Enterprise Assessment · Data-Sleek

Enterprise AI Readiness
Scorecard

A 5-Pillar Diagnostic for Production-Ready AI — evaluate your organization’s AI maturity across governance, technology, data infrastructure, business impact, and talent.

88%

of AI pilots never reach production

95%

of failures trace to data readiness gaps

20%

of companies achieve significant ROI

AI success is not about experimentation. It’s about readiness.
Instructions

How This Scorecard Works

This scorecard is the self-service version of our full AI readiness assessment, which evaluates 39 indicators across the same five pillars. It is not a theoretical benchmark — it is a production viability check designed to surface the gaps that derail AI initiatives before they reach deployment.
01
Complete all 25 statements — 5 per pillar. Score each from 1 (not in place) to 5 (fully embedded).
02
Calculate your average score for each pillar by summing the five scores and dividing by 5.
03
Calculate your Overall AI Readiness Score by averaging your five pillar scores.
04
Locate your score on the maturity scale and read your recommended next steps.
05
Identify your lowest-scoring pillar — this is your highest-priority production risk.
Scoring Scale
1
2
3
4
5
1Not in place
2Early stages
3Partially defined
4Largely embedded
5Fully embedded
Important: The five pillars are not independent. A score of 4 on Technology with a score of 1 on Governance is not a net 2.5 — it is a governance failure waiting to happen. Your lowest pillar score represents your highest production risk, regardless of your overall average.
Curious about the methodology? Read why we score these five pillars before any AI roadmap.
↓   Start Assessment   ↓

Pillar 1
AI Governance & Executive Alignment
AI transformation starts at the top. Without governance maturity, even well-funded AI initiatives remain fragmented experiments.
Assessment Statement Score  (1 = None → 5 = Fully)
1. We have a documented AI strategy aligned to business objectives.(Required)
2. Executive sponsorship exists and is active for AI initiatives.(Required)
3. AI risk, ethics, and compliance policies are defined.(Required)
4. AI investments are prioritized through ROI modeling.(Required)
5. AI performance is reviewed at leadership level on a regular cadence.(Required)
Pillar 2
Technology & Architecture Readiness
Many organizations attempt AI before their architecture can support it. This pillar determines whether your infrastructure is production-grade or optimized for experimentation only.
Assessment Statement Score  (1 = None → 5 = Fully)
1. Cloud infrastructure supports AI workloads at scale.(Required)
2. MLOps processes are defined and operational.(Required)
3. Model deployment pipelines exist and are documented.(Required)
4. AI monitoring and performance tracking is automated.(Required)
5. Systems are scalable across business units, not just isolated pilots.(Required)
Pillar 3
Data & Infrastructure Maturity
This is where most enterprise AI initiatives quietly break down. Data quality problems don't announce themselves — they surface six months into a deployment. 95% of AI failures trace to this pillar.
Assessment Statement Score  (1 = None → 5 = Fully)
1. Data governance policies are standardized across the organization.(Required)
2. Data quality validation processes exist and are enforced.(Required)
3. Datasets are labeled, documented, and AI-ready.(Required)
4. Cross-department data sharing is enabled with appropriate access controls.(Required)
5. Secure data architecture supports AI training and deployment at scale.(Required)
Pillar 4
Business Impact & ROI Readiness
AI readiness means economic readiness — not technical novelty. This pillar assesses whether your organization has the discipline to prioritize use cases by value and measure what actually matters to the board.
Assessment Statement Score  (1 = None → 5 = Fully)
1. AI use cases are ranked by business value, not technical interest.(Required)
2. AI ROI metrics are tracked against forecasted projections.(Required)
3. AI initiatives are tied to measurable KPIs reviewed by leadership.(Required)
4. AI investments align with strategic growth areas and competitive positioning.(Required)
5. AI adoption demonstrably improves operational efficiency.(Required)
Pillar 5
Talent & Organizational Capability
AI is not just a technology investment — it is a workforce transformation. Organizations that treat it as purely the former consistently underestimate the resistance, skill gaps, and change management overhead.
Assessment Statement Score  (1 = None → 5 = Fully)
1. AI literacy exists at the executive level.(Required)
2. Internal data science capability exists and is sufficient for planned initiatives.(Required)
3. Engineering resources support AI deployment and ongoing operations.(Required)
4. Workforce upskilling programs are in place for AI-impacted roles.(Required)
5. Change management plans exist for AI adoption across the organization.(Required)
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