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How ready is your enterprise to implement AI?
24 weighted diagnostic questions across six pillars of the AI value chain. Uncover your organization’s strong points, weak points, whether your systems and data architecture need modernization, and which Zokforce consulting offerings address your gaps.
Your AI readiness report
Each pillar score is weighted by how much each question matters.
You are early on: build the basics, starting with systems integration, before AI goes into production.
Overall readiness is 2.0 out of 4, which we call Developing. Pillars below 2: 0 of 6. Weighted delivery risk: 67 out of 100.
- 01All six pillars score 2.0 out of 4.No pillar is ahead of the others.
- 02Biggest gap: systems integration, at 2.0 out of 4.Close it before anything goes into production.
- 02Start with: Legacy integration and agents.With every pillar level, it starts with the pillar that weighs most.
- 0 answers need urgent work
- 24 answers need improvement
- 0 answers are strong
Your readiness profile, against the production-ready line
Points inside the dashed line show where work is needed.
Source: your answers to the 24 questions, weighted by importance.
How far each pillar is from production-ready
Bars that stop short of the line are the gaps to close first.
Source: your answers to the 24 questions, weighted by importance.
What each score means, and the service that closes the gap
| Pillar | Score | Status | What it means | Service that fits |
|---|---|---|---|---|
| 01Strategy & use-case readiness | 2.0 | Developing | Workable, with gaps to close before you scale. | Readiness and governance |
| 02Enterprise architecture & integration | 2.0 | Developing | Workable, with gaps to close before you scale. | Legacy integration and agents |
| 03ADLC & engineering practices | 2.0 | Developing | Workable, with gaps to close before you scale. | AI-driven development (ADLC) |
| 04Data & LLM training / fine-tuning | 2.0 | Developing | Workable, with gaps to close before you scale. | Custom model fine-tuning |
| 05AI harness & observability | 2.0 | Developing | Workable, with gaps to close before you scale. | AI harness engineering |
| 06Governance, privacy & human sign-off | 2.0 | Developing | Workable, with gaps to close before you scale. | Readiness and governance |
Each pillar maps to one of our five services.
Source: your answers to the 24 questions, weighted by importance.
Your four weakest answers, and why they matter
- 01
Access to your core systems
AI can’t safely read from or write to closed older systems.
Your answer: level 2 of 4 · Legacy integration and agents - 02
Personal data out of training data
A model trained on records that still hold personal data can memorize and repeat them; only retraining undoes it.
Your answer: level 2 of 4 · Custom model fine-tuning - 03
Models draft, people sign
Letting AI commit records or decisions without a real human sign-off creates clinical, financial and legal risk.
Your answer: level 2 of 4 · Readiness and governance - 04
A measured workflow first
Without a measured starting point, an AI pilot can impress in a demo and change nothing in daily work.
Your answer: level 2 of 4 · Readiness and governance
Start with these four. Each one links to the service that addresses it.
Source: your answers to the 24 questions, weighted by importance.
Confirm these scores with a readiness engagement
In two to three weeks we check these scores with your people and systems, and turn them into a 90-day plan your leadership can approve.
Get the full report by email.
Your scores are above. We email you a summary: your overall score, your six pillar scores, where to start, and a link back to this full report.
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