Making AI Work: Interactive Readiness Assessment

Evaluate Your Product’s or Service’s Preparedness for AI Implementation

This assessment evaluates your product or service across 9 key elements that determine AI implementation success. Rate each element from 0 (not started) to 4 (fully implemented) to receive your readiness score.

Foundation & Strategy

Business Value & ROI Management

Ensuring your AI investment delivers measurable business outcomes that justify the cost and effort through specific problem definition, baseline metrics, and gradual value realization planning.

No clear business case or specific problems have been identified. AI is being considered but without concrete objectives or success criteria.
Basic goals like “improve efficiency” or “reduce costs” have been identified, but specific problems and success metrics remain undefined.
Specific business problems have been clearly defined with initial cost-benefit analysis completed. Target outcomes are documented but baseline metrics may be incomplete.
Baseline metrics are established, success criteria are defined, and full cost of ownership has been budgeted. ROI framework is in place but not yet actively measured.
Complete ROI measurement system is operational with regular tracking of both planned and emergent value. Value realization is being actively managed and optimized.

Compliance & Risk Management

Ensuring AI implementation aligns with regulatory requirements and managing new risks around decision transparency, bias detection, and data governance.

No compliance review has been conducted. Regulatory requirements and risk implications of AI implementation are unknown or unaddressed.
Basic regulatory requirements have been identified and documented. Initial risk assessment has begun but comprehensive compliance framework is not yet developed.
Data governance practices are planned and documented. Privacy and security standards are defined but not yet fully implemented. Legal counsel has been consulted.
Comprehensive risk assessment including bias detection and decision transparency is complete. Documentation processes for AI decisions are established and tested.
Complete compliance framework is operational with active monitoring and regular audits. All regulatory requirements are met with documented processes and controls.

Infrastructure Strategy

Choosing deployment architecture that meets security, compliance, and integration requirements while managing costs and maintaining flexibility for future growth.

No infrastructure planning has begun. Cloud vs. on-premises decisions haven’t been evaluated, and technical architecture requirements are undefined.
Basic infrastructure options (cloud, on-premises, hybrid) have been evaluated. Security and compliance requirements for each option are understood but decisions not finalized.
Infrastructure architecture has been selected and documented. Vendor evaluations are complete, and capacity planning has begun but implementation hasn’t started.
Integration points with existing business applications are designed and tested. Infrastructure is partially implemented with scalability and flexibility considerations addressed.
Complete infrastructure is deployed and operational. All integration points are functional, scalability is proven, and ongoing management processes are established.

Implementation & Operations

AI System Selection & Management

Choosing appropriate AI technologies and establishing management processes that align with business requirements, including vendor evaluation and performance monitoring.

No AI technology evaluation has begun. Available options and vendor landscape are not yet researched or understood.
AI technology options are being researched and evaluated. Vendor demonstrations are being conducted but selection criteria and evaluation framework are still developing.
AI technology and vendor have been selected based on specific use cases and requirements. Contracts are negotiated and initial implementation planning has begun.
Management processes for monitoring performance, managing costs, and handling updates are defined and documented. Initial deployment is underway with governance in place.
Complete management system is operational with active monitoring, cost optimization, and vendor relationship management. System updates and evolution are systematically managed.

Integration & Security

Connecting AI systems securely with existing business applications while maintaining appropriate security standards, access controls, and monitoring capabilities.

No security planning has begun. Integration requirements and security implications are not yet understood or addressed.
Basic security and integration requirements have been identified. Existing security policies are being reviewed for AI-specific considerations but comprehensive planning hasn’t begun.
Security architecture for AI integration is designed and documented. Access controls and authentication methods are planned but not yet implemented or tested.
Secure connections between AI systems and business applications are implemented and tested. Data protection measures including encryption and audit logging are active.
Complete security implementation with comprehensive monitoring for both traditional threats and AI-specific risks. All integration points are secured and continuously monitored.

Performance & Reliability

Ensuring AI systems deliver consistent performance and handle failures gracefully, including monitoring, redundancy planning, and capacity management.

No performance planning has begun. Performance requirements and reliability expectations are undefined.
Basic performance requirements including response time and accuracy expectations have been defined. Reliability needs are understood but monitoring and fallback plans are not developed.
Performance monitoring systems are planned and designed. Capacity management strategies are developed but not yet implemented. Initial testing protocols are established.
Monitoring systems are operational with active performance tracking. Redundancy and fallback procedures are implemented and tested. Error handling processes are in place.
Complete performance management system with proactive optimization, predictive capacity planning, and comprehensive reliability measures. All failure scenarios have tested recovery procedures.

Data Pipeline Management

Ensuring reliable data flow and quality for AI systems through integration processes, validation systems, and error handling procedures.

No data pipeline planning has begun. Data sources, quality requirements, and integration processes are undefined.
Data sources for AI systems have been identified and cataloged. Basic data quality requirements are understood but integration architecture and validation processes are not designed.
Data pipeline architecture is designed with clear data flow documentation. Integration processes are planned and data validation rules are defined but not yet implemented.
Data pipeline is implemented with active quality monitoring and validation. Error handling procedures are operational and data lineage tracking is established for audit purposes.
Complete data pipeline management with proactive quality monitoring, automated error handling, and comprehensive lineage tracking. Pipeline performance is optimized and scalable.

Adoption & Growth

User Experience & Training

Designing interfaces and providing training that enable effective team adoption, including role-appropriate training and ongoing support resources.

No training plan has been developed. User interface requirements and training needs are not yet assessed or understood.
Basic training needs and user interface requirements have been identified. Different user roles and their specific needs are understood but training programs are not yet developed.
Training programs are developed and documented for different user roles. User interfaces are designed but not yet optimized. Initial training materials and documentation are created.
User interfaces are optimized for different roles and technical comfort levels. Training programs are delivered and feedback mechanisms are established for continuous improvement.
Complete training ecosystem with ongoing support, advanced user resources, and continuous interface optimization based on user feedback and evolving needs.

Change Management & Evolution

Managing organizational adaptation to AI capabilities while maintaining governance over system changes, including process redesign and ongoing capability building.

No change management planning has begun. Organizational impact and process changes required for AI implementation are not yet addressed.
Basic communication plan for AI implementation is developed. Key stakeholders are identified and initial expectation setting has begun but comprehensive change strategy is not developed.
Required business process changes are identified and documented. Organizational impact is assessed and change management strategy is planned but not yet implemented.
Governance framework for AI system changes is active. Business processes are being redesigned and change management activities are underway with stakeholder engagement.
Complete change management capability with systematic process optimization, ongoing organizational capability building, and mature governance for AI evolution and expansion.