Technical Infrastructure Assessment
Comprehensive evaluation of your organization's technical readiness for Agentic AI implementation
Version: 1.0 | Last Updated: January 2025
Part of: Agentic AI Executive Guide - Appendix A
Instructions for Use
- Assess each capability area honestly based on your organization's current state
- Record both current state (1-5) and desired target state for gap analysis
- Use the scoring methodology at the end to calculate your overall readiness
- Prioritize improvements based on the largest gaps and strategic importance
- Re-assess quarterly to track progress toward your target state
Category 1: Cloud & Compute Readiness
Evaluates your organization's cloud infrastructure maturity and computational resources necessary for running large-scale AI workloads.
Q1.1: Cloud Infrastructure Maturity
1 - No cloud adoption, fully on-premises infrastructure
2 - Limited cloud usage for non-critical applications
3 - Hybrid cloud with some production workloads migrated
4 - Cloud-first strategy with majority of workloads in cloud
5 - Cloud-native architecture with auto-scaling and full orchestration
Q1.2: GPU/TPU Compute Availability
1 - No dedicated AI compute resources available
2 - Limited shared GPU resources for experimentation
3 - Dedicated GPU clusters for development and testing
4 - Production-grade GPU/TPU infrastructure with reservation capability
5 - Elastic GPU/TPU pools with automatic scaling and optimization
Q1.3: Container Orchestration Platform
1 - No container adoption, traditional deployment only
2 - Basic containerization without orchestration
3 - Kubernetes/similar platform for some workloads
4 - Enterprise-wide container orchestration with CI/CD integration
5 - Advanced orchestration with service mesh and GitOps
Q1.4: Multi-Region Infrastructure
1 - Single data center or region deployment
2 - Primary region with basic disaster recovery
3 - Active-passive multi-region setup
4 - Active-active across multiple regions with data replication
5 - Global edge deployment with intelligent traffic routing
Q1.5: Cost Management & Optimization
1 - No cloud cost visibility or management
2 - Basic cost tracking with manual reporting
3 - Automated cost monitoring with alerts
4 - Proactive optimization with reserved capacity planning
5 - AI-driven cost optimization with automated resource management
Category 2: Data Architecture & Quality
Assesses your data infrastructure, governance, and quality management capabilities essential for AI training and operations.
Q2.1: Data Platform Modernization
1 - Legacy databases with siloed data storage
2 - Traditional data warehouse with ETL processes
3 - Modern data lake with structured/unstructured data
4 - Lakehouse architecture with real-time capabilities
5 - AI-native data mesh with federated governance
Q2.2: Data Quality Management
1 - No formal data quality processes
2 - Manual data quality checks on critical datasets
3 - Automated quality monitoring for key data pipelines
4 - Comprehensive DQ framework with automated remediation
5 - AI-powered data quality with predictive anomaly detection
Q2.3: Real-time Data Processing
1 - Batch processing only with significant latency
2 - Mini-batch processing with hourly updates
3 - Near real-time processing for specific use cases
4 - Stream processing platform with sub-second latency
5 - Event-driven architecture with complex event processing
Q2.4: Vector Database Capabilities
1 - No vector database or embedding storage
2 - Experimental vector storage for POCs
3 - Production vector DB for specific AI applications
4 - Enterprise vector platform with multi-modal support
5 - Distributed vector infrastructure with semantic search
Q2.5: Data Governance & Lineage
1 - No data governance or lineage tracking
2 - Basic metadata management with manual documentation
3 - Automated lineage tracking for critical data flows
4 - Comprehensive governance with policy enforcement
5 - AI-driven governance with automated compliance validation
Category 3: Integration Capabilities
Evaluates your ability to connect AI systems with existing enterprise applications, data sources, and external services.
Q3.1: API Management Platform
1 - Point-to-point integrations without API management
2 - Basic API gateway for external access
3 - Full API lifecycle management with versioning
4 - Advanced API platform with developer portal and analytics
5 - AI-enhanced API management with intelligent routing and optimization
Q3.2: Event-Driven Architecture
1 - Synchronous request-response only
2 - Basic message queuing for decoupling
3 - Event streaming platform (Kafka/similar) in production
4 - Comprehensive event mesh with schema registry
5 - Cloud-native event architecture with serverless processing
Q3.3: Identity & Access Management
1 - Application-specific authentication without SSO
2 - Basic SSO for human users only
3 - Unified IAM for humans and basic service accounts
4 - Zero-trust architecture with fine-grained permissions
5 - AI-native IAM with context-aware, adaptive authentication
Q3.4: Legacy System Connectivity
1 - No integration with legacy systems
2 - Manual data exports/imports from legacy systems
3 - Automated batch integration with key legacy systems
4 - Real-time bidirectional integration via modern adapters
5 - AI-powered integration layer with automatic protocol translation
Q3.5: Workflow Orchestration
1 - Manual process execution without orchestration
2 - Basic workflow automation for routine tasks
3 - Enterprise workflow platform with visual design
4 - Advanced orchestration with ML pipeline support
5 - Intelligent orchestration with self-optimizing workflows
Category 4: Security Infrastructure
Assesses security capabilities specific to AI workloads including model protection, data privacy, and threat detection.
