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How to Choose an Analytics Copilot You Can Actually Trust

January 7, 2026

How to Choose an Analytics Copilot You Can Actually Trust

By Andrey Avtomonov, CTO at Kaelio | 2x founder in AI + Data | ex-CERN, ex-Dataiku · Jan 7th, 2026

Analytics copilots promise instant answers to business questions, but only if they're accurate, consistent, and respect existing governance. Trustworthy AI-powered analytics requires semantic layers that bridge raw data and meaningful insights, ensuring AI and BI systems interpret information consistently. Kaelio exemplifies this approach by integrating with existing semantic layers rather than replacing them, inheriting governance controls while providing transparency into every calculation.

At a Glance

• AI data analyst tools achieve 50-89% accuracy depending on query complexity, with multi-table enterprise analytics dropping to around 50% without proper semantic context

• Semantic views capture critical metadata like synonyms, sample values, and verified queries needed for consistent AI-powered analytics

• Row-level security configurations can handle thousands of groups, ensuring data access restrictions are maintained automatically

• Leading BI platforms (Looker, Power BI, Tableau, ThoughtSpot) achieve 4.4-4.6 ratings but often struggle with governance gaps and high learning curves

• Healthcare organizations report 73% positive ROI in the first year, with radiology AI delivering 451-791% ROI over 5 years

• Kaelio differentiates by showing reasoning, lineage, and data sources behind each calculation while inheriting existing permissions and controls

An analytics copilot promises instant answers to your business questions. Yet the promise only holds if the answers are accurate, consistent, and respect your existing governance. This guide walks you through a five-step checklist for vetting any analytics copilot, using Kaelio as the north-star example of what trustworthy AI-powered analytics looks like.

What Exactly Is an Analytics Copilot—and Why Does Trust Matter?

An analytics copilot is software that "perceives, makes decisions, and acts" on those decisions by combining insights from large language models with the ability to execute through tools and data sources. In plain terms, it turns plain-English questions into governed SQL, surfaces results with lineage, then learns from every interaction to improve definitions over time.

Trust hinges on two pillars:

Data accuracy. AI data analyst tools achieve between 50-89% accuracy depending on query complexity. Simple lookups perform well, but multi-table enterprise analytics can drop to around 50%.
Semantic context. A semantic layer is a business-friendly abstraction between your warehouse and your BI or AI tools. Without it, copilots guess at business logic, producing inconsistent answers across teams.

The stakes are real. "AI-powered business intelligence has the potential to reshape the way we work with data," but only when answers reflect official definitions and remain reproducible.

How Do Governance, Security, and Privacy Decide Your Copilot's Fate?

Governance is not a nice-to-have checkbox. IBM earned Leader status in the 2024 Gartner Magic Quadrant for Data and Analytics Governance Platforms by demonstrating a principle that applies to every analytics copilot: "The effectiveness and trustworthiness of AI systems are intrinsically tied to the quality and governance of the data on which they are built."

Gartner's AI TRiSM framework spells out the two primary risks:

Compromise of sensitive data through oversharing and a lack of controls.
Inaccurate, hallucinatory, or otherwise unwanted results that lead to bad outcomes.

AI TRiSM "ensures governance, trustworthiness, fairness, reliability and data protection in AI deployments." Any copilot you adopt must inherit, never reinvent, these controls. Otherwise, it erodes confidence instead of building it.

Row-level security keeps sensitive slices safe

Row-level security (RLS) restricts access to table row data at the group or user level. Strict RLS is the default setting applied to clusters, ensuring that if an RLS rule is defined, it is always included in any query to the database.

Practical benchmarks matter: an RLS configuration can handle thousands of groups, making it flexible and self-maintaining for large enterprises. A trustworthy copilot should respect these rules automatically, never exposing data a user is not permitted to see.

Key takeaway: Verify that any copilot inherits your existing RLS policies rather than requiring you to rebuild them inside another system.

Why Must Your Copilot Integrate With the Existing Semantic Layer?

Semantic layers bridge the gap between raw data and meaningful insights, helping ensure that both AI and BI systems interpret information consistently and accurately.

In Snowflake, for example, semantic views capture metadata required for consistent AI-powered analytics, such as synonyms, sample values, and verified queries.

Semantic-aware copilots improve accuracy because they combine LLM reasoning with rule-based definitions rather than guessing business logic. MetricFlow, which powers the dbt Semantic Layer, handles SQL query construction and defines the specification for semantic models and metrics. It supports different metric types, including conversion, cumulative, derived, ratio, and simple, simplifying the SQL process via YAML configurations.

A copilot that sits on top of, rather than beside, your semantic layer gains immediate access to:

Unified metric definitions across business units.
Governance and access policies.
Synonyms and natural-language metadata that improve query interpretation.

Why text-to-SQL accuracy lives or dies on semantics

Cortex Analyst is a Snowflake tool that evaluates text-to-SQL accuracy in real-world BI scenarios by comparing generated SQL queries against benchmark queries. The tool demonstrates that the gap between natural language and correct SQL narrows dramatically when semantic context is available.

Semantic layers significantly boost accuracy by providing consistent data definitions and eliminating ambiguous business logic interpretation. Without them, text-to-SQL tools hallucinate and return inconsistent results, transforming from a Peak of Inflated Expectations into a liability.

