Managing Legacy Knowledge Risk in Manufacturing
For many manufacturers, critical or “legacy” knowledge is not found in manuals, systems, or formal policies; it resides with experienced employees. This knowledge is a competitive advantage, but it becomes an operational risk when concentrated among a few people who are nearing the end of their careers. When a key employee retires or leaves, the company may have to rebuild years of experience and knowledge while still meeting customer demands.
This risk is becoming more prominent as the manufacturing workforce ages, experienced employees approach retirement, and turnover moves knowledge in and out of organizations more quickly. This is supplemented by continued hiring challenges and shorter employee tenure, making it difficult to replace legacy knowledge. Without a deliberate knowledge transfer plan, manufacturers risk losing this expertise faster than they can rebuild it.
Understanding Legacy Knowledge Risk
Legacy knowledge is practical information developed through experience but not fully captured in formal records. It may include troubleshooting methods, equipment history, customer requirements, sequencing, or even informal workarounds that have become embedded as part of daily operations.
This risk can be easy to overlook while an experienced employee is still available to answer questions, but it becomes more visible when others are unable to complete the work without that employee’s help. Like other internal control weaknesses, reliance on one individual creates a single point of failure. A strong control environment supports clear responsibilities and accountability; knowledge retention should be part of that environment.
The Operational and Financial Impact
When legacy knowledge is difficult to locate or leaves with an experienced employee, the consequences extend beyond temporary production delays. Undocumented processes can contribute to recurring troubleshooting, downtime, inconsistent service, and slower decision-making. Atlassian’s “State of Teams 2025” report illustrates the scale of this challenge, estimating that employees collectively lose 2.4 billion hours each year searching for information and that surveyed teams spend 25% of their workweek trying to find the knowledge needed to perform their jobs. Similarly, the Harvard Business Review article “How Knowledge Mismanagement Is Costing Your Company Millions” cites Bloomfire research indicating that knowledge-related inefficiency can cost a business an average of 25% of annual revenue. This reinforces that knowledge retention is not just a workforce issue but is certainly relevant enough to be a financial and operational priority.
Creating a Practical Knowledge Transfer Plan
Start with the positions and processes that would cause the greatest disruption if an experienced employee were unavailable. Determine whether someone else can perform the task, whether instructions match how it is performed, and whether records explain key decisions made along the way.
Manufacturers can then take several practical steps:
- Document critical procedures, decision points, and exceptions, rather than recording only the basic steps.
- Cross-train employees so that important activities are not dependent on a single person.
- Pair experienced employees with developing personnel through structured mentoring, job shadowing, and recurring review.
- Record equipment history, customer requirements, supplier context, and lessons learned in a consistent and controlled location.
- Assign ownership for reviewing and updating knowledge so that procedures remain current as operations and technology change.
The goal is to standardize and preserve how decisions are made, including when procedures apply, when exceptions require approval, and who should be consulted.
How Artificial Intelligence Can Help
AI can strengthen these knowledge-transfer efforts by turning items, such as maintenance notes, work instructions, and lessons learned, into organized and searchable resources. This allows employees to find and apply legacy knowledge easily across shifts, locations, and generations of a company’s workforce.
However, AI-generated content should be validated by experienced personnel, with approved records remaining the source of truth. The National Institute of Standards and Technology AI Risk Management Framework provides a useful model to achieve this. See here and here for specific real-world examples.
Preparing Before Knowledge Is Needed
Legacy knowledge creates value when properly shared and not concentrated with a few individuals. By identifying key dependencies before retirement or departure, manufacturers can reduce disruption and build a more resilient business. AI can support that process, but it should complement the experienced judgment from legacy employees.
Manufacturers should ask a simple question: If a critical employee was unavailable tomorrow, would the organization still have access to what that employee knows? Addressing that question today can preserve decades of experience for the next generation of the workforce.
Please reach out to the Manufacturing & Distribution team for more information on the topic outlined above. For more information regarding our Manufacturing & Distribution practice, visit our Manufacturing & Distribution industry page.
About the Author
Tanner Rock, CPA is an Assurance Manager, specializing in serving manufacturers and nonprofit organizations. He provides audit, assurance, and advisory services to a broad range of entities.… Read more
