AI Readiness Checklist for Sri Lankan Businesses

AI readiness is not about whether a company has heard of ChatGPT. It is about whether the company’s data, systems, people, policies, and processes are ready for AI to be useful without creating new risk.

For many Sri Lankan businesses, the readiness work itself delivers value. Cleaning customer data, documenting workflows, clarifying approvals, and connecting systems improves operations even before an AI assistant or automation goes live.

Data readiness

• Do you know which system is the source of truth for customers, products, invoices, stock, and employees?
• Is the data complete, current, and structured enough for reporting or automation?
• Can the required data be accessed through an API, database, or controlled export instead of manual copying?
• Are sensitive fields such as salary, medical, financial, and customer data classified properly?

Process and system readiness

1. Choose one workflow and document every handoff, approval, exception, and data source.
2. Check whether ERP, CRM, POS, HR, support, and accounting systems can exchange data reliably.
3. Identify where human review is mandatory before AI can send, approve, post, or change anything.
4. Decide how errors will be detected, corrected, and logged after deployment.

Governance readiness

Every AI use case needs an owner, a purpose, a data boundary, a human oversight rule, and a success metric. Companies should also have an acceptable-use policy that tells employees what data they can and cannot enter into public AI tools.

A practical readiness score

• Ready: the workflow is documented, data is accessible, risk is understood, and ROI can be measured.
• Almost ready: the business case is clear, but data cleanup or integration is needed first.
• Not ready: the company cannot name the owner, data source, approval rule, or success metric.

Capricon can help businesses assess AI readiness, modernize workflows, connect systems, and build controlled automation around real business needs. The goal is not to use AI everywhere. The goal is to use it where it creates measurable operational value.