The Four Levels of AI Readiness
Level 0: Fragmented Data (Most Funds Start Here)
L0: Fragmented Data
Characteristics: Data scattered across multiple systems with manual processes, inconsistent definitions, and limited integration.Typical State: Fund admin data in one system, portfolio tracking in spreadsheets, CRM in another tool, with manual reporting processes.AI Capability: None. Data quality and accessibility issues prevent any reliable AI implementation.
- Investment data exists in fund administration systems but isn’t easily accessible
- Portfolio company information lives in spreadsheets with inconsistent formats
- Market data comes from multiple vendors with different schemas
- Reporting requires manual data gathering from multiple sources
- No single source of truth for key metrics like IRR, MOIC, or portfolio valuations
- Reporting cycles take weeks instead of days
- Investment decisions based on incomplete or outdated information
- Compliance reporting requires significant manual effort
- Limited ability to identify portfolio-wide trends or risks
Level 1: Consolidated Data
L1: Consolidated Data
Characteristics: Data from multiple sources brought together in a unified platform with basic standardization and quality controls.Typical State: Amos ingesting data from fund admin, portfolio systems, and market data with initial data models implemented.AI Capability: Basic analytics and reporting, simple predictive models for well-defined use cases.
- Automated data ingestion from primary fund systems
- Standardized data models for core entities (funds, investments, investors)
- Basic data quality monitoring and validation
- Unified reporting across all data sources
- Historical data preservation and accessibility
- Implement Amos core data platform
- Connect primary data sources (fund admin, portfolio management)
- Establish basic data governance policies
- Train team on unified data access patterns
Level 2: Governed Data
L2: Governed Data
Characteristics: Comprehensive data governance with role-based access, audit trails, and business-ready data models that support advanced analytics.Typical State: Full data lineage tracking, automated quality monitoring, and business users able to self-serve analytics needs.AI Capability: Production-ready AI for specific use cases like portfolio risk assessment, investor reporting automation, and basic due diligence support.
- Complete data lineage from source systems to business reports
- Role-based access controls aligned with fund compliance requirements
- Automated data quality monitoring with alerting
- Business-friendly data models that non-technical users can query
- Version-controlled data transformations and business logic
- Implement comprehensive data governance framework
- Establish role-based access controls and audit trails
- Create business-user-friendly data models and interfaces
- Develop data quality monitoring and alerting systems
Level 3: AI-Ready Feature Stores
L3: AI-Ready Feature Stores
Characteristics: Purpose-built feature engineering and model serving infrastructure that enables sophisticated AI applications across fund operations.Typical State: Real-time feature serving, A/B testing frameworks, and production AI systems supporting investment decisions and operational efficiency.AI Capability: Advanced AI applications including predictive due diligence, automated portfolio optimization, intelligent investor communications, and real-time risk monitoring.
- Real-time feature engineering and serving for AI models
- A/B testing frameworks for AI-driven decision support
- Model monitoring and performance tracking infrastructure
- Automated model retraining and deployment pipelines
- Integration with external AI services and custom model development
- Implement feature store infrastructure for AI model serving
- Develop model monitoring and management capabilities
- Create A/B testing frameworks for AI applications
- Establish MLOps practices for model lifecycle management
Interactive AI Readiness Assessment
Use this assessment to determine your current AI readiness level and identify the next steps for progression.Assessment Instructions: For each category below, select the statement that best describes your current state. Your overall readiness level is determined by your lowest-scoring category.
