Ready for Conversational BI? How to Implement AI/BI Genie Without Friction
- Arkon Data
- Oct 22
- 6 min read
Genie gives business users a new way to explore information using natural language queries. To deliver accurate results and scale across teams, Genie requires a clean and contextual data foundation. The best implementations are those that align technology, data quality, and governance from the start.
Below are the best practices that leading organizations follow when deploying Databricks AI/BI Genie across their analytics ecosystem.
1. Start with Clear Business Objectives
Every successful AI/BI Genie deployment starts with clarity. Before turning on the technology, define the specific business problems it will help solve and set expectations that match the reality of working with AI systems.
AI projects are inherently uncertain. Their performance depends on data quality, system complexity, and evolving business needs. Setting realistic expectations early prevents frustration and ensures teams focus on learning and improving, not defending unpredictable outcomes.
Begin by defining clear business questions Genie will address, such as improving sales forecasts, identifying supply chain delays, or accelerating customer analytics. Then, conduct an initial exploration phase to understand the available data and the feasibility of these objectives. Some organizations refer to this as “Phase 0,” where teams assess data readiness, test assumptions, and establish a baseline before committing to measurable targets.
At this stage, map all key stakeholders and align their expectations. Business leaders often prioritize outcomes like ROI and decision speed, while data teams care about data quality, governance, and scalability. Both perspectives are valid, and success depends on maintaining open communication between them.
It’s also important to explain the probabilistic nature of AI early on. Genie’s responses depend on patterns and probabilities, not fixed rules. Using demonstrations can help stakeholders understand that small variations in phrasing or data context may lead to different outputs; this is a feature of intelligent systems, not a flaw.
Finally, define milestones that allow you to review progress in phases. Establish clear checkpoints where stakeholders can evaluate results, provide feedback, and refine direction. This structured approach builds confidence, keeps the project on track, and ensures that Genie delivers value aligned with real business priorities.
Take a look at this free tool that's perfect for creating SMART objectives.
2. Build a Clean and Contextualized Data Foundation
AI/BI Genie only performs optimally when interacting with structured and meaningful data. Context gives Genie the ability to understand relationships, hierarchies, and business logic within datasets.
Arkon Data Platform plays a key role in achieving this foundation. By extracting data from complex systems such as Oracle Fusion Cloud, IoT, CRM, and PoS platforms while preserving metadata and structure, ADP ensures that Genie receives clean, contextualized data ready for analysis. This preparation minimizes errors and improves the accuracy of every generated insight.
3. Leverage Databricks Unity Catalog for Governance and Security
Strong Governance is essential for scaling AI responsibly. Unity Catalog provides a unified layer of access control, lineage tracking, and auditability across all Genie workflows. It allows teams to manage permissions consistently while maintaining compliance with internal and regulatory standards.
Integrating Genie through Unity Catalog helps maintain trust in AI-generated outputs. Every query can be traced back to its data source, thereby enhancing transparency and accountability across all business functions.
To see how this looks in action, watch the following short demo by Databricks. It shows how Unity Catalog streamlines governance while Genie enables conversational queries and instant visualizations, all without writing SQL. You’ll see how access permissions are granted, how data lineage is traced, and how users interact naturally with their data to unlock insights securely.
4. Design an Iterative Deployment Plan
Implementing Genie or any other GenAI solution should be treated as a continuous learning process, not a one-time implementation. The most successful projects evolve through small, deliberate iterations that refine both the technology and the way people interact with it.
Start by defining a focused pilot within a single business area, such as sales, logistics, or finance. Observe how users engage with Genie, what questions they ask, and how effectively the system responds. This initial stage provides valuable insight into data readiness, user expectations, and the clarity of your metadata.
Once the pilot begins, follow a structured iterative model similar to how AI itself improves over time:
Initialization: Define clear objectives for the pilot, establish the datasets Genie will use, and determine what outcomes define success.
Execution: Run Genie with real business queries and collect responses from users.
Evaluation: Measure accuracy, response clarity, and overall user satisfaction.
