Business Central AI readiness begins before a food manufacturer selects an AI tool. For many small and mid-sized companies, the first job is moving operational information out of paper records. Spreadsheets and disconnected accounting processes are part of the same problem.
Andrew Good is CEO of Liberty Grove Software. He said customers are asking how to prepare their information for AI while protecting the knowledge and proprietary data behind their operations. As a result, the emerging opportunity for Microsoft Partners spans ERP implementation, data structure, change management, use case planning and governance.
Business Central AI readiness begins on the shop floor
Liberty Grove works with Business Central customers in several specialized markets. They include food distribution and manufacturing. Its food customers include bakeries and companies working with meat and other proteins.
For example, some begin their ERP projects by recording production activities separately from their accounting systems.
“We often move them from a pen-and-paper-based system with back-end accounting to an integrated system so that the moment that something happens on the floor, it’s recorded in the system so they have real-time data to work with,” Good told PartnerTalks host Rick McCutcheon.
The goal is an end-to-end operating system suited to each company. Depending on the customer, the implementation can extend from manufacturing through distribution, delivery trucks and route planning. Liberty Grove also combines Business Central with products from ISVs, including Insight Works and Tasklet Factory.
As a result, this connected operating record provides the customer with current information for decision-making. It also contributes to the foundation required for Business Central AI readiness.
“Software is a part of the journey,” Good said. “Change management and cultural change is another part of that journey.”
Scattered information limits AI use
An ERP can provide an important source of structured operational data. However, a company’s information may also sit in SharePoint and other repositories. Therefore, Partners need to understand where it resides, what it represents and whether people can retrieve it reliably.
“Our customers are asking for guidance to understand how to structure data, how to have an enriched set of data,” Good said.
He added: “Some of it lives in SharePoint. So helping them structure that data so that it is discoverable, identifiable and usable by the large language models.”
For a Partner, Business Central AI readiness therefore includes data discovery and classification. In addition, a customer needs consistent terminology, ownership and access rules. Otherwise, a promising AI use case may depend on incomplete or poorly understood information.
Workshops turn interest into defined use cases
Liberty Grove is running what Good described as envisioning workshops. The sessions help customers examine their challenges and determine how AI could work with Business Central.
“Right now, people need encouragement to look at how to embrace the technology, how to identify use cases,” he said.
The workshops give Partners room to examine a business process before recommending technology. First, a clear use case can identify the necessary data and intended users. It can then define the expected outcome and controls required around the system.
PartnerTalks has also examined how Business Central AI agents are changing ERP delivery. Those agents could support project intake, setup and operating workflows. Similarly, Good’s approach emphasizes defining the work and the information surrounding it.
Meanwhile, governance belongs in the same conversation. The NIST AI Risk Management Framework organizes AI risk work around four functions: govern, map, measure and manage. Its accompanying playbook connects AI governance with existing data policies and data-quality standards.
Retirement creates a knowledge deadline
Business Central AI readiness can also become a workforce-planning issue. Experienced employees may understand production exceptions, customer requirements and informal procedures that have never been fully documented.
“Because perhaps you’ve got a lot of people on staff that have gray hair like myself and are nearing retirement,” Good said. “How can you leverage AI to ready your business for the future?”
His comments suggest a clear assignment for Partners. They can help customers identify knowledge embedded in people, processes and scattered files. Then they can decide how it should be documented and governed. A system needs access to usable information before it can retrieve the customer’s operational knowledge.
In turn, the work supports continuity beyond a single AI project. Structured process knowledge can improve implementation decisions, training and support as responsibilities pass to other employees.
Guardrails protect proprietary information
Good also framed Business Central as a way to establish boundaries around company information. He described those boundaries as “four walls around your data.”
“As you’re using your data with AI, you’re not leaking data out and enriching the public AI engines with your proprietary data,” he said.
The concern adds security and governance to the Partner’s role. Before deployment, customers need to know which information an AI system can access and where it travels. They also need to know who can use the resulting output.
Liberty Grove plans to continue developing its food-specific solutions. More customer requirements could then be available out of the box, reducing customization. Good also expects AI to help developers produce functionality more quickly while keeping it maintainable and testable.
For food manufacturers, the sequence is becoming clearer. Real-time operations create usable records. Data structure makes those records discoverable. Governance defines their safe use. Business Central AI readiness brings those pieces together before a customer asks AI to act on them.