Data Integration Assessment
L0 - Fragmented: Data exists in separate systems with manual processes for combining information- Investment data requires manual export from fund admin systems
- Portfolio tracking happens primarily in spreadsheets
- Market data comes from multiple sources without integration
- Reporting requires gathering data from 3+ different systems
- Primary fund systems connected to unified platform
- Automated data ingestion for core investment and fund data
- Some standardization of data formats and definitions
- Reporting can be generated from single system for most use cases
- All relevant data sources integrated with maintained lineage
- Standardized data models across all business functions
- Role-based access controls and audit trails implemented
- Business users can self-serve most analytics needs
- Real-time or near-real-time data availability for AI systems
- Feature engineering pipelines for model-ready data
- Integration with external AI services and model serving infrastructure
- Automated data quality monitoring optimized for AI requirements
Data Quality Assessment
L0 - Manual Quality Control: Data quality managed through manual processes- Data quality issues discovered during reporting cycles
- Manual validation of key metrics and calculations
- Inconsistent data definitions across systems
- Limited historical data quality tracking
- Automated validation of data completeness and basic formats
- Standardized calculations for key fund metrics
- Basic data quality dashboards and reporting
- Historical data quality trends tracked
- Business rule validation for complex fund operations logic
- Data quality SLAs with automated alerting
- Root cause analysis capabilities for quality issues
- Data quality metrics integrated into business processes
- Statistical data quality monitoring for model inputs
- Automated data drift detection for AI applications
- Quality controls integrated into feature engineering pipelines
- Model performance monitoring linked to data quality metrics
Governance and Compliance Assessment
L0 - Manual Compliance: Compliance managed through manual processes and documentation- Audit trails maintained through manual documentation
- Access controls managed at system level without centralized governance
- Compliance reporting requires manual data gathering and validation
- Limited ability to demonstrate data lineage for regulatory requirements
- Automated audit trails for data access and modifications
- Basic role-based access controls implemented
- Standardized compliance reporting processes
- Data lineage tracking for core business processes
- Fine-grained access controls aligned with business roles and compliance requirements
- Complete data lineage from source systems to business reports
- Automated compliance reporting with validation and approval workflows
- Version-controlled data transformations and business logic
- AI-specific governance policies for model development and deployment
- Automated bias detection and model fairness monitoring
- Explainability frameworks for AI-driven decisions
- Integration with MLOps practices for model lifecycle governance
Capability Gaps and Progression Paths
Based on your assessment results, here are the typical capability gaps and recommended progression paths:From L0 to L1: Foundation Building
Primary Focus: Data consolidation and basic standardization Key Initiatives:- Implement Amos core platform with primary system integrations
- Establish standardized data models for funds, investments, and investors
- Create automated reporting for key fund metrics
- Train team on unified data access patterns
- 80% reduction in manual data gathering for standard reports
- Single source of truth established for core fund metrics
- Automated daily/weekly reporting implemented
- Team trained and actively using unified platform
From L1 to L2: Governance Implementation
Primary Focus: Data governance, quality controls, and business user enablement Key Initiatives:- Implement comprehensive data governance framework
- Establish role-based access controls and audit trails
- Create business-user-friendly analytics interfaces
- Develop data quality monitoring and alerting systems
- Complete audit trail capability for regulatory requirements
- Business users able to self-serve 70% of analytics needs
- Data quality SLAs established and monitored
- Compliance reporting fully automated
From L2 to L3: AI Enablement
Primary Focus: Feature engineering, model serving, and AI application development Key Initiatives:- Implement feature store infrastructure for AI model serving
- Develop model monitoring and management capabilities
- Create A/B testing frameworks for AI applications
- Establish MLOps practices for model lifecycle management
- Production AI applications delivering measurable business value
- Real-time feature serving for AI models implemented
- Model performance monitoring and automated retraining operational
- A/B testing framework supporting AI-driven decision making
Recommended Next Steps by Current Level
If You’re at L0 (Fragmented Data)
- Immediate Priority: Implement Amos core platform to consolidate primary data sources
- Quick Wins: Automate your most time-consuming manual reporting processes
- Foundation Building: Establish standardized data models for core business entities
- Team Development: Train key team members on unified data access patterns
If You’re at L1 (Consolidated Data)
- Governance Focus: Implement role-based access controls and audit trail capabilities
- Quality Improvement: Establish automated data quality monitoring and alerting
- User Enablement: Create self-service analytics capabilities for business users
- Compliance Readiness: Automate regulatory and investor reporting processes
If You’re at L2 (Governed Data)
- AI Preparation: Identify high-value AI use cases and begin feature engineering
- Infrastructure Development: Implement feature store and model serving capabilities
- Experimentation Framework: Establish A/B testing for AI-driven improvements
- Expertise Building: Develop internal AI/ML capabilities or partner with specialists
If You’re at L3 (AI-Ready)
- Scale AI Applications: Expand successful AI use cases across fund operations
- Advanced Capabilities: Implement sophisticated AI applications like predictive due diligence
- Continuous Improvement: Optimize model performance and business impact
- Innovation Leadership: Explore cutting-edge AI applications for competitive advantage
Getting Professional Assessment
While this self-assessment provides valuable insights, a professional evaluation can identify specific opportunities and create a detailed roadmap for your fund’s AI readiness journey. Our team can provide:- Detailed technical assessment of your current data infrastructure
- Custom roadmap with specific milestones and success criteria
- ROI analysis for AI readiness investments
- Implementation support and change management guidance