How do I run a proof of concept for AI grounded in company data?

Summary

• Use a structured 3-phase, 4-week framework to move from discovery to business-context-grounded AI prototypes.
• Start by identifying high-impact use cases through executive interviews, then filter by technical feasibility in a 1-day design workshop.
• Build a semantic foundation with business glossary, metric views, and domain organization so AI answers draw from company context, not generic models.
• Execute an engineering sprint to build two working prototypes, deliver business requirements documentation, and quantify ROI.

How do I run a proof of concept for AI grounded in company data?

A proof of concept for business-context AI requires moving beyond generic models to ground AI in your company's data, definitions, and business logic. Databricks provides a structured 3-phase, 4-week engagement model designed to surface high-impact use cases, validate technical feasibility, and deliver working prototypes on real data.

The 3-phase POC framework

Phase 1: Discover (Week 0)

Gather your existing AI backlog and interview executive stakeholders to surface ideas with the highest business impact. Collect:

• The backlog of AI use cases your teams already have
• Input from 15 to 60+ executives and business leaders on priorities
• P&L and board-level priorities as anchors

The goal is to surface all candidate use cases without filtering yet.

Phase 2: Design (1 day)

Run a single-day workshop with business and IT leadership to filter the backlog by technical feasibility and business impact. Land on 2 priority use cases to prototype, with executive sponsor commitment on success targets. This workshop surfaces blockers early and ensures alignment before the engineering sprint begins.

Phase 3: Deliver (Weeks 1–4)

Execute a focused engineering sprint where Databricks engineers and your team build functional prototypes on real data. This phase produces:

• A stack-ranked portfolio with quantified ROI
• Business requirements documentation
• Two working prototypes on production data
• A go/no-go decision at week 4

Building the semantic foundation

Before AI can answer questions accurately, establish a semantic layer that grounds the AI in your business context. This consists of:

Component	Purpose
Business Glossary	Define business terms and concepts unique to your company (e.g., "Customer Acquisition Cost," "Approved Order", "Net Revenue Retention").
Metric Views	Create metric calculations once in Unity Catalog so they mean the same everywhere across Genie, dashboards, and third-party tools.
Domains and Subdomains	Organize data assets into business-aligned groups (e.g., Sales, Inventory, Payments, Marketing). Tag dashboards, agents, tables, and glossary pages by domain.
Authority Scoring	The system automatically weighs definitions by source authority, usage patterns, certification status, and freshness to resolve conflicts.

The Ontology Jumpstart workflow

For a rapid start, Databricks provides an Ontology Jumpstart approach:
Metrics Discovery (1 hour, led by account executive)
Meet with executive leadership, business users, and technical leads to identify the line of business's most valuable KPIs and use cases. Identify an executive sponsor and assess which data already exists to support the use case.
Genie Agent Build (4 hours, led by solutions architect/data scientist)
Work with the executive sponsor and technical team to build a production-ready agent on those KPIs. Create a playbook for future agents so the team can build independently after the POC.

Key principles for success

Model the head, let the system infer the tail. Start by deliberately curating your most critical definitions (metric views, certified assets, domains). The system automatically learns context from your existing tables, dashboards, queries, notebooks, and connected applications, so you don't have to manually define everything.
Sequence adoption: curate, then broadcast. Tune one high-value agent to a defensible benchmark before exposing it broadly. Business users can create and share agents directly without entering the data platform, but only once the foundation is solid.
Ground everything in Unity Catalog. The system respects Unity Catalog permissions and serves context in a permission-aware way, so users only see data they're entitled to. Apply fine-grained access control (row/column masking, attribute-based controls) on underlying data.
Put cost controls in place before scaling. Set budget guardrails at the account, workspace, group, and user levels. Monitor consumption from day one.

Expected outcomes

A well-executed POC delivers:

• A prioritized portfolio of opportunities with estimated ROI
• Business requirements documentation for implementation
• Two working prototypes built on real company data
• A clear go/no-go decision for moving to broader rollout

This approach replaces generic vendor POCs with a systematic, business-driven process that grounds AI capabilities in your actual company data and business logic.

Use cases

Financial services: Build an agent that answers questions about customer portfolios, risk exposure, and regulatory metrics, grounded in your core banking data.
Retail and e-commerce: Create agents that surface inventory trends, customer lifetime value, and promotion impact using your transaction and product data.
Healthcare: Develop an agent that answers clinical and operational questions while respecting HIPAA and data governance policies.
Manufacturing: Build an agent that surfaces production metrics, supply chain status, and quality insights from operational data.

FAQs

Do I need all my data cataloged before starting a POC?

No. Start with the data you know you'll need for the 2 priority use cases identified in the Design phase. The system learns context incrementally as you add more definitions.

How many people do we need to interview in the Discover phase?

The range is 15 to 60+ stakeholders, depending on your organization's size and complexity. Focus on executives and business leaders who set priorities and understand P&L impact.

Can we build more than 2 prototypes in 4 weeks?

The framework is designed around 2 prototypes to keep scope manageable and ensure quality. After the POC, your team can apply the playbook to build additional agents independently.

What happens after the 4-week POC?

You make a go/no-go decision. If you go forward, you move into implementation and rollout planning. The 1-day Design workshop and 4-hour Jumpstart playbook become your template for scaling.

How do we ensure the AI answers are accurate?

Accuracy depends on the quality of your semantic layer. Start by curating your most important definitions (metrics, terms, domains). The system then learns from your existing data catalog and usage patterns.