Blog

The Head of AI at Pella Corp reviews questions from our webinar together and discusses Pella's specific approach to rolling out enterprise AI.

Every data, analytics, and AI leader has heard "we've tried that before." Ryan Sousa explores what lies behind that reaction and how to build healthy disrespect for the status quo without losing the people who carry it.

The operating model is where good change management either compounds or collapses, and most enterprises have built theirs to deploy AI rather than sustain it.

Most organizations diagnose AI adoption failure as a training problem and respond with more training. Jack Phillips argues the primary barriers are psychological, and that most change programs are solving them in the wrong order.

Most AI adoption failures get blamed on culture and change management. Jack Phillips argues the more common culprit is a supply-side trust problem organizations often don't truly measure.

Only 5.5% of organizations report meaningful financial impact from AI, even as enterprises restructure workforces around it at scale. Check out our take on how to close the gap between AI and business impact.

Visible AI activity can mask uneven readiness. Learn what IIA advisory and assessment data reveal about the maturity gaps shaping enterprise performance. 

AI readiness depends on more than platforms. See why your analytics operating model design now matters just as much as data, governance, and architecture. 

The real test of AI readiness is not momentum. It is whether data and AI improve important decisions across the business.