Evaluating AI Solutions | Technical Fit and Business Impact

Topic	Key Questions
Architecture & Integration	Is it API-first? How well does it integrate with current stack? Does it support event-driven or microservices architecture?
Security & Compliance	SOC 2, ISO 27001, GDPR, HIPAA support? Data encryption at rest/in-transit? Role-based access controls?
Scalability & Performance	How does it handle peak loads? Latency benchmarks? Auto-scaling capabilities?
Data & AI Models	Proprietary or third-party LLMs? Model transparency? Customization options?
DevOps & CI/CD	Is it containerized (Docker/Kubernetes)? Supports version control, rollback, staging?
Monitoring & Analytics	Real-time dashboards, alerting, integration with tools like Datadog, Splunk, Grafana?
TCO & ROI	What’s the licensing model (user/usage-based)? Time to value? Pilot-to-production support?

2. CTO-Specific Pain Points

Pain Point	Related Evaluation Focus
❗Integration Complexity	Look for plug-and-play APIs, strong documentation, developer support.
🔐 Security/Compliance Burden	Ensure support for key certifications and granular access controls.
⚠️ Shadow IT / Tool Sprawl	Prefer unified solutions or open APIs for easy orchestration.
📉 Uptime & SLA Reliability	Need transparent uptime history, SLA guarantees, failover protocols.
🧩 Fragmented Workflows	Seek tools that centralize dashboards or automate routine cross-tool tasks.
🧠 AI Black Box Concerns	Ask about model explainability, training data origin, and error handling logic.