/ Case Studies / Preserving Critical Engineering Knowledge with an AI Assistant Case Studies Preserving Critical Engineering Knowledge with an AI Assistant How a Specialized Manufacturer Is Turning Institutional Expertise into a Governed, Searchable Engineering Resource Critical Issue A specialized manufacturing organization faced a significant business continuity risk as an experienced engineering subject-matter expert approached retirement. Critical knowledge existed across technical specifications, historical designs, testing information, troubleshooting experience, and years of undocumented decision-making. Finding the documentation was only part of the challenge. Engineers also depended on the expert’s ability to interpret requirements, identify relevant past work, evaluate test results, troubleshoot equipment, and recommend the appropriate next step. The organization needed to preserve both the technical information and the reasoning used to apply it. As part of a broader technology modernization initiative, the organization partnered with Concurrency to develop a production-ready Engineering and R&D AI Assistant grounded in approved engineering knowledge. The solution is designed to make critical expertise more accessible while maintaining security, source traceability, human oversight, and engineering authority. Customer Profile A specialized manufacturing organization whose operations depend on technical expertise, historical engineering information, and experienced subject-matter experts. The company needed a more sustainable way to preserve institutional knowledge and make trusted information available to current and future engineering teams. Key Problem The organization needed to preserve specialized engineering knowledge before it became less accessible through workforce transition. Engineers required a faster way to locate historical information, compare technical requirements, understand testing and troubleshooting methods, and apply approved knowledge to new R&D work. The solution also needed to operate within a highly controlled Microsoft 365 environment, respect existing permissions, cite authoritative sources, and support engineers without replacing qualified engineering judgment. BUSINESS CHALLENGES Institutional Knowledge at Risk Critical engineering knowledge was concentrated among a small number of experienced employees. Their expertise included not only documented technical information, but also the reasoning, methodology, and historical context used to solve complex problems. Fragmented Engineering Information Engineering content was distributed across documents, repositories, and legacy storage structures. The organization needed a focused knowledge architecture with clear metadata, permissions, search capabilities, ownership, and governance. Security and Engineering Control The AI assistant needed to respect established access controls and prevent unauthorized information exposure. It also required clear boundaries so it could assist engineers without approving designs, engineering changes, test procedures, or production instructions. Outcomes Preserved Engineering Intelligence Captured critical engineering methodologies, decision rationale, troubleshooting knowledge, and technical expertise in a governed, sustainable knowledge foundation. Faster Access to Trusted Knowledge Enabled authorized engineers to use natural-language search to find approved information, compare requirements, explore prior work, and access source-grounded guidance. Repeatable Knowledge Governance Established a controlled process to capture, review, approve, publish, maintain, and retire engineering knowledge as new expertise and lessons are developed. our solution Concurrency is helping the organization develop a focused Engineering and R&D AI Assistant supported by curated knowledge, a SharePoint-based repository, objective evaluation scenarios, governance processes, and operational handoff. The structured engagement includes design and validation, configuration and testing, a controlled pilot, deployment, hypercare, and knowledge transfer. Engineering Knowledge Discovery Conduct focused workshops to capture priority use cases, methodologies, reasoning patterns, authoritative sources, and validation requirements. Document how experts compare technical requirements, investigate problems, interpret results, and determine appropriate next steps. Prioritize high-value expertise rather than attempting to capture everything known by an individual. Validate discoveries and close critical knowledge gaps before configuration begins. Focused Engineering Use Cases Technical similarity analysis: Compare new requirements with approved historical information, explain similarities and differences, and cite supporting sources. R&D expert assistance: Answer recurring engineering questions using captured and approved methodologies while clearly identifying uncertainty. Controlled knowledge intake: Capture new troubleshooting information and lessons learned, route them through technical review, and publish approved content to the trusted knowledge base. Governed SharePoint Knowledge Foundation Configure a focused SharePoint repository for approved engineering knowledge. Define content organization, metadata, permissions, stewardship, and lifecycle practices. Maintain authoritative sources without creating another unmanaged document repository. Support ongoing knowledge validation, publication, maintenance, and retirement. AI Assistant Configuration and Validation Configure the assistant using Microsoft Copilot Studio. Establish instructions, grounding behavior, retrieval configuration, and context for the approved use cases. Test source traceability, permission enforcement, engineering scenarios, and response behavior. Use pilot feedback and evaluation results to guide production-readiness decisions and final configuration updates. Security and Operational Readiness Apply a documented, least-privilege access model. Validate user and group permission scenarios before production use. Define ownership, stewardship, escalation, maintenance, and support responsibilities. Deliver administrative guidance, operational runbooks, knowledge transfer, and future recommendations. Lessons Learned & Next Steps Capturing Documents Is Not the Same as Capturing Expertise Historical documents contain important facts, but expert knowledge also includes how information is interpreted, which evidence matters, what alternatives are eliminated, and when escalation is necessary. An effective engineering AI assistant must account for both approved content and the methodology used to apply it. Governance Determines Whether Knowledge Can Be Trusted Engineering information does not become trusted simply because it is available through an AI assistant. Content requires defined ownership, technical review, approval status, permissions, versioning, and lifecycle management. Human judgment remains essential for validating the knowledge and determining how it should be used. The initial engagement focuses on a limited set of high-value use cases rather than developing an enterprise-wide engineering knowledge platform. Future phases may expand the approach to additional experts, knowledge domains, repositories, and AI capabilities after the initial solution is evaluated and operational ownership is established. Conclusion For manufacturers with long-tenured technical experts, workforce transition can place valuable institutional knowledge at risk. Preserving documents alone does not preserve the experience required to interpret those documents, troubleshoot complex problems, or apply historical insight to new engineering challenges. By partnering with Concurrency, this organization is creating a governed Engineering and R&D AI Assistant grounded in curated content and captured expert methodology. The solution is designed to help engineers access trusted knowledge faster, preserve critical expertise, and establish a repeatable framework for future knowledge capture without replacing human review or engineering authority. Engineering Knowledge Management and AI Assistant FAQs How can manufacturers preserve engineering knowledge before experienced employees retire? Manufacturers can preserve engineering knowledge by capturing both authoritative documents and the decision-making methods experts use to apply them. This engagement combines focused interviews, structured methodology capture, approved content, technical review, and a governed AI assistant to make critical knowledge available to future engineers. What activities can an engineering AI assistant support? An engineering AI assistant can support technical comparisons, historical research, troubleshooting, test-information discovery, and access to approved methodologies. The assistant should identify uncertainty, cite supporting sources, and escalate when the available knowledge cannot support a reliable response. How does SharePoint support an engineering knowledge AI assistant? SharePoint provides a governed repository for organizing approved knowledge, metadata, permissions, ownership, and lifecycle information. A focused SharePoint knowledge architecture supports secure retrieval and ongoing maintenance without creating another unmanaged file repository. How can organizations validate an engineering AI assistant before production? Organizations can use representative engineering scenarios, source-traceability checks, permission testing, subject-matter expert review, and agreed acceptance thresholds. Testing should confirm that responses cite appropriate sources, respect access rights, identify uncertainty, and align with approved engineering methodology. Can an AI assistant replace engineering judgment? An AI assistant should not replace engineering judgment, approve designs, certify technical correctness, or issue production instructions. It should help authorized engineers locate and understand approved information while keeping technical review, approval, and engineering decisions with qualified people.
