AI Readiness Assessment
An industry-neutral, vendor-neutral assessment of enterprise AI readiness across 10 dimensions — strategy, use case portfolio, data, platform, MLOps and LLMOps, governance, security, talent, adoption and AI FinOps. Score yourself free in 8–10 minutes, or bring in practitioners for the 2–4 week deep dive.
- Dimensions
- 10
- Questions
- 28
- Time to complete
- 8–10 min
Two levels. One methodology.
Level 1 tells you where you stand. Level 2 tells you what to change, why, and how to execute it.
Free Online AI Readiness Assessment
Understand where you stand.
Self-score 10 dimensions and get an executive dashboard immediately — overall AI readiness score, dimension scorecard, top gaps, execution readiness, a 90-day action plan and a 6–12 month roadmap. No email required.
- 28 questions on a 1–5 maturity scale
- Overall score 0–100 with a maturity level
- Top 5 gaps and top 5 opportunities
- 90-day plan and 6–12 month AI roadmap
Practitioner-Led AI Assessment
Understand what to change, why, and how to execute it.
Pronix.ai practitioners work across your business, data, engineering, architecture, security, risk and finance stakeholders to validate findings against real data, models, architecture and spend — then build the prioritized roadmap and business case.
- Discover → Assess → Analyze → Prioritize → Roadmap
- Data, platform and LLMOps deep dive
- AI opportunity heatmap with value vs. feasibility
- Target-state AI architecture, ROI and 6/12/18-month roadmap
Discover → Assess → Analyze → Prioritize → Roadmap
A repeatable five-phase method run by practitioners who have shipped and operated enterprise AI in production, not surveyors working from a questionnaire.
- 01 · Week 1
Discover
Stakeholder interviews across business, data, engineering, security, risk and finance. We collect the current AI inventory, data landscape, platform architecture and spend history.
Outputs (3) ▾
- Stakeholder map
- Data & system inventory
- Current AI footprint
- 02 · Week 1–2
Assess
Structured assessment of strategy, data foundation, platform and architecture, MLOps and LLMOps practice, governance, security, talent and adoption against the 10-dimension framework.
Outputs (3) ▾
- Maturity scoring
- Architecture findings
- Governance gap analysis
- 03 · Week 2–3
Analyze
Quantitative analysis of use case value, data readiness, delivery throughput and AI unit economics — cross-referenced with qualitative findings to separate symptoms from root causes.
Outputs (3) ▾
- Use case value analysis
- Cost & unit economics baseline
- Root-cause findings
- 04 · Week 3
Prioritize
Every opportunity is scored on business value, feasibility, risk and dependency, then sequenced with your leadership against real constraints — budget cycles, contracts, regulatory dates and delivery capacity.
Outputs (3) ▾
- Opportunity heatmap
- Top 5–10 prioritized initiatives
- Business case & ROI
- 05 · Week 3–4
Roadmap
A sequenced roadmap with target-state AI architecture, 90-day quick wins, operating model, governance and the measurement framework that proves benefit realization.
Outputs (3) ▾
- Target-state AI architecture
- 90-day quick wins
- 6/12/18-month roadmap
Frequently asked questions
What is an AI readiness assessment?
A structured review of whether your organization can build, govern and scale AI in production — across strategy, use case portfolio, data foundation, AI platform and architecture, MLOps and LLMOps, governance, security, talent, adoption and AI FinOps. Level 1 scores those 10 dimensions live in your browser; Level 2 is a 2–4 week practitioner-led engagement that validates the findings against your actual data, architecture and spend.
How long does the free online AI readiness assessment take?
About 8–10 minutes. 28 questions on a five-point maturity scale across 10 dimensions, scored instantly with no email gate. Your answers stay in the page link, so you can share the exact result with your executive sponsor.
Is the assessment vendor-neutral?
Yes. Nothing in the scoring assumes a specific cloud, model provider or data platform. It applies equally to AWS, Azure, Google Cloud, Databricks, Snowflake, OpenAI, Anthropic, open-source models and on-premise or hybrid estates — and to any industry.
What is the difference between Level 1 and Level 2?
