How to fix inconsistent AI answers: Start with governance in BI

Beata Socha

September 25, 2025

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In the latest global survey, 43.4% of organizationsreported "inaccurate or inconsistent answers" as a core obstacle to scaling AI-powered analytics, eroding trust and slowing decisions that require additional validation.

Source: Governance first: the key to scalable, trusted AI+BI

Why AI delivers inconsistent responses

Enterprises use unique analytics stacks to collect, analyze, and act on data. They embed business logic into individual tools or data layers within the stack, using this logic to process information and reveal insights. If the same metrics and KPIs live in multiple tools, they can diverge.

For example, an organization's marketing and customer support teams might use different tools and define a "qualified lead"differently. So, when using their AI interface to ask, "How many qualified leads did we have in Q2?", both teams receive inconsistent answers.

The result? A fragile analytics workflow that requires manual quality assurance before any decision can be made.

The impact of inconsistent AI analytics-data chaos

Teams lose time reconciling numbers that should match. Conflicting definitions for revenue, inventory, or customer value create rework and distrust between departments. As one survey participant put it, "Most things need to be quality assured. This is still time-consuming."

A governance-first fix: The universal semantic layer

The whitepaper outlines a governance-first approach centered on Strategy Mosaic, the world's first universal semantic layer.Instead of scattering logic across tools, Mosaic defines business concepts once and applies them everywhere-across BI dashboards, AI agents, and custom applications.

This eliminates contradictory answers and reduces the need for manual checks.

- Saurabh Abhyankar, Chief Product Officer, Strategy.

Mosaic's reusable data models also improve AI quality. By feeding AI systems a governed semantic model rather than raw, unmanaged data, organizations lower the risk of inconsistencies and ensure that the same metric is calculated the same way-regardless of user, tool, or query.

The result: Faster time-to-trust and confident decisions at scale

Building and maintaining a shared model is traditionally a labor-intensive task. Data experts spend hours validating metrics and become bottlenecks, while business users wait to receive insights.

Mosaic's AI-powered modeling studio accelerates the initial build, delivering the following benefits:

• Reliable decision-making:Executives can compare metrics across tools with confidence.

• Reduced rework:Experts spend less time reconciling definitions or running redundant QA cycles.

• Improved AI outcomes:Governed inputs reduce spurious results and improve explainability for all users.

Simply put, work that once took days can be completed in minutes. Mosaic's AI-powered modeling studio reduces human error and equips users with a consistent, certified data model.

- Saurabh Abhyankar, Chief Product Officer, Strategy.

Read the full analysis: Governance first for trusted AI + BI

Here's the bottom line: AI is only as good as the data that powers it. Teams need the best, most accurate AI answers to unlock insights and power decisions.

With a governance-first semantic layer, organizations can deliver accurate, governed, and consistent AI output across teams, tools, and locations. They can restore consistency across the analytics stack and provide a foundation for trustworthy AI for years to come.