AI readiness assessment / EAR Index™
Before spending another dollar on AI, know where your company stands.
The EAR Index™ (Enterprise AI Readiness) measures all 8 pillars at once: strategy, data, ROI, risk governance, leadership, processes, experimentation and people. The result is not a number to file away. The result is the two things you must do first.
Free, 8 questions, about 5 minutes, instant results. No registration.
The EAR Index™ is a self-assessment tool for enterprise AI readiness, developed by Trần Tấn Sỹ in the book AI First Business. It scores 8 pillars on a 1–5 scale, converts them by weight to a 100-point scale, places the company in one of four maturity levels and identifies the two pillars to prioritize within 90 days.
The same AI tool makes one company money and costs another.
The difference is not in the tool. It lies in whether the data is ready, whether processes are clear, whether middle managers know how to delegate work to AI, and who is accountable when AI gets it wrong. The EAR Index™ forces leadership to answer those questions with evidence, not gut feeling.
Eight pillars
| Pillar | Weight | The question leadership must be able to answer |
|---|---|---|
| 1. Strategy and business goals | 12% | Is each AI initiative tied to a business metric and a stopping condition? |
| 2. Data, context and infrastructure | 13% | Does the core data have an owner, and has it been tested in Vietnamese and in the industry context? |
| 3. Unit economics and ROI discipline | 10% | Does each project have someone accountable for the numbers, tracking both direct benefits and accumulated assets? |
| 4. Governance of rights, risks and the AI lifecycle | 12% | How far can each AI agent decide on its own, based on what evidence, and who has the right to adjust it? |
| 5. Leadership capability and change management | 15% | Do middle managers know how to divide roles between people and AI in their teams? |
| 6. Operations and process redesign | 15% | Does each division have at least one AI application running for real and measurable? |
| 7. Experimentation, evaluation and learning | 10% | Is there an experimentation space with a baseline, stopping criteria and a scaling mechanism? |
| 8. People capability and readiness for change | 13% | Does the team have role-based skills, and is it safe enough to try, fail and learn? |
Score what has been done, not what is intended.
Each pillar is scored from 1 to 5 based on what has actually happened in the company: 1 means not started, 3 means piloted with initial evidence, 5 means deployed across the organization with a measurement mechanism. Scores are weighted and converted to a 100-point scale.
No level is fixed. A company that focuses its effort on its two weakest pillars will move up a level after two or three scoring cycles.
| Score | Level | Meaning |
|---|---|---|
| 80–100 | AI-First Scaled | Many core processes have been rebuilt around AI and can be scaled to new departments |
| 65–79 | AI-First Operational | AI has created real value in some areas, with gaps remaining in integration, skills and scaling |
| 50–64 | AI-Enabled Fragmented | AI is used, but each department does it differently and value is uneven |
| Below 50 | AI-Exploratory | Mostly experiments and personal tools, not enough evidence to scale |
One scorer gives an opinion. Six scorers give the truth.
Score it alone for a first impression. To make decisions, invite six people to score independently: a leadership representative, finance, technology, a business unit head, HR, and at least one non-management employee. The gap between the CEO's score and the front line's is often the most valuable finding of the whole assessment.
After the results: 90 days
- Days 1–30: Diagnose. With the EAR score as a baseline, choose the two weakest pillars and assign each to an accountable owner with a deadline.
- Days 31–60: Strengthen. Each priority completes at least one small, measured experiment.
- Days 61–90: Measure. Score a second time. Decide to scale, adjust or stop. Choose two priorities for the next cycle.
GoAI experts can work with you to turn this framework into a concrete plan for your company, free of charge.
FAQ
How does a self-assessment differ from an assessment with an expert?
A self-assessment gives the initial picture. An assessment with an expert goes into real processes, data and systems, and ends with a list of priority use cases and initial measurements for each.
Do I have to leave my contact details?
No. Results appear as soon as you finish. You only leave an email if you want an additional detailed report, also free.
Does a low score mean we should not do AI yet?
The opposite. A low score tells you where to start, and the earlier you start, the cheaper it is.
How often should we re-score?
Every two quarters.
Is the EAR Index™ an international standard?
No. It is a diagnostic tool built by the author from practice and research, used for decision-making, not certification.
Source: AI First Business, Chapter 12 and Appendix A.