White Paper
Intelligence by Iteration - AI's Software Path
Since early 2018, firms have spent billions of dollars for first mover advantage on the promise of artificial intelligence technology, particularly generative AI. For many though, the results have fallen flat, largely due to gaps in following standard software development lifecycle (SDLC) processes or appropriately adapting SDLC to the specific needs of AI model testing, documentation and monitoring. These early and ongoing failures are costly, potentially risky and can cause loss of trust among customers and partners.
This whitepaper explores how organizations can effectively develop and govern AI systems by adapting traditional software development lifecycle (SDLC) principles.
Key topics include:
- Identifying data sources and conducting a full provenance and lineage analysis
- Law and policy considerations
- The implementation of a strong data stewardship program
- Model management through iterative test and build cycles
- The importance of documenting enterprise AI governance policies, processes, and procedures
- Product-level and operational considerations
In addition, the paper concludes with a practical AI readiness checklist with questions aspiring AI leaders should be asking themselves and their organization.