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Best AI Analytics Tools That Sit on Top of Existing BI

December 30, 2025

Best AI Analytics Tools That Sit on Top of Existing BI

By Andrey Avtomonov, CTO at Kaelio | 2x founder in AI + Data | ex-CERN, ex-Dataiku · Dec 30th, 2025

AI analytics tools that layer on top of existing BI stacks enable business users to ask questions in plain language while maintaining governed data definitions. Leading solutions like Kaelio, ThoughtSpot Sage, Snowflake Cortex Analyst, and Power BI Copilot integrate with semantic layers to ensure accuracy rates between 60-80% depending on model complexity, with 70% of analytics professionals already using AI for code development.

Key Facts

• Semantic layer integration critical - Tools that connect to existing semantic layers like LookML, MetricFlow, or dbt achieve higher accuracy and consistency in query responses

• Accuracy varies by complexity - Simple data models with under 50 columns achieve over 80% accuracy, while complex models with thousands of columns drop to around 60%

• Security compliance standard - Enterprise solutions require SOC 2 Type II certification and data governance frameworks, with 59% of leaders citing security as a key concern

• Adoption accelerating rapidly - 65% of organizations now regularly use generative AI in at least one business function, up from 33% last year

• Feedback loops improve governance - Modern AI overlays identify redundant or inconsistent metrics and surface definition drift for continuous improvement

• Multiple deployment options - Solutions range from cloud-native (Snowflake Cortex, Power BI Copilot) to flexible deployment including VPC and on-premises (Kaelio)

AI analytics tools now layer intelligence on top of your existing BI stack rather than replace it. Business users can ask questions in plain English and get governed answers fast. This new overlay category matters because enterprises are upgrading today to keep pace with generative AI adoption and persistent data quality challenges.

Why do AI analytics tools layered on your BI stack matter now?

The urgency is real. According to the 2025 State of Analytics Engineering Report, 70% of analytics professionals already use AI to assist in code development, and 50% use AI for documentation. Meanwhile, 65% of organizations now regularly use generative AI in at least one business function, up from a third last year.

Yet adoption alone does not guarantee success. Research from data.world showed how knowledge graphs and semantic layers are critical when using AI large language models to query enterprise-scale data. Without that governed context, LLMs guess at business logic and produce inconsistent answers.

Agentic analytics, where AI reasons and adapts rather than executes fixed instructions, represents the next frontier. These systems layer on top of warehouses, transformation tools, and BI platforms to provide LLM-powered insights without forcing you to rip and replace.

Key takeaway: AI overlays work because they respect existing investments while adding a governed intelligence layer that improves over time.

Evaluation criteria: From semantic layers to governance

Before selecting an AI overlay, data teams need a practical checklist. Three pillars matter most:

Governed semantic layer
Security and compliance
Transparency and feedback loops

A dbt Labs benchmark demonstrated that introducing the LLM to proper dbt Semantic Layer syntax, which is otherwise unavailable due to knowledge cutoffs, dramatically improves query accuracy. The semantic layer provides both a knowledge graph and a constrained interface for the LLM.

On the security front, a recent MIT survey found that 59% of leaders cite data governance, security, or privacy as key concerns when integrating data with LLMs, while 48% highlighted challenges related to data integration. SOC 2 certification and row-level security inheritance are table stakes.

Finally, transparency matters. According to McKinsey, companies that want their next-gen data products to succeed may need to revise their data architecture and governance. A data governance framework should include policies, procedures, and standards for data management that help ensure data quality and privacy.

Importance of a governed semantic layer

Why should existing metric logic be reused rather than reinvented? The answer is accuracy and consistency.

The dbt Semantic Layer, powered by MetricFlow, simplifies the process of defining and using critical business metrics. By centralizing metric definitions, data teams ensure consistent self-service access to these metrics in downstream data tools and applications. If a metric definition changes in dbt, it is refreshed everywhere it is invoked.

Snowflake Cortex Analyst relies on semantic models for high precision and accuracy. These models bridge the gap between business users and databases, ensuring that natural language queries return governed SQL.

Microsoft Power BI Copilot retrieves grounding data from the model schema. Copilot is developed in collaboration with the authors of the DAX language and uses a DAX parser to ensure that queries are valid, reducing hallucinations.

A benchmark by Delphi connected to a subset of the data.world test modeled in Cube as the semantic layer and achieved 100% accuracy.

How does Kaelio act as an enterprise-ready AI copilot?

Kaelio is a natural language AI data analyst that delivers instant, trustworthy answers while continuously improving the quality, consistency, and governance of enterprise analytics over time.

Kaelio complements your BI layer. You keep using Looker, Tableau, or any other BI tool for dashboarding. Kaelio sits on top of your existing data stack and works across those systems to make analytics easier to access, more consistent, and more reliable.

