Session

Genie is a Game Changer

Overview

Experience	In Person
Track	Analytics & BI
Industry	Manufacturing
Technologies	Genie
Skill Level	Beginner
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We would like to share our vision, strategy and learnings with Databricks Genie. You want to know something, you ask, you get an answer. By enabling our users to interact with our data, AkzoNobel can anticipate developments in Supply Chain Management, Finance and Service Delivery more quickly driving impact. This requires a new form of cooperation between business and IT. The responsibilities will be shared and clearly defined as IT will be in control to safeguard standards and the business will provide semantics. For the users there is also a change to be managed, most will think in reports when asking questions to Genie while DeepSearch allows much more. Further innovation with Genie DeepSearch allows us to bring insights to our management in the form of podcasts or videos. Genie is a gamechanger when done right. It allows for completely different ways of interacting with insights but also offers opportunities for IT to simplify the Analytics landscape.

Session Speakers

Kristi Blaisdell

/Team Lead Enterprise Reporting
AkzoNobel

Peter Snoep

/Domain architect data & analytics
AkzoNobel

Full Summary

How AkzoNobel turned Genie into an executive habit, not just a demo

AkzoNobel, a global paints and coatings company, faced a familiar enterprise problem: fragmented systems, proliferating dashboards, and slow answers to strategic questions. The organization used conversational analytics to flip that script, but the hard work was not technical. It was reshaping habits so leaders would ask questions directly, trust the answers, and act on them. The story underscores a broader shift in data analytics, where value comes from context and action rather than prettier charts, a theme echoed in Databricks' view on how AI is transforming data analytics. For organizations evaluating where to begin, Databricks' complete guide to business intelligence and analytics in the AI era offers a practical framework for connecting data foundations to decision-making.

Fix the foundation before chasing GenAI

AkzoNobel started with sprawl: more than 50 ERP systems, inconsistent KPIs, and Excel gluing together answers that took months. Piling on hundreds of dashboards did not help. A question that required an enterprise view still triggered a costly, slow hunt across siloed systems.

The turnaround began with simplification. IT was centralized, the ERP estate collapsed to four, finance data was consolidated into a single instance, and analytics moved to a lakehouse architecture on the Databricks Platform. That two and a half year investment harmonized definitions and data models, creating the conditions that made Genie credible later.

Turn a demo into a must have with one leader's hardest question

Early Genie demos fell flat. Generic use cases drew the reflexive response, "I already have a dashboard." Momentum arrived when the team built a demo on live finance data and sat with the CIO. After answering his common questions in seconds, they asked for his toughest unsolved question. Genie answered it on the spot.

That moment converted a skeptic into a daily user and gave the program executive air cover. The lesson is simple: skip abstract capability tours. Ground the experience in one leader's real decisions and unanswered questions.

Engineer trust and move analysts up, not out

Adoption rises with trust, and trust needs structure. Each Genie space at AkzoNobel has an owner, an SLA, and a certification path. When a Genie EBITDA answer conflicted with a Power BI figure, the team uploaded a screenshot and asked Genie to explain the gap, validated the correct logic with finance, and fixed the metric within minutes. Fast reconciliation, transparent ownership, and finance validation turned a probabilistic tool into a dependable companion.

Analysts are central to that trust. Their business logic, definitions, and context feed Genie's semantic layer and prompts. Their role shifts from producing and formatting dashboards to curating metrics and validating outputs. Success also depends on an explicit IT‑business contract that clarifies responsibilities and keeps feedback flowing.

Shorten the path from question to action

Dashboards show what happened. Leaders need to know what to do next. Genie demonstrated value by turning a churn analysis into a prioritized action plan with root causes and target accounts. Not every suggestion will be perfect, yet leaders no longer face a blank page, which accelerates action.

The roadmap points toward more proactive experiences, contextual access that gives new users immediate, role‑based answers, and agentic workflows where recommendations can trigger next steps.

From novelty to norm: the habits behind durable adoption

AkzoNobel's experience shows that conversational analytics sticks when the organization changes how it asks questions, not only the tools it uses. The winning habits were to fix the data foundation first, personalize the aha moment for one executive, formalize trust, and elevate analysts into stewards of meaning. The technology was the easy part. Changing behavior delivered the impact.

FAQ

Dashboards multiplied faster than employees but could not bridge fragmented ERPs, inconsistent KPIs, and siloed processes. Without harmonized data and shared definitions, visualization layers only shifted the problem and still delivered slow, contested answers.

They compared Genie's output with the trusted Power BI figure, uploaded a screenshot into Genie to diagnose the difference, confirmed the correct logic with finance, and updated the Genie space within minutes. Corrections were treated as a normal product feedback loop.

Analysts provide the business definitions, logic, and context that make Genie reliable. Their work moves from building and beautifying dashboards to curating metrics, enriching context, and validating outputs, which the team frames as moving people up rather than out.

Roughly two and a half years. The team centralized IT, consolidated ERPs to four, moved finance data to the Databricks Platform, and built harmonized models like the balance sheet, P&L, and margin management. Genie was layered on top of that foundation.

No. Dashboards remain a shared grounding point so everyone sees the same numbers. Genie complements them by answering ad hoc questions, supporting exploration, and proposing actions that would be impractical to codify as static reports.