Industrial companies are under pressure to cover persistent labor gaps, digitize work, and increase output without adding the same amount of overhead. AI agents may help, but only when companies give them clear work, reliable information, and sensible limits.
In this episode of MetaPod, Ron Crabtree speaks with Len Landale and Scott McIsaac, co-founders of Helios Core, about what changes when software begins to act. Their message is practical. Start with repetitive work, document how it is really done, train employees, and measure the result before moving to more complex processes.
An AI agent does more than answer questions
A chatbot waits for a person to ask a question and then returns an answer. An AI agent can receive an event, decide what must happen next, use approved systems, complete an action, document the result, and escalate when it reaches a limit.
Landale and McIsaac use a password reset as a simple example. An agent can identify the request, verify the employee, unlock the account, confirm that access has been restored, and close the ticket. The employee gets help faster, and the IT team keeps its time for work that requires judgment.
The same pattern applies on the plant floor. If equipment telemetry indicates a loss of hydraulic pressure, an agent could gather the relevant logs and send a technician to the line with a likely cause and a list of parts to bring. Even when the agent cannot make the repair, better triage can reduce diagnosis time and downtime.
Start with high volume work that requires limited judgment
The best first use cases are usually frequent, necessary, and easy to verify. Password resets, ticket routing, routine pricing steps, after-hours monitoring, and predictable seasonal work all fit that pattern.
A narrow first project gives the team room to learn. It also makes results easier to measure. Companies can compare cycle time, service levels, errors, downtime, and cost before and after deployment. If the pilot works, the team can expand it. If it does not, the failure is contained and easier to diagnose.
Starting with a complex end-to-end workflow creates too many possible causes when something breaks. The agent may be working from incomplete data, unclear instructions, missing access, or an exception no one documented. A smaller use case exposes those gaps without putting a major business process at risk.
Buying an AI tool is not an AI strategy
Giving every employee an AI assistant may create useful experiments, but it does not tell the organization which problems to solve or how success will be measured. AI is a business issue, not a project that belongs to IT alone.
A workable strategy brings together people from operations, finance, HR, IT, security, and the functions that own the process. The group should identify where routine work consumes capacity, decide which outcomes matter, and set rules for data access and escalation. Employees also need training. Without it, even a capable tool sits unused or gets applied in risky ways.
Capture tribal knowledge before it leaves
Many industrial processes depend on an experienced employee who knows which material to select, how a customer behaves, or what a machine sounds like before it fails. That judgment may never appear in a procedure.
AI can help organize years of emails, quotes, service records, and completed work, but it cannot infer every company-specific rule. Teams still need to interview experienced employees, record why they make particular choices, and connect those choices to examples. If a retiring expert is the only person who knows the process, waiting means losing the best source of training data.
Make the process legible
An agent follows the instructions it receives. Humans often fill gaps without noticing them, so a procedure that seems clear to an employee may be ambiguous to software.
Map each step, decision, system interaction, handoff, and exception. Then test the written process with the literalness of someone who has no background knowledge. Landale compares this to asking a child to write instructions for brushing their teeth. If the instruction says to put toothpaste on the toothbrush, it still does not say where on the toothbrush it belongs.
Once the process is explicit, run many test cases before production. Those tests reveal edge cases and unclear rules. The team can refine the workflow until the agent handles normal cases reliably and sends unusual cases to a person.
Measure cost per outcome
Headcount is only one part of the cost. A missed access revocation can create a security exposure. A slow response to a failed production system can extend downtime. An incomplete employee offboarding process can leave accounts active across email, ERP, and other business systems.
The useful question is what it costs to produce a correct outcome. That measure includes labor, delays, errors, rework, downtime, and the consequence of failure. It also shows where an agent adds value even when no job is removed. Faster triage, better consistency, and wider service coverage may be the return.
Build security into normal use
Banning AI does not remove demand. It often pushes employees toward free consumer tools that the company cannot monitor. Landale and McIsaac recommend approved business or enterprise accounts, proper configuration, clear usage policies, training, and controls over where company data goes.
Security should grow with the agent's authority. A tool that summarizes a document carries less risk than an agent that changes ERP records or disables accounts. Permissions should match the task, and people should be able to review what the agent did.
Connect and learn more
The companies that gain the most from AI agents will be the ones that combine ambition with disciplined execution. They will begin with clear work, preserve the knowledge their people have built, test the process, and expand only after the result is proven.
To learn more, connect with Len Landale and Scott McIsaac through Helios Core at helios-core.com. You can also connect with Ron Crabtree on LinkedIn or contact the MetaPod team at MetaExperts.com.