How strategy, governance, and the right team turn GenAI into trusted results

Natalia emphasizes that scaling GenAI beyond proof of concept starts with quality data, strong governance, and cross-functional teams - aligning strategy, roles, and metadata to close the AI trust gap.

Problem statement

Too many organizations chase GenAI pilots without a strong data strategy and governance. Data domains aren’t defined, ownership is murky, metadata is inconsistent, and cost controls are missing. The result? Impressive demos, inconsistent outputs, surprise bills, and slow adoption. Without shared definitions and clear accountability, trust erodes – and scaling stalls.

Author’s perspective and expertise

Observations and learnings from recent projects

GenAI delivers lasting value only when strategy, governance, and a cross-functional team come together around clear business goals, and pragmatic guardrails for quality, risk, and cost.

Proposed solution: Align strategy and people, fix the data and manage metadata, then scale safely with simple, enforceable guardrails.

1. Bring strategy and people together from day one

• Define data domains and ownership: Appoint domain data owners and stewards; clarify decision rights and responsibilities.

• Tie the roadmap to real business goals: Which metrics must move – revenue, cost, risk, customer experience – and by when?

Example: An international retailer set up a cross-functional data team (data architects, data engineers, data scientists, domain owners), defined domains, and aligned the roadmap to merchandising and supply chain targets. The result was faster decisions and clearer accountability.

2. Start with data and metadata

• Establish common terms and master data: Agree on shared definitions for customers, products, vendors, and finance metrics; standardize reference data

• Improve quality and document lineage: Set quality rules and thresholds; track where data comes from, how it’s transformed, and who can use it

• Example: With an oil and gas client, the team aligned on one set of finance terms and KPIs so every decision used the same numbers. Discrepancies dropped, and analytics stabilized.

3. Scale safely with simple guardrails

• Set cost limits and FinOps practices: Budget model usage, track unit economics (inference cost per outcome), and avoid surprise bills.

• Standardize ways of working: Create lightweight processes for approvals, prompt and model versioning, change control, and evaluation.

• Build reusable assets: Launch data products and a data marketplace so teams can share and reuse governed datasets instead of rebuilding.

• Example: A pharma client used this approach to publish its first data products and a marketplace. Teams shared trusted data and avoided duplicate spend, with fewer downstream defects.

Team and controls that make it work

• Cross-functional team: Data architects and engineers, data scientists, analytics engineers, domain data owners and stewards, security/compliance, and FinOps.

• Essential tooling and controls: Data catalog and lineage, role-based access control, policy enforcement, qualitative and quantitative model evaluation, human-in-the-loop checkpoints, and observability (cost, quality, drift).

Measure what matters

• Business KPIs: Gross margin uplift, revenue per customer, operational cost reduction, cycle time improvements, compliance exceptions avoided.

• Data and model KPIs: Data freshness, completeness, accuracy, duplicate rate, model quality (precision/recall), time-to-value, unit cost per inference.

A word of caution

• Don’t lead with “tech only”: Without governance and ownership, GenAI will produce inconsistent outputs – faster.

• Avoid governance theatre: Policies in slide decks don’t help. Embed rules in pipelines, tools, and workflows so they’re automatically enforced.

• Watch hidden costs: Inference, integration, and compliance overhead can erode ROI. Track total cost of ownership against realized benefits.

• Beware fragmented metadata: If teams don’t share definitions and lineage, you’ll multiply rework and undermine trust.

• Change management matters: New ways of working require coaching, training, and clear incentives. Adoption is earned, not assumed.

Concluding point

GenAI is build–measure–learn. However, it only compounds value when strategy, governance, and people move in lockstep. Define your data domains and ownership, align on common terms and master data, enforce simple guardrails for quality and cost, and empower a cross-functional team from day one. Do that, and you will move beyond POC to trusted, durable results – faster and with fewer surprises.

Supporting perspectives

To see how strong data foundations translate into impact across the business:

• Applying GenAI to Strategic Initiatives like Revenue Optimization by Adam Rogalewicz shows how solid data, pricing, and trade terms come together to drive growth. Read Adam’s perspective.

• Automating and accelerating software development with AI agents by Wiktor Witkowski explains how engineering teams can compress design, testing, and delivery cycles—without sacrificing quality. Read Wiktor’s perspective.