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AI Readiness Scorecard

How Ready Is Your Organization for Responsible AI at Scale?

Introduction

Artificial Intelligence isn't about adding tools — it's about transforming how your organization makes decisions.

Before you deploy copilots, LLMs, or automation layers, you must ask one question: Is your organization's foundation ready for AI to deliver measurable ROI — not just hype?

This scorecard helps you assess your AI maturity across six readiness pillars: Strategy, Data, Governance, People, Technology, and ROI Measurement.

SECTION 1

Organizational Strategy & Vision

Evaluate whether AI is aligned with real business outcomes.

Do you have an AI roadmap linked to corporate goals or OKRs?
Is there executive sponsorship or budget ownership for AI initiatives?
Are current KPIs designed to measure automation or data-driven decision-making?
Does leadership see AI as a core enabler (not an experiment)?
Have you defined "where AI shouldn't be used" (ethical boundaries)?

📍 Current AI Priority Area:

SECTION 2

Data Quality & Infrastructure

Assess the reliability, accessibility, and governance of your data.

Is your data centralized and version-controlled across departments?
Are key data sources labeled, tagged, and retrievable for contextual search?
Do you maintain metadata and data lineage documentation?
Do you have defined data retention, privacy, and access policies?
Is your current tech stack (ERP, CRM, BI tools) integrated for unified data views?

📍 Primary Data Environment:

SECTION 3

Governance, Compliance & Trust

Gauge whether your AI adoption is ethically sound and regulatory-ready.

Are you following frameworks like ISO 42001 (AI Management) or NIST AI RMF?
Do you track model bias, explainability, or fairness metrics?
Is there a data ethics committee or responsible AI policy?
Are security and privacy audits embedded in AI workflows?
Do you document AI decision logic for compliance or auditability?

📍 Industry Sector:

SECTION 4

People & Change Readiness

Determine if teams are prepared to adopt and scale AI effectively.

Are employees trained on AI literacy or tool usage?
Is there visible excitement (not anxiety) around automation initiatives?
Do you have champions or AI advocates within business units?
Are workflows redesigned to complement AI (not fight it)?
Is AI adoption part of performance or innovation KPIs?

📍 Department Most Eager to Adopt AI:

SECTION 5

Technology Readiness

Evaluate whether your current infrastructure can handle AI workloads.

Is your infrastructure cloud-ready and API-first?
Can your systems integrate with copilots, RAG pipelines, or MLOps frameworks?
Do you maintain model registries and versioning systems?
Have you tested AI integrations in sandbox or staging environments?
Are your vendor tools (CRM, ERP, etc.) AI-compatible or extensible?

📍 Primary Cloud Provider:

SECTION 6

ROI Measurement & Continuous Improvement

Identify how AI impact is tracked and sustained.

Are AI pilots tied to quantifiable KPIs (revenue, cost, efficiency)?
Do you have dashboards tracking AI adoption metrics?
Are ROI or value realization frameworks defined pre-launch?
Do you run quarterly reviews of AI success/failure patterns?
Do business leaders own the outcomes of AI investments?

📍 What Does AI Success Mean for You?

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Your AI Readiness Score

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