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# AI readiness assessment: Framework, checklist & scoring model

**[Netguru](https://daily.dev/sources/netguru)** · 14 min read · 0 upvotes · 0 comments

## Summary

A framework for assessing organizational AI readiness across six pillars: data readiness, strategy, governance and compliance, AI literacy, cloud/technical architecture, and use cases. It details a scoring model with five maturity stages (Fragmented, Piloting, Standardized, Scaled, Optimized), a diagnostic checklist, an EU AI Act-specific assessment process, generative AI-specific considerations (unstructured data, evaluation, prompt governance, variable cost), and practical guidance on cost ($15,000-$75,000+ for external engagements), timeline (two to six weeks), and choosing between self-assessment and external providers.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.netguru.com/blog/ai-readiness-assessment-guide>

## Questions this post answers

### What are the six pillars used in an AI readiness assessment?

The six pillars are data readiness and infrastructure, strategy, governance and compliance, AI literacy, cloud and technical architecture, and use cases. Each pillar gets scored separately on a 1-5 scale and rolls up into an organization-wide readiness rating mapped to a maturity stage such as Fragmented, Piloting, Standardized, Scaled, or Optimized.

_Teams weighing where to start an AI initiative can find comparable frameworks curated on daily.dev._

### How much does an external AI readiness assessment typically cost?

External AI readiness engagements typically run $15,000-$75,000 for a mid-market company and $100,000 or more for an enterprise-wide review. Cost scales with scope rather than headcount, driven mainly by how many business units, data domains, and cloud environments fall inside the review, with multi-cloud, multi-data-island engagements taking four to six weeks versus one to two weeks for a single business unit and cloud.

_Budget planners comparing consulting quotes for AI initiatives can track this kind of benchmark data on daily.dev._

### What changes in an AI readiness assessment when the use case involves generative AI or LLMs instead of classic machine learning?

Generative AI shifts the review to four areas: unstructured data (documents, tickets, wikis feeding retrieval-augmented generation) becomes the main asset instead of structured tables; evaluation requires checking faithfulness, relevance, and harmful outputs against a maintained test set rather than a single accuracy score; governance moves to prompt and response logging and access controls; and cost becomes variable with token-based pricing tied to usage rather than fixed server spend.

_Engineers scoping an LLM rollout can follow evolving generative AI readiness practices on daily.dev._

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---

Tags: [#genai](https://daily.dev/tags/genai), [#data-management](https://daily.dev/tags/data-management), [#ai-governance](https://daily.dev/tags/ai-governance)

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