Assessment

How Ready Is Your
Organization?

Most enterprises want AI.
Few have the data foundation to support it.
This assessment takes 10 minutes and covers the two things that determine whether your AI investment pays off: your Data readiness and your AI readiness.
Do one or both.

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Start Your Assessment

Fill in your details, pick your focus, and get your score in 10 minutes.

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Assessment Focus *

Data Readiness Only

AI Readiness Only

Both (recommended)

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1. Data Sources & Architecture
Where does your data live, and can you get to it?
Select all that apply

We have a documented inventory of our primary data sources (databases, SaaS platforms, spreadsheets, file shares)

Our data architecture has been reviewed or updated in the last 18 months

We have a target-state data architecture (data lake, lakehouse, or warehouse) that's either built or in progress

Critical business data lives in governed systems, not in scattered spreadsheets or personal drives

We can answer the question "where does [X data] live?" for our top 10 business processes without asking around

Our data is scattered across spreadsheets, personal drives, and disconnected systems with no central inventory

We haven't started organizing or documenting our data sources

2. Data Quality & Consistency
Can you trust what's in your systems?
Select all that apply

We have defined data quality standards (completeness, accuracy, timeliness) for our critical datasets

Someone owns data quality. There's a named person or team responsible for it

We run regular data quality checks, not just when something breaks

Our reporting numbers match across departments. Finance, ops, and sales look at the same data

We've addressed the "Budget_Final_Final_V2.xlsx" problem. Version control exists for business-critical files

Our data is often inconsistent — different departments report different numbers for the same metrics

We have no data quality standards or ownership in place

3. Data Governance & Compliance
Who can access what, and can you prove it?
Select all that apply

We have a data classification framework (public, internal, confidential, restricted)

Role-based access controls are enforced across our primary data systems

We can produce an audit trail showing who accessed what data and when

Our data handling meets the regulatory requirements for our industry (HIPAA, SOX, Basel III, PCI-DSS, or equivalent)

We have a data retention and disposal policy that's actually followed

Most people can access most data — we don't have clear access controls or classification in place

We have no formal data governance or compliance framework

4. Data Integration & Accessibility
Can your systems talk to each other?
Select all that apply

Our core systems (CRM, ERP, data warehouse, operational tools) share data automatically, not through manual exports

We use an integration platform (iPaaS, MuleSoft, or equivalent) rather than point-to-point connections

Business users can access the data they need without filing IT tickets or waiting for custom reports

Real-time or near-real-time data is available for the processes that need it

Adding a new data source or system connection takes weeks, not months

We rely heavily on manual exports, copy-paste, and email to move data between systems

Our systems don't talk to each other — everything is siloed

5. Data Team & Skills
Do you have the people to make this work?
Select all that apply

We have dedicated data engineering resources (internal or contracted)

At least one person on the team understands our cloud data infrastructure (AWS, Azure, GCP)

Our analysts spend more time on analysis than on data cleaning and prep

We have a data literacy baseline. Business users understand how to interpret dashboards and reports

There's a clear owner for data strategy at the leadership level (CDO, VP Data, or equivalent)

Data work falls to whoever has time — there's no dedicated data team or clear ownership

We don't have any data-focused roles or skills in the organization

Your Data Readiness Score

0.00

/25

1. AI Strategy & Use Cases
Do you know where AI creates value for your business?
Select all that apply

We've identified specific business processes where AI could reduce cost, increase speed, or improve accuracy

Our AI use cases are tied to measurable outcomes, not just "we should be using AI"

We've evaluated build vs. buy for our priority AI use cases

Leadership understands the difference between generative AI, predictive AI, and agentic AI, and knows which applies where

We have a prioritized list of AI projects ranked by business impact and feasibility

We know AI is important but haven't identified specific use cases or business outcomes for it

We haven't started thinking about AI strategy or use cases

2. AI Governance & Security
Can you deploy AI without exposing your organization?
Select all that apply

We have a policy for which data can and cannot be used to train or feed AI models

Our AI deployments use enterprise-grade, closed LLM configurations (not public-facing models with public-facing risks)

We've addressed information leakage risk. Our AI can't expose proprietary data to the open web

Governance rules differ by use case and data sensitivity level, not one-size-fits-all

We can explain how our AI reaches its outputs. Explainability and audit trails exist for high-stakes decisions

People are using public AI tools (ChatGPT, etc.) with company data and there's no policy around it

We have no AI governance or security policies in place

3. Human Oversight & Workflows
Is there a person in the loop where it matters?
Select all that apply

We've defined which AI decisions can auto-process and which require human review

Confidence thresholds are configured. Low-confidence outputs route to a reviewer, not straight to production

Reviewers are trained on what to look for, not just clicking "approve" on everything

We track approval rates and review times as health metrics for our AI workflows

Escalation paths exist for edge cases the model hasn't seen before

We either don't use AI in workflows yet, or there's no structured human review process for AI outputs

We have no AI-driven workflows or oversight processes

4. AI Infrastructure & Operations
Can you run AI in production, not just in a pilot?
Select all that apply

We have a cloud environment configured for AI workloads (GPU access, vector databases, model hosting)

Our AI models have version control, rollback capability, and deployment pipelines

We monitor model performance and can detect drift before users notice degraded outputs

Our data pipelines feed AI models with clean, current data on a reliable schedule

We've moved at least one AI use case from proof-of-concept to production deployment

We've experimented with AI in sandboxes or pilots but nothing is running in production

We have no AI infrastructure or operational capabilities

5. Organizational Readiness
Is your team ready to work with AI?
Select all that apply

Teams that will use AI tools have been trained on how they work and what their limitations are

We've addressed change management. People understand how AI fits into their daily work, not just that it's coming

There's executive sponsorship for AI initiatives with budget authority

We measure AI ROI with specific KPIs, not just "it feels like it's working"

We have a roadmap for scaling AI beyond the first use case

There's interest in AI from leadership but no training, change management, or dedicated budget yet

Our organization hasn't started preparing for AI adoption

Your AI Readiness Score