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See Your
Last updated
See Your
AI Readiness Report
Last updated
This is a complete sample report. Your actual assessment will be customized to your organization with the same professional detail and actionable insights.
AI Readiness Assessment Report
Acme Corporation
Technology & Software Industry | 2,500+ employees | February 2026
72
AI Readiness
Overall Assessment: Advancing
Acme Corporation demonstrates solid AI readiness with strong strategy alignment and growing technical capabilities. The organization has moved beyond pilot phase with multiple production AI systems. Focus areas: scaling data infrastructure and expanding organizational skills.
Key takeaways
- This is a complete sample of the AI Readiness Report. Yours is customized to your organization.
- The report opens with an overall readiness score and level, plus an executive summary.
- It scores all 8 dimensions, with a deep-dive of findings and a recommendation for each.
- It compares your scores with your industry average and top performers (75th percentile).
- It ends with the top 5 prioritized recommendations, rated on effort and impact, and clear next steps.
8
Dimensions
Over 40
Questions
+14pts
vs Industry
Executive Summary
✓
Strong AI strategy with clear executive backing and well-defined use cases prioritized for ROI
✓
Modern technology stack with cloud infrastructure and investment in ML platforms ready for scale
→
Data governance framework exists but quality standards and accessibility need improvement
→
Skills gap: while data science talent is strong, broader organization lacks AI literacy
→
Opportunity: establish structured governance, upskilling programs, and COE to accelerate adoption
Category Breakdown
78
Strategy & Vision
72
Technology
65
Data
68
Skills
75
Process
71
Governance
64
Culture
69
Financial
What does each dimension deep-dive cover?
1
Strategy & Vision
- • Board-level AI steering committee established with quarterly reviews
- • Clear 3-year roadmap: Phase 1 (pilots) complete, Phase 2 (scaling) underway
- • Top 5 AI use cases identified with business case prioritization
- • Competitive positioning gap: limited innovation beyond core business optimization
Recommendation
Expand innovation focus beyond cost reduction to revenue-generating AI products. Establish dedicated innovation pipeline with quarterly hackathons.
2
Technology Integration
- • Migrated 85% workloads to AWS cloud with containerized deployments
- • SageMaker and Databricks platforms deployed for ML development
- • API-first architecture supports model serving; latency targets met
- • Legacy systems integration remains bottleneck for some use cases
Recommendation
Build API adapters for remaining legacy systems. Consider event-driven architecture for real-time model updates. Invest in API gateway standardization.
3
Data & Information
- • Data lake exists but lacks formal governance; data cataloging incomplete
- • Data quality inconsistent across source systems; no SLOs defined
- • Privacy controls adequate; security posture strong with encryption
- • Data accessibility limited; discovery tools not widely adopted
Recommendation
Establish Data Governance Council. Implement Collibra or similar tool for cataloging. Define data quality SLOs by domain. Train 50+ power users on discovery tools.
4
Skills & Literacy
- • 30+ data scientists and ML engineers; hiring competitive salary bands
- • Ad-hoc training only; no structured upskilling program for business users
- • Executive AI fluency varies widely; CFO and CRO weak on technical details
- • Certification programs exist but participation only 12% of eligible staff
Recommendation
Launch role-based training: (1) Executives—quarterly AI literacy bootcamps, (2) Managers—monthly use case workshops, (3) Analysts—self-paced cert with incentives.
5
Process & Operations
- • 2-week agile sprints for model development; cross-functional teams
- • CI/CD pipelines deployed; 80% of models deployed within 5 days
- • Change management process documented; adoption resistance managed
- • Model monitoring basic; retraining cadence ad-hoc not systematic
Recommendation
Formalize MLOps: establish model registry, automated retraining schedules, performance SLAs. Build 3-person MLOps team to standardize practices.
6
Governance & Ethics
- • AI risk framework exists; model risk review process formal
- • Compliance team engaged for regulated use cases (Finance, Healthcare)
- • Bias testing performed manually; no automated guardrails in place
- • Model explainability concerns raised but tooling limited
Recommendation
Invest in explainability tools (SHAP, LIME). Build automated bias detection into CI/CD. Create AI Ethics Board with Legal, Compliance, and Product leads.
