Stability: The Solid Ground for AI Development

During a recent webinar, I discussed AI and its dependence on data foundations, emphasizing how successful AI projects—those that move beyond pilot phases, mitigate risks, and deliver measurable value—hinge on trustworthy and reliable data.  We pinpointed four crucial areas for data infrastructure:  • User Focus – Aligning the AI products, data, and goals with the needs/goals of end users• Comprehensive Models – Creating the taxonomies, ontologies, schemas, etc that describe your organization’s domain consistently across your systems, business units and interfaces• Organizational Alignment – Ensuring that strategy, data, technology, KPIs, and resourcing are all working together• Stability – Ensuring that the user understanding, models, and organizational alignment are maintained going forward. Stability’s emergence as a key area was unexpected. User understanding, robust model creation, and organizational data alignment are widely recognized principles, forming core tenets of Factor’s methodology. Stability has never been an explicit area of focus, although I believe it has been one of our core concepts nonetheless.