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Infor adds industry-specific AI agents to Velocity Suite after survey finds 68% of leaders see generic AI gaps
Infor's Industry AI platform claims it processes shipments 60% faster and cuts auditing costs by 90%. A company survey of 2,111 business leaders found 68% say generic AI fails their industry's needs.
Infor rolled out updates to its Industry AI platform and Velocity Suite on October 6, 2026, at Velocity Week 2026 in Orlando, claiming its domain-specific AI agents can process shipments 60% faster and cut auditing costs by 90%. The announcement arrives alongside a company-commissioned survey of 2,111 business leaders that found 68% believe generic, off-the-shelf AI fails to address their industry's specific requirements.
The survey, which Infor calls its Enterprise AI Adoption Impact Index, spanned seven markets and revealed sharp dissatisfaction in manufacturing (73%), distribution (76%), and retail (69%). Only 10% of respondents said their organization assigns AI accountability to a Chief AI Officer. Data security and compliance concerns ranked as the top barrier to adoption, cited by 33% of those surveyed.
Four pillars built on domain data
Infor's Industry AI platform rests on four components: Precise Outcomes, which targets industry-specific results; the Velocity Suite, a bundled package of agents and automation; Process Intelligence, drawing on the company's operational data; and Augmented Intelligence, designed to assist rather than replace workers. The company said its agents use domain-specific models and process APIs rather than general-purpose large language models.
CEO Kevin Samuelson pointed to concrete use cases during the event. "Industry-specific knowledge-not generic models-drives value," he said, citing examples such as tracing contaminated food lots through a supply chain or adjusting freight pricing dynamically based on real-time conditions.
Flat-fee pricing aims to remove friction
The Velocity Suite now bundles AI agents, orchestration tools, and prebuilt use cases into a single flat-fee package with unlimited access. This marks a shift from metered or per-use pricing models that can make cost projection difficult for operations and IT teams evaluating AI Agent Courses and enterprise-scale deployments.
The flat-fee structure targets a common enterprise pain point: fragmented procurement and unpredictable costs when scaling AI across departments. Operations and product development leads, in particular, often struggle to forecast automation expenses under usage-based models.
Why this matters for operations, IT, and product teams
For operations and supply chain managers, the 60% faster shipment processing and 90% audit cost reduction are the numbers that matter-if they hold up outside Infor's existing customer base. The flat-fee model also simplifies budgeting conversations with finance, removing the variable-cost anxiety that stalls many automation projects.
IT and development leaders should note the survey's finding on accountability gaps. With only one in ten firms assigning AI oversight to a dedicated executive, the burden often falls on technical teams to evaluate domain fit, security, and compliance-areas where generic models underperform. For product and sales leaders, the industry-specific approach signals that vendors are moving beyond horizontal AI tools toward solutions that map directly to sector workflows, which may shift build-versus-buy calculations.