Infor Expands Industry AI With Shared Business Context and Built-In Governance
Key Takeaways
- Infor has expanded its Industry AI architecture and Velocity Suite with industry-specific AI agents, adaptive role-based interfaces and enterprise-wide governance controls.
- A YouGov survey of 2,111 business decision-makers found that 68% believe off-the-shelf AI does not adequately address their industry’s specific needs.
- Responsibility for AI governance remains divided across executive teams, IT leaders and dedicated committees, while 15% of respondents said accountability was unclear.
Infor has expanded its Industry AI architecture and introduced the latest release of Infor Velocity Suite, its package of AI capabilities for enterprise customers. The update brings additional industry-specific AI agents, adaptive interfaces that tailor information to individual roles, and governance controls designed to manage AI activity across the organisation.
The announcement coincides with the second edition of Infor’s Enterprise AI Adoption Impact Index, a survey conducted by YouGov on behalf of the company. The research surveyed 2,111 business decision-makers and found that 68% believe off-the-shelf AI solutions fail to meet the specific requirements of their industries.
Infor chief executive Kevin Samuelson highlighted the importance of business context, arguing that AI effectiveness depends on what models understand about an organisation and its operations.
For ERP teams, the development raises two important questions: how AI agents understand business processes and industry requirements, and who remains accountable for their decisions.
How Infor Gives AI Agents Industry-Specific Context
Infor’s architecture centres on a shared semantic layer called Infor IQ. It provides applications across a CloudSuite environment with consistent definitions of core business concepts, including products, locations and customers, while incorporating information specific to individual industries.
This context is particularly important when AI agents handle operational tasks. For example, an agent ordering raw materials for a food manufacturer needs to understand the Brix measurement used to assess sugar concentration. An agent working in automotive manufacturing, meanwhile, may need to interpret a vehicle identification number correctly.
Infor’s agents also draw on its Industry CloudSuites, Industry Process Catalogues and industry-specific language models. The company provides business-level process APIs, allowing an agent to complete an operation, such as creating a purchase order, through a single call rather than coordinating numerous technical requests.
Infor says this approach can reduce operational complexity, lower token costs and minimise the risk of errors.
The architecture also supports connections to non-Infor applications and third-party orchestration and analytics tools. Users can interact with the agents through the Infor GenAI Assistant or other AI assistants they already use.
Infor reports more than 350 pre-built use cases and customer results that include up to 60% faster shipment processing and time savings of up to 90% in selected procurement, supply chain, manufacturing and sales workflows. These figures represent reported outcomes rather than guaranteed results for every implementation.
Survey Highlights the Gap Between AI Adoption and Business Needs
Infor’s survey, conducted across the US, UK, Germany, France, Australia, Singapore and Japan in August 2026, suggests that industry-specific requirements remain a significant obstacle to wider AI adoption.
Concerns were particularly pronounced in operational sectors. According to the findings, 76% of distribution respondents, 73% of manufacturing respondents and 69% of retail respondents said generic AI solutions did not adequately meet their needs.
The research also points to growing confidence in autonomous AI. More than half of respondents, or 54%, said they were comfortable allowing autonomous agents to execute critical processes without human input at every stage. Meanwhile, 59% expected their organisations’ AI investment to increase over the following 12 months.
However, adoption has not eliminated the need for human oversight. Respondents estimated that 44% of AI-generated insights and workflows still required expert review or adjustment.
This highlights a challenge for ERP leaders. Introducing AI into everyday operations is only one part of the process. Organisations must also ensure that AI-generated outputs are reliable, that employees can intervene when necessary and that the appropriate controls are in place as automation expands.
Built-In Governance Does Not Remove the Need for Accountability
Infor has incorporated governance capabilities into the orchestration layer of its platform. These include human approval workflows, agent permissions, explainable AI logic and immutable logs recording agent activity.
The controls sit within a governance, risk and compliance layer at the core of the Infor Industry Cloud Platform.
Infor also advocates a zero standing privilege approach, under which AI agents receive access only for the task they need to perform. Permissions are withdrawn when the task is complete, helping to limit unnecessary access to business systems and data.
Yet technology alone cannot resolve the question of organisational accountability.
When survey respondents were asked who should be responsible for AI governance, 23% identified the chief executive or executive leadership, while 22% named the chief information officer or chief technology officer. A further 15% pointed to an AI committee, and 10% identified department heads.
Notably, 15% said there was no clear ownership of AI governance within their organisation. This figure rose to 21% in Japan, compared with 4% in Singapore.
Data security, data sovereignty and regulatory compliance were also significant concerns, with 33% of respondents identifying them as the biggest barriers to advancing AI.
Infor’s position is that governance should be embedded in the platform rather than treated as a separate consideration. However, businesses must still establish who is responsible for AI-driven decisions, how exceptions are handled and where human intervention is required.
How Infor Velocity Suite Fits Into the Strategy
Infor Velocity Suite brings together AI agents, process mining, automation capabilities and implementation expertise. The company offers the package for a flat annual fee rather than charging separately by user, bot or transaction.
This pricing model gives ERP teams an alternative to usage-based arrangements, particularly as the number of AI agents and automated workflows grows.
However, the most suitable model will depend on the organisation’s expected usage, implementation requirements and ongoing oversight costs. A flat annual fee may simplify budgeting, but businesses should also account for integration, training, exception handling and the human review that remains necessary for some workflows.
The wider strategy reflects a shift towards AI that is integrated with enterprise processes, business data and operational controls rather than operating as a standalone tool.
What This Means for ERP Leaders
1. Evaluate how AI agents understand business processes
ERP teams should establish where an AI agent obtains its understanding of products, customers, locations and industry-specific requirements. Testing agents against a process that relies on specialised operational data can help identify gaps before deployment expands.
A well-defined business context can reduce the need for manual corrections and costly workarounds.
2. Establish accountability before increasing autonomy
Organisations should appoint clear owners for AI-driven decisions and define which activities require human approval. They should also verify that the platform records agent actions and maintains an audit trail.
These measures provide process owners, compliance teams and auditors with a clear route to investigate decisions and address errors.
3. Assess the total cost of AI adoption
ERP leaders should compare flat-fee offerings such as Velocity Suite with usage-based pricing models. The assessment should include projected agent activity, implementation costs, integration requirements and the ongoing effort needed to review AI-generated outputs.
Understanding the full cost of adoption can help finance and technology teams build realistic budgets before expanding AI across the business.
The Bigger Picture
Infor’s expanded Industry AI architecture reflects a broader development in enterprise technology: moving beyond general-purpose AI towards agents that understand industry requirements, operate within established business processes and follow defined governance rules.
For ERP customers, the opportunity lies in connecting AI to the systems and data that underpin daily operations. But successful adoption will depend on more than the capabilities of the platform.
Organisations must combine relevant business context, reliable operational data, appropriate access controls and clear accountability. Without these foundations, greater AI autonomy could introduce new risks rather than deliver the expected efficiency gains.