Q4.1: AI-Specific Security Controls
1 - No AI-specific security considerations
2 - Basic model access controls only
3 - Model versioning with audit trails
4 - Comprehensive AI security including adversarial protection
5 - Advanced AI security with real-time threat detection and response
Q4.2: Data Encryption & Privacy
1 - Limited encryption, no privacy controls
2 - Encryption at rest for sensitive data
3 - Full encryption at rest and in transit
4 - Advanced privacy preservation with data masking/tokenization
5 - Privacy-preserving AI with federated learning capabilities
Q4.3: Security Monitoring & SIEM
1 - Basic logging without centralized monitoring
2 - Centralized logging with manual review
3 - SIEM platform with rule-based alerting
4 - Advanced SIEM with ML-based anomaly detection
5 - AI-powered security operations with automated response
Q4.4: Compliance Automation
1 - Manual compliance tracking and reporting
2 - Spreadsheet-based compliance management
3 - GRC platform for compliance tracking
4 - Automated compliance validation with continuous monitoring
5 - AI-driven compliance with predictive risk assessment
Q4.5: Secrets Management
1 - Hardcoded credentials and manual key management
2 - Environment variables with basic rotation
3 - Centralized secrets vault with API access
4 - Dynamic secrets with automatic rotation
5 - Zero-trust secrets with ephemeral credentials
Category 5: Development & Deployment Tools
Evaluates your toolchain for building, testing, deploying, and monitoring AI agents in production environments.
Q5.1: MLOps Platform Maturity
1 - No MLOps practices or platform
2 - Basic experiment tracking and model registry
3 - End-to-end MLOps platform with CI/CD
4 - Advanced MLOps with A/B testing and feature stores
5 - AI-driven MLOps with self-optimizing pipelines
Q5.2: Testing & Validation Framework
1 - Manual testing only, no automated validation
2 - Basic unit tests for AI components
3 - Comprehensive testing including model validation
4 - Advanced testing with bias detection and robustness checks
5 - AI-powered testing with automatic test generation
Q5.3: Observability & Monitoring
1 - Basic application logs only
2 - APM tools with basic metrics
3 - Full observability stack with distributed tracing
4 - AI-specific monitoring including drift detection
5 - Intelligent observability with predictive analytics
Q5.4: Development Environment
1 - Local development only, no standardization
2 - Shared development servers with basic tooling
3 - Cloud-based development with notebooks and IDEs
4 - Integrated AI development platform with collaboration
5 - AI-augmented development with code generation and optimization
Q5.5: Deployment Automation
1 - Manual deployment processes
2 - Basic CI/CD for traditional applications
3 - Automated deployment with rollback capabilities
4 - Advanced deployment with canary releases and feature flags
5 - Self-healing deployments with automatic optimization
Scoring Methodology & Interpretation
Raw Score Calculation
Sum all current state scores (Range: 25-125)
Average score = Total ÷ 25
Use this for quick baseline assessment
Weighted Score (Recommended)
Category Weights:
Cloud & Compute: 25%
Data Architecture: 25%
Integration: 20%
Security: 20%
Dev & Deployment: 10%
Gap Analysis Priority
Sort by gap size (Target - Current)
Consider strategic importance
Focus on gaps ≥2 points first
Investment Estimation
1-point improvement: $50K-200K
2-point improvement: $200K-500K
3+ point improvement: $500K+
*Varies by category and organization size
Overall Maturity Levels
Level 1 (1.0-1.9)
Initial: Ad-hoc processes, limited infrastructure. Focus on foundation building.
Level 2 (2.0-2.9)
Developing: Basic capabilities established. Ready for pilot implementations.
Level 3 (3.0-3.9)
Defined: Standardized processes and tools. Can support production deployments.
Level 4 (4.0-4.5)
Managed: Optimized infrastructure with advanced capabilities. Ready for scale.
Level 5 (4.6-5.0)
Optimizing: Leading-edge capabilities with continuous improvement. Innovation leader.