Feedback: Capture gaps between expected and actual results, both from users and technical teams.
Modification: Refine data structures, prompts, and model context to improve performance.
Reiteration: Repeat the process, scaling to more teams or broader datasets as confidence grows.
This cyclical process mirrors the way iterative AI models evolve. Each round of feedback sharpens Genie’s contextual understanding and strengthens its ability to deliver insights that are both relevant and reliable.
By treating deployment as an ongoing loop of learning and refinement, organizations ensure that Genie continues to align with business goals, adapt to new data, and remain effective as conditions change. The result is a conversational BI system that matures alongside the enterprise, delivering smarter, more trusted insights over time.
5. Integrate Genie into Existing Workflows
AI adoption grows when it fits naturally into existing processes. Embed Genie into dashboards, internal chat systems, or collaborative tools where users already operate. The goal is to reduce friction and make data-driven insights as accessible as any conversation.
For example, a supply chain manager can query shipment delays directly within a collaboration tool, or a finance leader can ask Genie to summarize quarterly performance without switching platforms.
6. Foster a Culture of Data Literacy
Building true data literacy goes beyond training sessions or technical workshops. It’s about giving people the confidence and freedom to explore how AI can improve their work. Encourage teams to experiment with Genie, test different types of questions, and share what they learn with others.
Walmart offers a great example of how to approach this. The company opened its generative AI tool My Assistant to thousands of employees, inviting them to identify where the technology could simplify their daily tasks. This approach turned AI adoption into a collective effort, where employees themselves surfaced new use cases that improved both productivity and customer experience.
Organizations implementing AI/BI Genie can follow a similar path by inviting business users to propose and test real applications within their areas. When people are part of discovering how AI adds value, adoption grows naturally, and innovation becomes part of the culture.
The goal is not only to teach teams how to use data and AI tools, but to help them see these systems as extensions of their creativity and decision-making.
7. Continuously Optimize Performance and Context
As Genie usage expands, monitor query trends and performance metrics. Identify which datasets are driving the most business value and where additional context or refinement might be needed. Continuous improvement turns Genie into a dynamic intelligence layer rather than a static reporting tool.
With platforms like Arkon Data Platform ensuring structured data flows into Databricks, updates and improvements can be implemented without disrupting ongoing operations.
From Clean Data to Intelligent Insights
Databricks AI/BI Genie represents a major step toward conversational and autonomous analytics. Yet its success depends entirely on how well the underlying data is prepared, governed, and contextualized.
Organizations that pair Genie with Arkon Data Platform gain an advantage: they can connect complex enterprise data to AI systems that truly understand business context and deliver reliable insights in seconds.
Watch the Demo
See how Arkon Data Platform prepares and delivers clean, contextualized data that empowers Databricks AI/BI Genie.
Watch our quick demo below to learn how your organization can enable AI agents to make faster, more accurate business decisions.
FAQs on how to implement AI/BI Genie successfully
1. What is Arkon Data Platform?
Arkon Data Platform is an enterprise data management solution that prepares, cleans, and structures raw data, making it ready for analytics, AI, and BI applications, such as Databricks AI/BI Genie.
2. How does Arkon Data Platform improve data quality for Databricks?
It automates data preparation—removing duplicates, resolving inconsistencies, and enriching context—so Databricks can work with high-quality, trustworthy data for faster and more accurate insights.
3. What types of data can Arkon handle?
Arkon can process structured, semi-structured, and unstructured data from multiple enterprise sources, ensuring all datasets are unified and analytics-ready.
4. Why is clean and contextualized data important for AI and BI?
AI and BI models rely on accurate and consistent inputs. Clean, contextualized data reduces noise and errors, helping models generate meaningful insights and reliable business intelligence.
5. How can businesses benefit from using Arkon with Databricks AI/BI Genie?
Together, Arkon and Databricks enable organizations to transform complex data into actionable intelligence, accelerating analytics workflows, enhancing decision-making, and driving improved business outcomes.
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