How to Use the AAQ Skill to Score Companies on AI Maturity
The AI Acceleration Quotient, or AAQ, is a repeatable methodology for scoring and ranking companies on their maturity in machine learning, generative AI, and agentic AI readiness. It helps analysts move beyond vague claims about “AI leadership” and produce evidence-based comparisons that can support strategy, research, due diligence, and competitive intelligence.
What the AAQ Skill Does
The AAQ skill scores companies from 0 to 100 across three dimensions:
| Dimension | Default Weight | What It Measures |
|---|---|---|
| Machine Learning and Advanced Analytics | 40% | Predictive models, optimization, computer vision, forecasting, NLP, and analytics embedded in operations |
| Generative AI | 35% | LLMs, copilots, custom assistants, AI-enabled knowledge systems, coding tools, and foundation model adoption |
| Agentic AI Readiness | 25% | Autonomous workflows, closed-loop systems, digital twins, real-time decisioning, AI governance, and infrastructure maturity |
The final AAQ score is calculated as:
Final AAQ = ML score x 40% + GenAI score x 35% + Agentic score x 25%
The result is a ranked view of which companies appear most mature in their practical use of AI, based on publicly available evidence.
How to Install the AAQ Skill
For any AI system that supports skills, custom instructions, projects, or reusable agents, use this folder structure:
aaq/
SKILL.md
references/
methodology.md
workflow.md
The main SKILL.md file should tell the AI when to activate the skill. A good trigger description is:
Use this skill whenever the user asks to score, rank, benchmark, or compare companies on AI capability, AI maturity, AI readiness, digital transformation, AAQ, AI scorecards, or the AI Acceleration Quotient.
The supporting files should contain:
| File | Purpose |
|---|---|
| methodology.md | Contains the scoring framework, weights, scoring tiers, and interpretation guidance |
| workflow.md | Contains the research process, source priorities, scoring steps, and output format |
If your AI platform does not support formal skills, paste the AAQ instructions into the system prompt, project instructions, or custom GPT/Claude project knowledge area.
How to Trigger the Skill
Use prompts like these:
Use the AAQ skill to score Microsoft, Google, Amazon, and Meta on AI maturity.
Rank the top defense primes using AAQ. Use deep research and standard weights.
Compare companies in the energy sector using the AI Acceleration Quotient.
Use AAQ to benchmark these companies on AI maturity: Company A, Company B, Company C.
Best Prompt for Fast Results
Use this prompt when you want a quick analysis:
Use the AAQ skill. Score these companies: Company A, Company B, Company C.
Use quick research, standard weights, and chat-only results. Return a ranked table with AAQ scores, dimension sub-scores, tier labels, short evidence notes, and the required AAQ disclaimer.
Best Prompt for High-Confidence Results
Use this prompt when you want a more credible analysis:
Use the AAQ skill. Deep research these companies: Company A, Company B, Company C, Company D.
Use standard AAQ weights unless there is a strong industry-specific reason to adjust them. Cite evidence for every score above 40. Return a ranked table, per-company rationale, cross-cutting observations, analysis date, and the AAQ disclaimer.
What the AI Should Ask Before Starting
A properly configured AAQ skill should ask four setup questions before scoring:
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Do you already have a company list, or should the AI select companies from an industry sector?
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Should the analysis use quick research or deep research?
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Should the analysis use standard weights or industry-adjusted weights?
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Should the output be chat-only, PowerPoint, Word report, or both?
This setup step keeps the analysis consistent, repeatable, and aligned with the user’s goal.
What Good AAQ Output Looks Like
A good AAQ result should include:
| Required Element | Description |
|---|---|
| Ranked table | Shows companies ordered by final AAQ score |
| Final AAQ score | Composite 0–100 score |
| Tier label | Interprets the score in maturity terms |
| Dimension sub-scores | Shows ML, GenAI, and Agentic AI scores |
| Justification | Explains why each company received its score |
| Observations | Highlights patterns across the companies |
| Analysis date | Makes clear the score is point-in-time |
| Disclaimer | Notes that AAQ is a starting point, not a definitive assessment |
Example output table:
| Rank | Company | AAQ | Tier | ML | GenAI | Agentic | Rationale |
|---|---|---|---|---|---|---|---|
| 1 | Company A | 78 | AI-Accelerating | 82 | 76 | 70 | Strong production ML, broad GenAI rollout, and early autonomous workflow evidence. |
| 2 | Company B | 63 | AI-Accelerating | 70 | 61 | 52 | Scaled analytics and GenAI pilots, but less evidence of closed-loop agentic systems. |
| 3 | Company C | 46 | AI-Adopting | 55 | 44 | 32 | Active AI experimentation with limited evidence of enterprise-wide deployment. |
AAQ Score Interpretation
| Score | Tier | Meaning |
|---|
| Score | Tier | Meaning |
|---|---|---|
| 81–100 | AI-Native | AI is embedded in core enterprise decision-making at scale |
| 61–80 | AI-Accelerating | Multiple scaled AI systems, expanding GenAI, early agentic capability |
| 41–60 | AI-Adopting | Active ML deployments, GenAI exploration, infrastructure in transition |
| 21–40 | AI-Emerging | Localized analytics, isolated pilots, foundational gaps |
| 0–20 | AI-Nascent | Little or no publicly visible AI capability |
The AAQ methodology is only useful as the start of a deeper discussion. Scores reflect publicly available evidence at a point in time and should not be treated as definitive assessments. For a more detailed analysis or due diligence support, reach out to the methodology’s authors at ooda.com.
Why This Matters
Many companies describe themselves as AI-enabled, but public evidence often tells a more nuanced story. The AAQ skill gives analysts a structured way to separate aspiration from operational reality. It rewards demonstrated capability, not hype, and it makes AI maturity comparisons more transparent, repeatable, and useful.
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