AI Readiness Assessment

Data Infrastructure

How would you describe your organization's data collection and storage capabilities?

No centralized data storage; data is scattered across systems

Basic data storage exists but lacks organization and accessibility

Centralized data storage with some governance policies in place

Well-organized data infrastructure with clear governance and quality standards

Advanced data platform with real-time processing, high quality, and comprehensive governance

What is the quality and accessibility of your organization's data?

Data is siloed, inconsistent, and difficult to access

Data exists but requires significant cleaning and integration efforts

Data is moderately clean and accessible with some integration tools

High-quality data with established pipelines and accessibility tools

Enterprise-grade data quality with automated validation, lineage tracking, and self-service access

How mature is your data analytics capability?

Limited to basic reporting; no advanced analytics

Descriptive analytics with standard dashboards and reports

Diagnostic analytics with some predictive modeling capabilities

Advanced predictive analytics integrated into business processes

Prescriptive analytics with AI/ML models driving automated decision-making

Team Skills & Capabilities

What is the current level of AI/ML expertise within your organization?

No AI/ML expertise; would need to hire or train from scratch

A few individuals with basic AI/ML knowledge or interest

Small team with practical AI/ML experience on pilot projects

Established AI/ML team with proven track record on production systems

Center of excellence with deep expertise across multiple AI disciplines

How would you characterize your organization's data literacy across teams?

Very low; most teams don't work with data regularly

Limited to specific analytical roles; basic spreadsheet usage elsewhere

Moderate; most teams can interpret reports and dashboards

High; teams actively use data for decision-making and hypothesis testing

Advanced; data-driven culture with widespread analytical capabilities

What resources are available for AI training and development?

No formal training programs or development budget

Ad-hoc training opportunities; limited budget allocation

Dedicated training budget with some structured programs

Comprehensive training program with external partnerships and certifications

Strategic learning initiatives with innovation time, conferences, and continuous development

Process Maturity

How mature are your organization's software development and deployment processes?

Ad-hoc development with manual deployment processes

Basic version control and deployment scripts

CI/CD pipelines with automated testing for most projects

Advanced DevOps practices with infrastructure as code and monitoring

MLOps capabilities with automated model training, validation, and deployment

What governance and compliance measures are in place for AI systems?

No specific AI governance framework or policies

Awareness of AI ethics but no formal policies or oversight

Basic AI governance policies with review processes for new projects

Comprehensive AI governance framework with dedicated oversight committee

Enterprise-wide AI governance with automated compliance checks and regular audits

How does your organization approach experimentation and innovation?

Risk-averse culture with no formal innovation processes

Occasional innovation initiatives but limited support or resources

Structured innovation program with dedicated time and small budgets

Strong innovation culture with fail-fast mentality and pilot frameworks

Innovation lab or R&D function with systematic experimentation and scaled deployment pathways

Strategic Alignment

How aligned is AI with your organization's strategic objectives?

AI is not mentioned in strategic plans or executive discussions

AI is recognized as important but not formally prioritized

AI is part of digital transformation roadmap with identified use cases

AI is a strategic priority with dedicated budget and executive sponsorship

AI is central to business strategy with C-suite ownership and organizational transformation

What level of leadership understanding and support exists for AI initiatives?

Leadership has limited understanding of AI capabilities and implications

Leadership is aware of AI trends but unsure how to apply them

Leadership understands AI potential and supports exploratory projects

Leadership actively champions AI with clear vision and resource commitment

Leadership demonstrates deep AI literacy and drives organizational AI transformation

How clear is your organization's AI value proposition and ROI measurement?

No defined value metrics or ROI framework for AI projects

General business case development but inconsistent measurement

Defined KPIs for AI projects with post-deployment tracking

Comprehensive value framework with proven ROI on deployed AI solutions

Portfolio-level AI value management with continuous optimization and benefit realization

Technology Stack

What is the state of your organization's cloud infrastructure and scalability?

Primarily on-premise with limited cloud presence

Basic cloud adoption for some applications; limited scalability

Hybrid cloud environment with moderate scalability capabilities

Cloud-native architecture with auto-scaling and containerization

Advanced cloud infrastructure with GPU/TPU access and distributed computing capabilities

What AI/ML tools and platforms are currently available to your teams?

No AI/ML tools or platforms; would need to start from scratch

Open-source tools used by individual developers on local machines

Basic ML platform with experiment tracking and model registry

Enterprise ML platform with automated pipelines and collaboration features

Comprehensive AI platform ecosystem with MLOps, feature stores, and production monitoring

How integrated are your systems and data sources?

Siloed systems with no integration layer

Point-to-point integrations for critical workflows

API layer connecting major systems with some data integration tools

Enterprise service bus or modern integration platform connecting most systems

Event-driven architecture with real-time data streaming and comprehensive API management

Data Infrastructure

Team Skills & Capabilities

Process Maturity

Strategic Alignment

Technology Stack

Your AI Readiness Score

Personalized Recommendations