Level 1 tells you where you stand. Level 2 tells you what to change, why, and how to execute it — a practitioner-led 2–4 week assessment producing a current-state assessment, data readiness review, platform and architecture review, AI opportunity heatmap, target-state AI architecture, business case and a 6/12/18-month roadmap.
Who should take part in the assessment?
For Level 1, score it with the AI or data leader, the platform owner and a business sponsor together. For Level 2, our practitioners work with business, data, engineering, architecture, security, risk and finance stakeholders.
What does the Level 2 AI readiness assessment cost and how long does it take?
Two to four weeks depending on scope, estate complexity and the number of stakeholder groups. Scope and pricing are proposed after a short qualification conversation — submit the form and a practitioner replies within one business day.
What the assessment measures.
Ten dimensions, weighted so that the ones which unlock everything downstream — data foundation, AI platform and MLOps/LLMOps delivery — carry more of the composite score.
View the 10 dimensions
- 01 · Strategy & Business Alignment
Whether AI is a funded, owned agenda tied to P&L outcomes rather than a portfolio of disconnected experiments.
- 02 · Use Case Portfolio & Value
How candidate use cases are sourced, scored on value and feasibility, and tracked against a defensible business case.
- 03 · Data Foundation & Quality
Whether the data AI depends on is accessible, governed, documented and good enough to trust in production.
- 04 · AI Platform & Architecture
The reference platform for building, serving and evaluating models and agents — versus shadow tooling per team.
- 05 · MLOps, LLMOps & Delivery
Whether models, prompts and agents ship under the same engineering discipline as the rest of production software.
- 06 · Governance, Risk & Compliance
Model risk, bias, privacy, auditability and regulatory alignment — the gate that decides how fast anything can scale.
- 07 · Security & Responsible AI
Controls specific to AI systems: data leakage, prompt injection, access boundaries, third-party model risk and content safety.
- 08 · Talent, Skills & Operating Model
Whether the organization has the people, pods and career paths to build and run AI without permanent vendor dependency.
- 09 · Change, Adoption & Process Redesign
Whether processes and roles are redesigned around AI, and whether people actually use what has been shipped.
- 10 · FinOps & Unit Economics
Whether the cost of AI per request, per workflow and per outcome is known, attributed and actively controlled.
Score your AI readiness
Answer as your organization operates today, not as it is described in a strategy deck. Scores update the dashboard live and stay in the page link, so you can share the exact result with your executive sponsor.
Strategy & Business Alignment
60 / 100
Whether AI is a funded, owned agenda tied to P&L outcomes rather than a portfolio of disconnected experiments.
Is there a documented AI strategy tied to measurable business outcomes?
Is there a single accountable executive owner for AI across the enterprise?
Are AI investments funded as a portfolio with stage gates rather than one-off projects?
0 of 28 questions answered (0%)
How candidate use cases are sourced, scored on value and feasibility, and tracked against a defensible business case.
Are AI use cases prioritized from data on value, feasibility and risk rather than opinion or vendor demos?
Does each funded use case have a business case with baseline metrics agreed with finance?
Are realized benefits tracked after go-live against the original case?
0 of 28 questions answered (0%)
Whether the data AI depends on is accessible, governed, documented and good enough to trust in production.
Can teams discover and access the data an AI use case needs without a multi-week request cycle?
Are data quality, lineage and ownership defined for the datasets feeding AI?
Is unstructured content (documents, transcripts, tickets) prepared and retrieval-ready?
0 of 28 questions answered (0%)
The reference platform for building, serving and evaluating models and agents — versus shadow tooling per team.
Is there a reference AI platform and architecture teams are expected to build on?
Are model access, routing and vendor abstraction centralized rather than hard-coded per app?
Is there a reusable retrieval, memory and tool-calling layer instead of per-project rebuilds?
0 of 28 questions answered (0%)
Whether models, prompts and agents ship under the same engineering discipline as the rest of production software.
Are prompts, models and agent flows versioned and released under change control?
Is there an automated evaluation and regression suite run before every release?
Is production behavior observable — traces, quality, drift, failure modes?
0 of 28 questions answered (0%)
Model risk, bias, privacy, auditability and regulatory alignment — the gate that decides how fast anything can scale.
Is there an approved AI policy covering acceptable use, human oversight and escalation?
