The AI lifecycle

Know organization's purpose, programs, processes, systems, information, personnel and issues.

Focus on opportunities for AI for value, feasibility, risk, readiness and resources.

Define the user experience, data flows, AI architecture, permissions, workflows, escalation paths, and human review points.

Build the AI application, assistant, analytics environment, automation, integration or data solution.

Integrate connected systems, APIs, databases, documents, cloud services, and business applications.

Transfer the solution to the production environment, under the right conditions and with proper testing and controls.

Automate approved repetitive tasks, while maintaining human approval as required.

Monitor the performance of systems, data quality, AI outputs, usage, errors, and operational behavior.

Check security needs, such as access control, data handling, integrations, authentication, logging, and more.

Keep policies, documentation, model evaluations, access rules, risk controls and review procedures.

Enhance workflows, prompts, retrieval, models, integrations, reporting and user experience in light of observed performance.

When the organization is ready, expand into other departments, programs, locations, data sources, assistants, workflows, and analytics capabilities.