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AI Readiness · Platform

Maturity Scale.

The AI Readiness Maturity Scale™. This page is being prepared.

ReferencePLT/2026/07

Updated2 September 2026

StatusIn preparation

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PLATFORM

Introduction Why us (vs Int. / Ext.) CCDM (the engine) Landscape Discovery Digital Doorway AI Readiness Maturity Scale Misinformation Secure Tag PDF to HTML Schematic

USE CASES

Shareholder confusion Regulatory inquiry Clinical and licensing exposure Prospectus inaccuracies Intellectual property disputes Updates lost in templates Legacy document exposure Conflicting official positions

SECTOR ASSESSMENTS

Global Chemicals London listed (100/250) Medical Devices Fortune Global Higher Ed.

Articles (all)

AI Accelerator, misinformation and hallucinations 9 in 10 AI projects fail AI is collecting on your digital debt Why are organisations blocking the AI systems shaping how they are found, understood and represented? Looking both ways, and not being run over by AI The Triad of Exposure Evergreen PR, and the sector benchmark If AI Is the Front Door, What Does It See? Correct to People, Wrong to Machines: The Financial Reporting Control Gap Same exposure lands differently Visibility, amplifying poor governance Three stages, one order Protecting against AI Misinformation Automation supporting AI Governance

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AI usage declaration

AAAnow uses AI in the great majority of what it produces. Over 95% of our work carries AI usage elements [AAAnow internal data]. We use it for efficiency, to hold accuracy across large volumes of material, and to let us work at a scale that would otherwise be out of reach. We are open about this, and we set out below how it is done.

Our method breaks a piece of work into as many as 10 segments. The early segments are led by people, the middle segments are delivered by AI under direction, and the final segments are review. The balance is deliberate: AI does the work it is strongest at, and people hold the judgement at the start and the decisions at the end.

Understanding what needs to be done. The task is defined before anything is written, along with what it will be built from.
Why, how, and what. The purpose and the reader are settled, so the work serves them.
Mapping the work with AI. The shape of the piece is planned, and the sources it will rest on are named.
Research and evidence gathering. AI assembles the material at a breadth and speed no manual pass would match.
Research evaluation and source quality. Each source is weighed, and weak or second-hand material is set aside for primary evidence.
Validation of terms and definitions. The language is checked for consistent, correct meaning throughout.
Confirmation of citations and figures. Each claim is traced back to its source, and figures are checked against what that source actually states.
Drafting and structural assembly. The evidence is written up in sequence, to the plan set at the start.
Human review. A person reads the whole against purpose, accuracy, and standard, and directs the changes.
Final pass, often external. On a major document, a further review is run, frequently by an outside eye, before release.

The result is work produced with the reach of AI and the judgement of people, declared plainly so the reader knows how it was made.