Industrial operations are being modernized through a combination of generative AI and process mining that uncovers inefficiencies in traditional business workflows. For decades, the backbone of many manufacturing and distribution powerhouses has remained rooted in the reliable, yet often isolated, IBM i environment. However, the introduction of the Infor Velocity Suite marks a significant shift in how these legacy systems interact with the modern digital economy. By moving toward an “agentic enterprise” model, companies are no longer just looking at dashboards for historical insights; they are deploying autonomous AI agents capable of executing complex business processes alongside their human counterparts. This strategy provides a lifeline for organizations running on-premise software like LX, XA, or System21, ensuring they remain competitive. Instead of mandating a high-risk, full-scale cloud migration, this approach offers a sophisticated hybrid path where the intelligence of the cloud meets the stability of proven on-premise infrastructure.
The Evolution of Industry-Specific Autonomous Intelligence
Specialized Agents: Targeted Solutions for Vertical Success
The strength of this suite lies in its granular focus on specific industrial sectors rather than offering a generic, one-size-fits-all artificial intelligence toolset. For every supported vertical, such as automotive, aerospace, healthcare, and distribution, the platform delivers five “shrink-wrapped” AI agents designed to handle specific operational challenges immediately upon deployment. In the distribution sector, for instance, these agents take charge of critical functions like purchasing, project management, and fixed asset tracking, significantly reducing the cognitive load on human managers. Within the automotive space, the focus shifts toward warehouse and logistics agents that optimize movement and storage in real-time. These agents are not merely chatbots; they are functional entities that can identify a shortage, suggest a procurement strategy, and even initiate the necessary paperwork within the ERP system, effectively acting as an extension of the workforce that never tires.
Synergizing Generative AI: Enhancing Workflows With Automation
Beyond the autonomous agents, the integration of generative AI within the ERP interface changes how employees interact with complex data on a daily basis. Users can now leverage advanced language models to summarize lengthy reports, translate technical documentation, or draft communications directly within their existing work environment. This capability is deeply intertwined with process mining tools that constantly analyze the flow of information to detect hidden bottlenecks or redundant steps that slow down production. Once these inefficiencies are identified, Robotic Process Automation comes into play to handle repetitive, low-value tasks like invoice entry or customer onboarding. By automating the “busy work,” organizations allow their skilled professionals to focus on strategic decision-making and creative problem-solving. This creates a cohesive ecosystem where process mining points the way, RPA handles the repetition, and generative AI simplifies the human-machine interface.
Integrating Legacy Architecture: Modern Cloud Frameworks
Hybrid Connectivity: Leveraging Multi-Tenant Infor OS
A major concern for many IBM i users has traditionally been the perceived requirement of a total cloud migration to access modern features. The current strategy addresses this by providing a hybrid path through the implementation of Multi-Tenant Infor OS, which serves as a powerful bridge between on-premise servers and cloud-based AI services. By utilizing Infor ION and Business Object Documents, companies can stream data from their legacy systems to the cloud environment where the AI agents reside. This setup allows a business to maintain its heavily customized XA or System21 environment while still benefiting from the high-speed processing and advanced machine learning capabilities of the cloud. It represents a pragmatic balance between preserving decades of investment in reliable hardware and adopting the latest software innovations. This architecture ensures that data remains synchronized across the entire enterprise, providing a single version of the truth regardless of where the core ERP is hosted.
Operational Excellence: Practical Implementation and Next Steps
Real-world adoption of these technologies already demonstrated clear benefits for early adopters during the initial rollout phase. Partners such as Guide Technologies played a pivotal role in helping XA customers integrate these sophisticated agents into their daily manufacturing routines without disrupting the existing supply chain. The focus for stakeholders shifted toward data hygiene and organizational readiness to ensure these AI agents had the high-quality information they needed to function effectively. The transition required a clear roadmap where businesses first mapped their existing processes using mining tools before choosing which specific agents to deploy. By following this structured path, companies successfully avoided the pitfalls of unmanaged automation and achieved a higher return on their legacy infrastructure. This initiative proved that the modernization of older environments did not necessitate a total, disruptive platform replacement but rather a thoughtful infusion of intelligence into existing frameworks.
