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

Описание: 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