AI Transparency Statement

IAI advises industrial organisations on responsible AI adoption. We hold ourselves to the same standards we recommend to our clients, including being precise about how we use AI in our own operations. This statement explains how AI is and is not used in the Industrial AI Index, how scores are calculated, and the limitations of the methodology.

This statement will be updated as our use of AI evolves and as the regulatory environment develops.

01 Our Position

We use generative AI to create imagery and to power our business operations. We say so here. We hold ourselves to the same standards we recommend to our clients, including being precise about how we use AI in our own operations.

02 How the Industrial AI Index Score Is Calculated

The Industrial AI Index is a self-reported diagnostic. Your archetype is determined by a weighted scoring formula applied directly to your 12 survey responses. Each question is assigned to one of four pillars. Your responses are scored against a defined scale and weighted according to the commercial and operational significance of each pillar at each maturity level.

No generative AI, machine learning model, or large language model is involved in calculating your score or assigning your archetype. The result is produced by a deterministic algorithm. The same responses will produce the same result every time.

03 How AI Is Used in the Industrial AI Index

AI tools were used in the research, copywriting, and development phases of building the Industrial AI Index. Specifically:

The diagnostic framework, archetype definitions, and pillar weightings were developed by Marnie Davey drawing on her industry experience and research. AI writing tools were used to assist in drafting and refining copy across the survey, results pages, and roadmap documents. All content was reviewed, edited, and approved by Marnie Davey before publication.
The scoring algorithm and results logic were built by human developers. No AI-generated code was deployed without review.

AI tools were not used to determine the scoring methodology, define the archetype bands, or produce the benchmark framework. Those decisions were made by people with operational and strategic expertise in industrial AI.

04 Limitations of the Methodology

The Industrial AI Index produces results based on self-reported information. Results are only as accurate as the responses provided. The diagnostic is designed to surface patterns and prompt useful conversations, not to produce a definitive or auditable assessment of organisational AI capability.

Results reflect your perception of your organisation's current state at the time of completion. Perception and operational reality do not always align.
The archetype bands are indicative ranges, not precise thresholds. The archetype is a starting point for conversation, not a certification.
The regional benchmark data is in its early stages. Comparisons to regional averages will become more meaningful as the dataset grows.
The diagnostic does not assess technical infrastructure, review documentation, audit systems, or verify any claim made in your responses. It is a structured self-assessment tool, not an independent review.

05 What You Should Do With Your Results

Your results are a diagnostic output, not a verdict. They are most useful as a structured starting point for a conversation about where your organisation is, where it wants to go, and in what order the work needs to happen.

IAI recommends treating your archetype and pillar scores as a framework for that conversation, with your leadership team, with your board, or with an operational AI adviser, rather than as a definitive statement of capability.

We do not claim the Industrial AI Index is a perfect instrument. We claim it is an honest one.