Is there a production inventory of models, prompts and agents with risk classification?
Are bias, privacy and security reviews mandatory gates before production release?
0 of 28 questions answered (0%)
Controls specific to AI systems: data leakage, prompt injection, access boundaries, third-party model risk and content safety.
Are AI-specific threats (prompt injection, data exfiltration, tool misuse) tested and mitigated?
Do AI systems respect existing identity, permission and data-residency boundaries?
Are third-party model and vendor risks assessed, contracted and monitored?
0 of 28 questions answered (0%)
Whether the organization has the people, pods and career paths to build and run AI without permanent vendor dependency.
Do you have in-house AI, data and platform engineering capability rather than full vendor dependency?
Are AI delivery pods cross-functional, with product, engineering, data and domain expertise?
Is there a defined skills and enablement path for AI across technical and business roles?
0 of 28 questions answered (0%)
Whether processes and roles are redesigned around AI, and whether people actually use what has been shipped.
Are processes redesigned around AI rather than AI bolted onto the existing process?
Is adoption measured per use case, with intervention when it stalls?
0 of 28 questions answered (0%)
Whether the cost of AI per request, per workflow and per outcome is known, attributed and actively controlled.
Is AI spend attributed to workloads, teams and use cases rather than a single line item?
Are cost controls in place — model routing, caching, budgets and guardrails per workload?
0 of 28 questions answered (0%)
Executive results dashboard
Your overall AI readiness score, dimension scorecard, top gaps and the sequenced plan that follows from them.
Delivery is repeatable. Invest in LLMOps, evaluation and observability to move from pilots to a governed production portfolio.
Composite of data foundation, AI platform and architecture, and MLOps/LLMOps delivery — the three dimensions that decide whether a use case reaches production or stalls as a pilot.
10-dimension scorecard
Ordered by your weakest weighted dimensions first.
- Talent, Skills & Operating Model3.0 / 5
- Change, Adoption & Process Redesign3.0 / 5
- Use Case Portfolio & Value3.0 / 5
- Security & Responsible AI3.0 / 5
- FinOps & Unit Economics3.0 / 5
- Strategy & Business Alignment3.0 / 5
- MLOps, LLMOps & Delivery3.0 / 5
- Governance, Risk & Compliance3.0 / 5
- Data Foundation & Quality3.0 / 5
- AI Platform & Architecture3.0 / 5
Top 5 gaps
- 1 · Talent, Skills & Operating Model — 3.0/5
Delivery capability sits mostly with vendors, so knowledge does not accumulate internally and run costs stay high.
- 2 · Change, Adoption & Process Redesign — 3.0/5
AI is bolted onto unchanged processes and adoption goes unmeasured, so shipped capability produces no measurable benefit.
- 3 · Use Case Portfolio & Value — 3.0/5
Without scored prioritization and baselined cases, investment flows to what demos well rather than what pays back.
- 4 · Security & Responsible AI — 3.0/5
AI systems bypass or blur existing security and permission boundaries, creating exposure that traditional controls do not catch.
- 5 · FinOps & Unit Economics — 3.0/5
AI cost is neither attributed nor controlled, so unit economics degrade silently as usage scales.
Top 5 improvement opportunities
- 1 · Talent, Skills & Operating Model
Build a 4–6 person cross-functional AI pod per priority domain and put a role-based enablement path behind it.
- 2 · Change, Adoption & Process Redesign
Redesign the target process end to end around the AI capability and instrument adoption per cohort from day one.
- 3 · Use Case Portfolio & Value
Build an AI opportunity heatmap across the enterprise, score every candidate on value and feasibility, and baseline the top five with finance.
- 4 · Security & Responsible AI
Threat-model every production AI system, enforce per-user permissions on retrieval, and bring model vendors under standard third-party risk.
- 5 · FinOps & Unit Economics
Instrument per-request cost, attribute spend to use cases, and introduce cheap-first routing with per-workload budgets.
Recommended priorities & 90-day action plan
Sequenced from your three weakest weighted dimensions — moves that can start now without waiting on a platform program.
- Days 0–30 · Talent, Skills & Operating Model
Stand up one cross-functional AI pod — delivery lead, AI engineer, data engineer, domain owner — for the top use case.
