure integration, aligned identities and permissions, consistent data definitions, and clear ownership. Infor has also built integrations with Microsoft Copilot, Claude desktop, and AWS Quick, which allow customers to access Infor’s agents from within their own ecosystems.

Coming soon: Value Intelligence connects adoption data to business outcomes

Infor plans to introduce Value Intelligence as a self-service experience that gives customers a single view of where additional value may be available across their CloudSuite environment. The intelligence layer is expected to combine configuration data, adoption telemetry, process-mining insights, consumption data, industry benchmarks, and AI use-case activity to recommend and prioritize actions across optimization, adoption, and innovation.

Value Intelligence is expected to connect value discovery with execution by highlighting underused capabilities, configuration changes, process bottlenecks, relevant releases, and industry AI opportunities. It would then present a prioritized path based on the customer’s environment and business goals. The experience is coming soon. Supporting capabilities such as process mining, configuration scans, adoption assessments, and the AI Adoption Hub are already part of Infor’s customer success model.

What this means for Infor customers

• Choose a process with known volume, clear ownership, and an observable outcome.
• Define which actions require human review and which actions an agent may complete autonomously.
• Test integration outside the Infor estate before assuming the process is end to end.
• Measure realized value after deployment and use that evidence to decide whether to expand.

Organizations still running on-premises products should first assess cloud readiness, process standardization, data quality, and integration debt. Reproducing old customizations in a new environment can preserve the same constraints that limit artificial intelligence adoption.

Our Take

Many organizations move into generative AI before fully using the data, analytics, and predictive capabilities they already have. Enterprise AI programs should begin with trusted operational data and clear business goals for customer experience, revenue growth, and employee experience. Infor’s advantage is its industry ontology, which gives agents a shared understanding of business entities, relationships, rules, and processes. Combined with process mining, this context can make recommendations more relevant and actions more accurate because the AI operates within the customer’s workflow and security model.

Questions to test Infor’s AI value proposition

• How detailed is Infor’s industry process catalog for our micro-vertical, and does it describe work at the activity level?
• Can Velocity Suite use our process telemetry to recommend use cases and reveal bottlenecks that our teams have not already identified?
• Which value assumptions does Infor provide, and which inputs for revenue, margin, labor, risk, and adoption must the customer validate?
• What commercial, integration, and implementation requirements apply when a customer moves from the first Velocity Suite use case to the second and subsequent use cases?
• Which Infor and third-party systems can each Industry AI Agent access, who owns the agent, and can auditors export its registry and activity history?
• Does Velocity Suite enforce the same identity and least-privilege controls across the user interface, application programming interface, database, and connected applications?
• How does Infor validate agent calculations and recommended actions before they affect financial or operational records?
• How quickly can a business user configure a role-specific Infor workspace or agent experience, and what controls govern testing and promotion to production?

A practical path to enterprise AI value

To close the gap between AI adoption and business benefit, organizations should use process mining and predictive analytics to identify where operational friction is affecting cash, inventory, compliance, customer service, or employee productivity. They should then apply generative AI to a focused set of use cases with measurable outcomes, trusted data, accountable owners, and clear human oversight.

Infor’s advantage is the combination of industry context, process data, governance, and delivery support within one environment. Organizations using other platforms can follow the same model, although they may need to assemble these capabilities from several products.

Infor’s process-first strategy is credible because it grounds AI in operational evidence instead of treating the model as the starting point.

For CIOs, the test is whether Velocity Suite translates that foundation into more accurate recommendations, faster adoption, and measurable business outcomes across their processes and connected systems.