7
Culture & Change
- • Some teams embracing AI; others skeptical or resistant
- • Success stories communicated; but ROI metrics not visible to all
- • Innovation encouraged in pockets; systemically risk-averse in others
- • Change fatigue present; previous transformation programs incomplete
Recommendation
Establish Centers of Excellence (CoE) for AI in key departments. Launch transformation office. Share ROI metrics quarterly with all employees. Recognize early adopters.
8
Financial Resources
- • $5M annual AI budget (0.8% of revenue); competitive for industry
- • Investment allocation: 50% infrastructure, 30% talent, 20% tools
- • ROI tracking informal; cost attribution unclear across projects
- • 2-year budget horizon; 5-year planning absent
Recommendation
Establish AI Finance Center. Track ROI by use case with standardized metrics. Plan 5-year budget with scenarios (conservative/optimistic). Quarterly forecasting reviews.
How does the organization compare with its industry?
Illustrative sample, not real benchmark data: Acme Corporation compared against a Technology & Software industry average and top performers (75th percentile)
Overall Score
72
Your Score
58
Industry Avg
82
Leaders
Strategy & Vision
78
You
62
Avg
88
Best
Data & Information
65
You
58
Avg
81
Best
Skills & Literacy
68
You
61
Avg
79
Best
What are the top 5 recommendations in the report?
1
Establish Data Governance & Quality Program
Low Effort
High Impact
Creates foundation for all downstream AI initiatives. Establishes data governance council, implements cataloging tools, and defines quality SLOs. Timeline: 3 months. Effort: 3-person team. Expected impact: +8-12 pts on Data dimension.
2
Launch Role-Based AI Upskilling Program
Medium Effort
High Impact
Addresses organizational skills gap and adoption resistance. Design tracks for executives, managers, and analysts. Partner with external training provider. Timeline: 2 months to launch. Effort: L&D team + subject matter experts. Expected impact: +10-15 pts on Skills dimension.
3
Formalize MLOps & Model Lifecycle Practices
Medium Effort
High Impact
Reduces deployment time, improves model reliability, and accelerates ROI realization. Implement model registry, automated retraining, and monitoring dashboards. Timeline: 4 months. Effort: Hire 1-2 MLOps engineers. Expected impact: +5-8 pts.
4
Create AI Centers of Excellence by Department
Higher Effort
High Impact
Decentralizes AI adoption, drives cultural change, and builds sustainable scaling. Establish CoEs in Finance, Sales, and Operations. Assign dedicated leads. Timeline: 6 months. Effort: Significant organizational restructuring. Expected impact: +8-12 pts on Culture dimension.
5
Implement AI Ethics Board & Governance
Medium Effort
High Impact
Ensures responsible AI deployment and mitigates regulatory risk. Create ethics framework, implement bias testing in CI/CD, and establish model approval workflows. Timeline: 3 months. Effort: Cross-functional taskforce. Expected impact: +6-10 pts on Governance.
| Measure | Your score | Industry average | Leaders |
|---|---|---|---|
| Overall score | 72 | 58 | 82 |
| Strategy & Vision | 78 | 62 | 88 |
| Data & Information | 65 | 58 | 81 |
| Skills & Literacy | 68 | 61 | 79 |
Frequently asked questions
What is in an AI Readiness Report?
An overall readiness score and level, an executive summary, a score for each of the 8 dimensions, a deep-dive with findings and a recommendation per dimension, an industry benchmark comparison, the top 5 prioritized recommendations and next steps.
Is this sample the same as the report I will get?
It is a complete sample report. Your actual assessment is customized to your organization, with the same professional detail and actionable insights.
How are the recommendations prioritized?
Each recommendation is rated on effort and impact. It shows a timeline, the effort involved and the expected score improvement for the dimension it addresses.
Who is my organization compared against?
Your industry average and top performers, defined as the 75th percentile, for your overall score and for individual dimensions.
How do I get my own report?
Take the free AI Readiness Assessment. It takes about 10 minutes, and you get your readiness score, dimension breakdown and industry benchmarks at no cost.
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