AI-driven Audit Checklist Generation [use case]
Empower your audit team by adopting Gen AI to generate questions from past findings, occurrences, and areas of risk, offering improved compliance, safety, and operational efficiency.
As in aerospace and other industries, assurance audits play a critical role assuring compliance with safety regulations, quality standards, and operational procedures.
However, the effectiveness of audits can be enhanced using a Generative AI-based system to generate relevant questions based on previous audit findings and safety occurrences.
Today, this use case shall bring to you:
The opportunity
The solution
The benefits
The Risks and Mitigations
Conclusions
Let’s dive in! 🤿
The Opportunity
Assurance audits are in place for keeping safety, quality, and compliance within the aerospace industry.
With experience in managing auditor teams, I'm fully aware that maintaining updated and relevant checklists is labour-intensive, requiring a significant number of working hours. Additionally, changes in safety and operational nuances and emerging risks may inadvertently be overlooked by the auditor team.
This is an area where the door is open to enhance this process by empowering the auditor team through advanced technology, which makes it efficient, effective, and help cognitive offloading.
The Solution
The following multi-step process by a generative AI-based system would, thus, facilitate the assurance audits, by generating checklists leading to better preparation and execution by the auditor team:
Data Collection: Kicking off with the comprehensive collection of data derived from previous audit findings, safety occurrences, incident reports, quality controls and compliance records in order to build a base for proper analysis.
Pre-processing: It concerns to cleaning and organising the information collected in depth, excluding any duplicate and/or irrelevant data, and organising it according to the type of audit, safety categories, levels of severity, and operational areas to which they refer.
Train the Generative AI Model: Train a Generative AI model to work with pre-processed data for the recognition of patterns, trends, and common problems that can be identified within previous audits, safety occurrences and other safety or compliance related data.
Techniques such as retrieval-augmented generation are used to further improve the ability of the model to produce contextually relevant questions.
The deployed model will be used to generate questions in the audit.
The model, once deployed, will draw information relevant to the situation under scrutiny brought up from the databases, including the audit scope, safety concerns, and operational requirements, among others, to generate the questions that would cover areas of interest or potential risks or raise compliance gaps for rectification.
Auditors' Feedback: The feedback is provided by an iterative process where the auditors review the relevance and clarity of questions generated and topics they cover critically. The feedback is crucial for the refinement and improvement of the process of question generation.
Implementation by Audits: Auditors will actually be auditing through the questions generated by the AI for assurance. The questions will guide the auditor to inspect whether the requirements are being met and if they are sufficient, potential hazards, and how efficiently the existing mechanisms for reduction of risk are working.
Continuous improvement: The model performance, auditors' feedback, audit outcomes, and alignment with organisational objectives are continuously evaluated for better understanding of the Generative AI model. Continuously adding new data and insights to the model for accuracy and efficiency will update the model.
The Key Benefits
The following key benefits are identified:
Tailored Question Generation: Provides added focus and thoroughness in any audit by identifying better risk and non-compliance through the interpreted historical data leading to better informed strategies and outcomes of audits.
Efficiency Gains: The auditor team spend less time and effort formulating the audit questions, and therefore has much more of its skills and energies available to be devoted to critical analysis and strategic initiatives.
Compliance and Safety: It enhances compliance; thus, overall safety and operational resilience.
Retention and Knowledge Transfer: It supports the retention of organisational knowledge and insights of past audits and brings about continuity and consistency in practices throughout the organisation.
Risks and Mitigations
There are inherent risks though ranging from bias in AI-generated questions or potential missed problems that have recently come up and were not there in the past.
To reduce these risks, it is important to:
Continuously update and refine the AI model in view of new data, feedback, and the changing industry standards.
Combine the insights with human professional judgement, in turn ensuring an integrated and wholesome approach in the audit.
It shall combine robust validation and oversight mechanisms to monitor and correct for any biases or inaccuracies in questions that are generated.
Conclusions
In summary, integrating Generative AI into assurance audits can revolutionise aerospace safety practices and regulatory compliance in the aerospace industry.
By empowering the audit team tailoring questions, boosting efficiency, and enhancing safety and compliance.
While it brings immense benefits, careful oversight and continuous refinement are crucial to address potential biases and ensure accuracy.
The aim is augmenting the human, in this case, the auditor.
This is all for today.
See you next week 👋
Disclaimer: The information provided in the newsletter and related resources is intended for informational and educational purposes only. It does not constitute professional advice, and any actions taken based on the content are at the reader's discretion.