AI Analytics Without the Dashboard: Trust, Change Management, and Real Adoption скачать в хорошем качестве

AI Analytics Without the Dashboard: Trust, Change Management, and Real Adoption

AI adoption doesn’t fail because leaders lack ambition, it fails because organizations can’t mobilize the change. This episode is a grounded look at what “real AI” looks like once you’re past the hype and inside the workflow. Philip Odelfelt, CEO of Datavations, explains what it takes to deliver decision intelligence that executives will actually use: clean data, credible signals, and a system that reduces manual work without pretending relationships and judgment can be automated away. Along the way, we talk about why trust and verification become the competitive edge in analytics, and why disciplined experimentation matters when AI R&D success rates are still unpredictable. Why “change management” is the hidden cost of AI adoption inside enterprises Turning messy, fragmented data into a usable source of truth (and why that comes first) When conversational interfaces help, and when dashboards still win Where humans stay in the loop: relationships, judgment, and accountability How trust, testing, and continuous validation become a moat in analytics platforms The discipline problem: making bets in an AI landscape where most experiments won’t ship Comment below: Where, in your organization, is trust the real bottleneck to AI adoption: data quality, workflow change, or decision accountability? This conversation sits inside the broader work of Managing AI and Big North Network: helping leaders adopt AI with stewardship, not reflex, and with a clear view of second-order consequences for people, trust, and decision rights. Resources Datavations: https://datavations.ai/ Big North Network: https://bignorthnetwork.com/ Managing AI: https://bignorthnetwork.com/managing-ai Chapters 00:00 — Structural unemployment and “new jobs” we didn’t predict 00:44 — Episode setup + Datavations overview 01:34 — Philip’s path: data, markets, and underserved industries 04:04 — Building defensible vertical data infrastructure 06:35 — Team growth and the Series A milestone 08:55 — What “adoption” really requires inside large orgs 11:53 — How Datavations uses AI internally (cleaning, insights, anomalies) 12:46 — Ontologies, standardization, and a real productivity benchmark 15:18 — “Housing Quant”: using LLMs as a new UI for data 18:20 — Beyond dashboards: automation + human approval loops 21:23 — Pragmatic buyers: utility over hype 22:53 — Where humans remain essential (relationships and executive judgment) 29:21 — Trust as moat: testing, backtesting, and validation 30:43 — Optimism: budgets shifting toward technology investment 31:51 — Concern: low AI R&D success rates and the need for discipline 32:48 — Where to find Philip and Datavations