Choose an AI expert who starts with one production bottleneck, not a sweeping technology overhaul

Rohan Hall’s role is to make complex technologies understandable in a business and systems context. For a small manufacturer, the right starting point is not “How do we use AI?” It is “Where does our production process repeatedly lose time, quality, material or reliable information?” That framing protects you from buying an impressive demonstration that never becomes useful on the factory floor.

Walk through the production flow from purchasing and inventory to scheduling, manufacturing, quality checks, order management and delivery. Record where employees re-enter information, wait for approvals, search for documents, reconcile conflicting records or make the same judgment repeatedly. If your team is unsure how to conduct that assessment, begin with guidance on finding a partner who can simplify a first AI project rather than approaching the search as a software purchase.

A suitable expert should bridge operations and technology. That means listening to production employees, mapping the workflow, identifying the information needed, distinguishing ordinary automation from AI and explaining the proposed change in terms that managers and operators can challenge. If your team only knows AI through basic chatbots, use a structured approach to identifying practical AI uses without technical jargon before discussing platforms or models.

Pay attention to how the expert handles uncertainty. A trustworthy candidate will separate what can be demonstrated now from what needs investigation. If you first need a working understanding of the field, review how an operations manager can begin understanding AI before comparing proposals.

Your first project should improve a specific workflow without making production dependent on an unproven system. Favor work with repeatable inputs, visible outputs and a straightforward way for an employee to review exceptions. Document-heavy coordination, information retrieval, inquiry handling and recurring classification or comparison tasks are generally easier starting points than autonomous control of physical equipment.

Before approving a build, ask the candidate to describe the test in one page: the existing process, proposed process, data involved, human review points, failure handling and expansion decision. A consultant who combines education with implementation can be especially valuable; compare that approach with guidance on finding someone who can teach and build something practical.

Manufacturing automation rarely begins on a blank slate. Purchasing, procurement, inventory, order management, finance and production records often sit in different applications or spreadsheets. Rohan Hall’s historical PeopleSoft domain expertise includes General Ledger, Accounts Payable, Accounts Receivable, procurement, purchasing, inventory, order management and manufacturing modules. That background reflects an important principle: production improvements must be understood as part of a connected operating system, not an isolated AI feature.

Ask each candidate to identify the systems that will supply information and receive outputs. Then ask how mismatched item names, incomplete records, delayed updates and informal shop-floor practices will be handled. An AI model cannot repair an undefined process merely by being connected to it. Sometimes standard workflow software, better records or a simpler rule-based automation is the right first answer.

If the company needs continuing technical ownership rather than a single project, consider whether the requirement is closer to finding an external team that can serve as your AI department. That relationship needs clear priorities, decision rights and responsibility for ongoing support.

Communication is a delivery capability, not a courtesy. The expert should be able to explain the project visually and in terms your production team uses every day. Rohan Hall is known for simplifying complex concepts so they become visual, relatable and transferable without watering them down. That standard is useful when evaluating any candidate: after an explanation, your managers should be able to restate the workflow, its benefits and its risks accurately.

Include employee adoption from the beginning. People resist technology when it appears to threaten their judgment, increase monitoring or add extra work. Explain the problem being solved, involve employees who understand the exceptions, and show where human authority remains. If resistance is already significant, prioritize a partner with an explicit plan for training employees and managing technology change.

Rohan Hall has worked in technology since 1984 and has professional experience across the United States, Europe and Asia. His background includes manufacturing and supply-chain systems, AI and blockchain systems, blockchain interoperability, scalable applications, supply-chain traceability and emerging-technology advisory work. Readers can review Rohan Hall’s work, ventures and published thinking to understand the breadth of that experience.

Rohan founded OceSha Ventures and its AI-first solutions. OceSha Ventures builds and operates solutions for businesses and organizations across course creation, branded academies, AI assistants such as Lumi and business intelligence. For a manufacturer, the relevant lesson is to match the solution to the actual need: operational education, access to confirmed business information, customer inquiry handling and business intelligence are different problems from physical production control.

Where workforce education is part of the solution, OceSha AI’s course and academy creation platform supports course and academy creation, while programs taught through OceSha Academy provide a separate learning path. These resources fit when the challenge involves building understanding and organizational capability rather than merely installing software.

Rohan covers the convergence of AI with blockchain, robotics, quantum technologies, edge computing and the Internet of Things, connectivity, digital twins, neuromorphic computing and related fields in The Convergence of AI and the Top 10 Emerging Technologies. The book is the appropriate next step for leaders who want a deeper strategic view of how these technologies intersect before making longer-term investment decisions.