AI for Infor M3: when to use Velocity Suite, extend it or build custom agents

For Infor CloudSuite M3 customers, a useful starting point is to test whether an existing capability can handle the workflow. Extend it where a defined gap remains. Build a business-specific agent when your requirements justify the development and ongoing responsibility.

The choice depends on process fit, reliable data and who will maintain the solution. Custom agents can also be built within Infor's platform, so the decision includes both what to build and where it should run.

On this page: Compare the options · Data requirements · Truck-load planning · Your first assessment

Compare the options

These approaches can coexist. One process may use a standard capability for document handling, an integration for data access and a custom component for a particular decision.

When should you start with Infor Velocity Suite?

Start with the available catalogue when the task follows an established process. Infor Velocity Suite brings together capabilities including Industry AI Agents, GenAI, Process Mining, Value+ automations and RPA.

Check whether a relevant capability handles your actual documents, process variants and exceptions. Ask for a demonstration using representative inputs. Include difficult cases: incomplete information, conflicting values or an action requiring approval.

Before choosing a capability, check that it is available for your CloudSuite M3 environment and what licensing, configuration and data access it requires.

A standard solution is a strong candidate when it passes those checks and the team can operate it. Assess the implementation effort alongside the subscription, including data preparation, testing and user adoption.

When does an extension make sense?

Consider an extension when you can describe exactly what is missing from an otherwise suitable capability. Perhaps it needs another approved data source, a business-rule check or an additional workflow step.

Infor Agent Factory supports custom agent creation, and Infor's agent platform supports connections to both Infor and non-Infor applications. Evaluate those options before introducing another platform to handle work across systems.

Ask the implementation team to demonstrate the proposed extension. Check the available APIs, permissions and failure handling. Establish who will retest it when a connected system changes.

Keep the extension focused on the missing capability. If it grows into a substantial application with its own rules and operating needs, reassess the architecture and cost before expanding it further.

When is a business-specific agent worth building?

A custom agent is worth evaluating when available capabilities cannot handle business rules that are important to your operations. For example, a planning decision may need to balance customer commitments, transport constraints and available capacity in a way that standard functionality does not support.

Describe that logic before selecting the technology. What must the solution know? Which decisions may it prepare? What may it change, and when must it return the task to a person?

Then assess where the agent belongs. An Infor-based implementation, an existing enterprise platform or a separate component may fit different requirements. Compare access to data, integration effort, control over changes and the team's ability to support it.

Budget for operation as well as development. Include monitoring, model or service consumption where applicable, integration maintenance, regression testing and user support. A useful prototype still needs an owner who can keep it reliable in daily work.

Data requirements

Every option depends on data fit for the task. For a planning workflow, establish which quantities are current, which dates the business relies on and which constraints take priority. Define who resolves missing or conflicting information.

A shared data platform can be useful when several workflows need consistent information across systems. For a first use case, focus on the data needed to make that specific decision reliably. Consider what other teams will need to reuse before building separate connections and definitions for each workflow.

Elvenite's Data Intelligence team helps connect and prepare ERP data for analytics and operational AI.

Truck-load planning: how to choose an approach

Consider a planner who needs to combine order lines into truck loads while respecting weight, volume and delivery constraints. The task is to prepare a workable load plan that the planner can review before any changes are made in M3.

First, test whether existing planning functionality can produce the required plan. If it handles the constraints and exceptions, focus on making it usable in the planner's daily work.

If the calculation works but the planner still gathers information manually, an extension could connect the missing data or add a review step. An agent might help explain the proposed load and identify information that needs checking.

If the planning logic itself is missing, evaluate a custom component. Keep the calculation and the agent's role clear: optimisation software or rules may calculate the load, while an agent gathers inputs, presents the proposal and coordinates an approved update.

Begin with load proposals that a planner approves. Compare preparation time, the number of corrections and compliance with loading constraints against the current process. Use those results to decide whether to expand the scope.

Your first assessment

Bring the process owner and implementation team together around one workflow. Record answers to these questions:

1. What should improve? Choose a measurable result, such as shorter handling time or fewer corrections, and record the current baseline.
2. What already fits? Test available capabilities against normal work and important exceptions. Document the remaining gap.
3. What must be connected? Identify the required data, business rules, access permissions and updates to M3 or other systems.
4. Where does a person decide? Define approval points, exception handling and how an incorrect action would be stopped or corrected.
5. Who owns the running solution? Agree responsibility for cost, support, testing and improvement before expanding the scope.

Use those answers to select the smallest implementation that meets the workflow's requirements. Expand when the results and operating experience justify it.

Discuss your M3 workflow

Elvenite combines M3 process knowledge with data, integration and AI development. Explore our AI agents for Infor M3 in CloudSuite or bring us one workflow to assess together.

We can help identify the relevant capabilities, data dependencies and level of control, so the next investment has a clear purpose in daily operations.

Discuss your M3 AI use case