What generic AI tools are designed to do

Generic AI tools are designed to process prompts and generate responses based on broad, general knowledge. Their strength lies in producing text quickly, summarizing information, or assisting with general-purpose tasks. They aren’t embedded in certification-related environments and therefore lack the context needed to support how audits are planned, conducted, and reviewed.

When auditors prepare for an audit or assess findings, they rely on specific information such as audit history, scope, and documented findings. Generic AI tools can work with information that is manually provided, but that alone doesn’t make their output directly usable in certification work, which limits their ability to support these activities in a meaningful way.

Where generic AI falls short in certification workflows

This limitation becomes more apparent when generic AI is used in practice. Outputs may appear well-formed, but they aren’t connected to the underlying audit data. In certification workflows, where referencability, traceability, and consistency are essential, this creates clear limitations: 

• No connection to certification systems: Generic AI operates outside the platforms where audits and certification activities take place. 
• Limited certification context: Providing audit data manually doesn’t recreate the broader context in which audit and certification decisions are made.  
• Lack of traceability: Results can’t be easily linked back to underlying data or decisions. 
• Misalignment with workflows: Outputs are created outside the steps, roles, and review points that shape audit and certification work, which can require additional interpretation before they can be used.  
• Unclear data handling context: When used outside certification systems, it may be difficult to ensure how audit-related data is processed, stored, and separated. 

As a result, AI-generated content is hard to verify and not practical to use. Instead of fitting into the workflow, it introduces an additional layer that auditors need to validate separately. 

Why this matters for CBs

For CBs, this affects how easily AI can be integrated into day-to-day work. If outputs can’t be linked to audit data or aligned with existing workflows, they introduce additional effort rather than reducing it. Auditors may need to cross-check results, re-enter information, or manually connect outputs back to the certification context.

Over time, this limits the value of generic AI tools. While they may support isolated tasks, they don’t contribute directly to the structured processes that CBs rely on. As a result, these tools remain peripheral rather than embedded in how certification work is actually performed.