AI Readiness Checklist for Startups & Scaleups

Core business data lives in a proper database (PostgreSQL, MongoDB) rather than spreadsheets.

Product events and user actions are captured via analytics pipelines (Segment, Amplitude, Mixpanel).

Database schemas and data dictionaries are documented and maintained as the product evolves.

GDPR/CCPA-compliant data handling with consent management is implemented from early stages.

APIs and integrations with external data sources (market data, enrichment services) are operational.

An analytics warehouse (BigQuery, Snowflake) consolidates product, sales, and operational data.

Key data features for ML models are computed and stored in a reusable feature store.

Automated backup and disaster recovery procedures protect critical business and user data.

Cloud infrastructure meeting startups & scaleups security and compliance requirements is operational.

A secure API gateway manages access to AI services with authentication and rate limiting.

Infrastructure for deploying and serving ML models is available with appropriate latency.

Continuous integration and deployment pipelines support AI model versioning and rollback.

Sufficient compute resources (GPU/TPU) are available for model training and inference.

A centralized model registry tracks model versions, metadata, and deployment history.

Model performance monitoring tracks accuracy, drift, latency, and resource usage.

Data encryption in transit and at rest meets SOC 2 Type II (required for enterprise sales) requirements.

Domain experts who understand AI capabilities are identified to champion adoption and validate outputs.

Data scientists with startups & scaleups domain expertise are on staff or available.

Staff across the organization receive training on AI tools, limitations, and responsible use.

AI projects include domain experts, data scientists, engineers, and compliance.

MLOps engineers manage model deployment, monitoring, and lifecycle in production.

Product managers translate business needs into AI requirements and manage AI roadmaps.

Teams can evaluate and manage AI vendors for startups & scaleups applications.

The organization invests in AI education through conferences, certifications, and knowledge sharing.

A formal AI policy defines acceptable use, risk levels, and approval workflows aligned with SOC 2 Type II (required for enterprise sales).

An ethics framework addresses fairness, transparency, and accountability for startups & scaleups AI.

AI models undergo structured validation testing before deployment to production.

Regular bias audits evaluate AI models across relevant demographic and operational dimensions.

An incident response plan addresses AI model failures, biased outputs, and data issues.

Structured change management guides organizational transitions from AI implementation.

Vendor AI solutions undergo proper evaluation for quality, compliance, and data handling.

Post-deployment monitoring tracks model performance, drift, and business impact continuously.

High-impact AI use cases for startups & scaleups are documented with expected outcomes.

An ROI model quantifies AI impact including Time to market for AI features and AI feature adoption and engagement rate.

C-suite executives actively champion AI initiatives with dedicated budget and resources.

A 2-3 year AI roadmap aligns with the organization's startups & scaleups strategic plan.

AI maturity is benchmarked against startups & scaleups industry peers and leaders.

AI initiatives directly map to improving key stakeholder metrics and satisfaction.

Organization embraces AI experimentation with acceptance of measured failures.

Strategic partnerships with AI vendors, cloud providers, and domain experts are established.