- Days 30–60 · Change, Adoption & Process Redesign
Instrument adoption for every live use case and run a redesign workshop on the process with the largest gap.
- Days 60–90 · Use Case Portfolio & Value
Score your current AI backlog on value, feasibility and risk, and kill or park everything below the top five.
6–12 month AI roadmap
The structural work that follows the quick wins, ordered by dependency.
- Wave 1 · Talent, Skills & Operating Model
Scale to a federated model: a central platform team plus domain pods, with role-based enablement and defined career paths.
- Wave 2 · Change, Adoption & Process Redesign
Make process redesign and adoption targets mandatory parts of every AI business case and stage gate.
- Wave 3 · Use Case Portfolio & Value
Operate a quarterly value scorecard reporting realized benefit, unit economics and adoption for every production use case.
- Wave 4 · Security & Responsible AI
Embed AI threat modeling, content safety and vendor risk monitoring into the standard security operating model.
- Wave 5 · FinOps & Unit Economics
Operate AI FinOps: cheap-first model cascades, caching, monthly guardrails and cost-per-outcome reported alongside benefit.
The 2–4 week enterprise AI assessment.
A self-score is a hypothesis. Our practitioners validate it against your actual data estate, model inventory, architecture and AI spend — then produce the roadmap and business case your leadership can fund.
What we analyze in depth
Pronix.ai practitioners work with your business, data, engineering, architecture, security, risk and finance stakeholders across:
- AI strategy, ownership and funding model
- Use case portfolio and business cases
- Data access, quality, lineage and ownership
- Unstructured content and retrieval readiness
- AI platform, model access and architecture
- MLOps, LLMOps, evaluation and observability
- Governance, model risk and regulatory alignment
- AI security, privacy and responsible AI controls
- Talent, operating model and enablement
- Change, adoption and process redesign
- AI FinOps, unit economics and business case
Vendor-neutral by design
We do not resell platform licences or model capacity. The assessment applies to any AI and data ecosystem, including:
- AWS Bedrock & SageMaker
- Microsoft Azure & Azure OpenAI
- Google Cloud & Vertex AI
- Databricks
- Snowflake
- OpenAI
- Anthropic
- Open-source & self-hosted models
- Vector & retrieval platforms
- On-premise & hybrid estates
Executive deliverables you can act on.
Every engagement closes with a documented, board-ready pack — not a slide summary of workshop notes.
Current-State AI Assessment
A documented view of how AI is actually built, governed and run today across people, process, platform and data — validated with your teams, not inferred from a survey.
AI Maturity Score
Scored maturity across all 10 dimensions with peer context, showing where you lead, where you lag and which gaps are blocking the others.
Data Readiness Assessment
Assessment of access, quality, lineage, ownership and unstructured content readiness for the specific use cases you intend to fund.
AI Platform & Architecture Review
Vendor-neutral review of the model access layer, retrieval and orchestration services, environments, integration surface and technical debt.
MLOps & LLMOps Practice Review
Assessment of versioning, evaluation, release control, observability and incident response for models, prompts and agents in production.
Governance, Risk & Compliance Gap Analysis
Policy, model inventory, risk tiering, review gates and audit evidence assessed against your regulatory obligations and internal risk appetite.
AI Opportunity Heatmap
Every candidate use case scored on business value versus feasibility, so investment goes to what is provable rather than what demos well.
Top 5–10 Prioritized Initiatives
A ranked initiative list with expected impact, effort, dependencies, risks and the owner required to make each one land.
Target-State AI Architecture
Vendor-neutral target architecture across model access, retrieval, orchestration, data, evaluation, observability and governance layers.
Business Case & AI Unit Economics
Quantified benefit by initiative with cost-to-serve, assumptions, sensitivities and a payback view your CFO can interrogate.
90-Day Quick Wins
The moves that can start immediately without waiting on the platform program — sequenced, owned and measurable inside one quarter.
6/12/18-Month AI Roadmap
A phased roadmap with delivery waves, dependencies, operating model, resourcing and the benefit-realization measures for each wave.
Request a Practitioner-Led AI Readiness Assessment
Tell us where you are today. An AI practitioner replies within one business day with a proposed scope, stakeholder list and timeline for the 2–4 week assessment.