Which BI Copilots Lead—And Where Do They Fall Short?

A balanced comparison helps you understand what the market offers and where gaps remain.

Looker holds an overall rating of 4.5 based on 817 reviews, with 86% willing to recommend it. Users praise its drag-and-drop dashboard creation.
Microsoft Power BI carries an overall rating of 4.4 based on 3,190 reviews, with 84% willing to recommend. It excels in Microsoft ecosystem integration and remote access without VPN.
Tableau (Salesforce) has an overall rating of 4.4 based on 4,157 reviews, with 82% willing to recommend. Reviewers highlight its proactive metric alerts and workflow integration.
ThoughtSpot achieves an overall rating of 4.6 based on 398 reviews, with 89% willing to recommend. Users note its natural-language querying and speed to insights.

Where most copilots stumble

Peer reviews surface recurring themes:

Governance gaps. "AI query accuracy requires business context; natural language query tools hallucinate and return inconsistent results without semantic governance."
High learning curves. One Gartner review summarizes Looker as "High Learning Curve But Delivers Great Insights."
Data lag. Some platforms require users to wait days before accessing up-to-date data, a dealbreaker for real-time decision-making.

Integration architecture determines real value. Integration barriers remain a primary adoption obstacle, making platform compatibility with your existing data infrastructure the critical selection factor.

Why Kaelio Scores Highest on Trust, Transparency, and Long-Term Value

Kaelio is designed for enterprises that already have semantic layers, governance platforms, and BI tools in place. Rather than replacing existing tools, it "empowers serious data teams to reduce their backlogs and better serve business teams."

What sets Kaelio apart:

Transparency. Kaelio "shows the reasoning, lineage, and data sources behind each calculation." Users see exactly how numbers were computed and where they came from.
Semantic-layer agnostic. Kaelio integrates with LookML, MetricFlow, Cube, and Kyvos, ensuring consistent metrics without duplicating definitions.
Governance inheritance. Permissions, roles, and row-level security flow from your existing stack. Kaelio generates queries that respect existing controls.
Continuous feedback loops. As users ask questions, Kaelio captures where definitions are unclear or duplicated, feeding insights back to data teams for improvement.

Semantic views, natively available in Snowflake's Cortex Analyst experience, are critical to delivering high-quality conversational analytics. Kaelio leverages these foundations rather than competing with them.

What ROI Can Decision Intelligence Deliver in Healthcare and Beyond?

Decision intelligence spans industries. The numbers below illustrate what organizations can expect.

Time savings per user: 9 hours per month (Forrester TEI study)
Worker output improvement: 75% report speed or quality gains (OpenAI Enterprise Report)
Healthcare ROI, first year: 73% report positive returns (Google Cloud)
Radiology AI ROI over 5 years: 451%, rising to 791% with radiologist time savings (ScienceDirect)

In healthcare specifically, AI scribes produced estimated time savings in documentation of more than 15,700 hours, equivalent to 1,794 working days, over one year of use. These gains translate directly to reduced burnout and improved patient care.

Key takeaway: The ROI of AI-powered analytics is proven, but only when the underlying copilot is trustworthy enough to be adopted at scale.

Putting It All Together

Here is your five-step checklist for choosing an analytics copilot you can actually trust:

Accuracy baseline. Ask vendors for real-world accuracy benchmarks, not lab results. Remember that multi-table enterprise analytics can drop to 50% without proper context.
Governance inheritance. Confirm the copilot inherits permissions, RLS, and compliance controls from your existing stack.
Semantic-layer integration. Ensure the tool connects to your semantic layer rather than building a parallel one.
Transparency and lineage. Demand full visibility into how every answer is computed.
Feedback loops. Look for continuous learning that surfaces definition gaps back to your data team.

Without continuous monitoring and feedback loops, even the best AI analytics tools will degrade. Kaelio was built to address each of these criteria, integrating with your existing infrastructure while surfacing reasoning, lineage, and data sources behind every answer.

Ready to see the difference? Visit Kaelio to explore how a trustworthy analytics copilot can reduce your team's backlog and deliver answers your stakeholders can rely on.

About the Author

Former AI CTO with 15+ years of experience in data engineering and analytics.

Frequently Asked Questions

What is an analytics copilot?

An analytics copilot is software that uses AI to interpret plain-English questions into governed SQL, providing accurate and consistent data insights while learning from interactions to improve over time.

Why is trust important in an analytics copilot?

Trust is crucial because it ensures that the analytics copilot provides accurate, consistent, and governed answers, which are essential for making reliable business decisions.

How does Kaelio ensure data governance and security?

Kaelio inherits existing governance policies, including row-level security and compliance controls, ensuring that data access and usage are secure and aligned with organizational standards.

What role does a semantic layer play in analytics copilots?

A semantic layer bridges raw data and insights, ensuring consistent interpretation of data across AI and BI systems, which enhances the accuracy and reliability of analytics copilots.

How does Kaelio differentiate from other analytics tools?

Kaelio stands out by integrating with existing semantic layers and governance systems, providing transparency, and continuously learning from user interactions to improve data definitions and governance.