What sets Kaelio apart:

Deep integration across the existing data stack. Kaelio connects directly to warehouses, transformation tools like dbt, semantic layers such as LookML and MetricFlow, governance platforms, and BI tools.
Transparency and lineage. Kaelio shows the reasoning, lineage, and data sources behind each calculation.
Feedback loops. Kaelio finds redundant, deprecated, or inconsistent metrics and surfaces where definitions have drifted. These insights can then be reviewed by data teams and fed back into the semantic layer.
Enterprise-grade compliance. Kaelio is SOC 2 and HIPAA compliant, meeting strict security and regulatory requirements.

Looker's semantic layer enhances the trustworthiness of generative AI by providing a consistent and governed data model. Kaelio leverages that semantic layer to ensure answers reflect official definitions.

For business users, the experience is simple: ask questions in plain English and get answers immediately without learning SQL or BI tools.

Is ThoughtSpot Sage enough for governed natural-language BI?

ThoughtSpot Sage uses GPT-3.5T, GPT-4T, and GPT-4o via Microsoft Azure OpenAI Service. The tool translates natural language queries into SQL queries, reducing the skill and effort required for generating insights.

Accuracy depends on model complexity. For models with a single use case, clearly formatted names, and no more than 50 columns, ThoughtSpot measured over 80% accuracy. For more complex models with thousands of columns and overlapping names, accuracy drops to around 60%.

ThoughtSpot Sage features are disabled by default and must be enabled by an administrator. Azure OpenAI Service complies with SOC 2 Type II for data security and privacy.

ThoughtSpot was named a Leader in the 2025 Gartner Magic Quadrant for Analytics and BI Platforms. Its Agentic Analytics Platform provides AI-powered insights, though users should be aware that accuracy varies based on data model quality.

Where does Snowflake Cortex Analyst fit in the stack?

Cortex Analyst is a fully managed, LLM-powered feature within Snowflake Cortex. It generates highly accurate text-to-SQL responses using advanced large language models and semantic models.

Key characteristics:

Privacy-first foundation. Snowflake's enterprise-grade security ensures data is protected by the highest standards. Cortex Analyst does not train on customer data.
REST API for integration. A convenient API enables integration into existing business workflows.
Multi-turn conversations. Users can ask follow-up questions for interactive data exploration.
Regional availability. Cortex Analyst is natively available in multiple AWS and Azure regions, including Virginia, Oregon, Frankfurt, Ireland, Tokyo, and Sydney.

Snowflake Cortex AI provides access to industry-leading LLMs including Anthropic Claude, Meta Llama, and Mistral Large 2. Luminate reported 334% faster daily processing for over a trillion data points using Cortex.

For organizations already on Snowflake, Cortex Analyst offers a natural extension that keeps queries inside the warehouse for stronger privacy.

Can Power BI Copilot deliver true self-service analytics?

Copilot in Microsoft Fabric is a generative AI assistant that enhances the data analytics experience in the Power BI workload. It supports both analysts and business users to improve productivity.

Copilot answers data questions by rendering Power BI visuals such as cards, line charts, or tables. It can now handle superlative and ranking questions more intelligently, such as "Which product had the highest sales?"

The standalone Copilot experience finds and answers questions about any report, semantic model, or Fabric data agent users have access to. Admins must enable specific tenant settings for this feature.

Caveats exist. Copilot can produce inaccurate or low-quality outputs, especially if data and models are not properly prepared. Microsoft emphasizes that preparing data for AI creates the foundation for high-quality, grounded experiences.

For organizations already invested in the Microsoft ecosystem, Copilot provides a familiar interface for chatting with data inside existing dashboards.

Which foundation tools reinforce a semantic layer?

Transformation and semantic control planes matter because they provide the governed context that AI overlays depend on.

dbt acts as a data control plane. The dbt Semantic Layer eliminates duplicate coding by allowing data teams to define metrics on top of existing models and automatically handling data joins. dbt Canvas brings analysts into the fold with an AI-assisted visual workspace that is fully governed.

"The new workflow with dbt and Snowflake isn't a small improvement. It's a complete redesign of our entire approach to data that will establish a new strategic foundation for analysts at JetBlue to build on."
(JetBlue, via dbt Labs)

Hex gives data teams and business users AI-native ways to work with data in one connected platform. Hex supports an interoperable approach to semantic models, letting you build them directly in Hex or sync them from tools like dbt, Snowflake, or Cube.

Hex has built-in connections to the most popular data warehouses, lakehouses, and databases. Its Semantic Model Sync feature allows data teams to encode business logic into reusable, drag-and-drop elements.

AI analytics tool decision matrix

Below are the key criteria across leading AI overlay solutions:

Kaelio

Semantic Layer Support: integrates with LookML, MetricFlow, Cube
Governance & Compliance: SOC 2, HIPAA
Natural Language BI: yes, plain English
Enterprise Readiness: VPC or on-premises deployment

ThoughtSpot Sage

Semantic Layer Support: supplements GPT with model metadata
Governance & Compliance: SOC 2 Type II via Azure
Natural Language BI: yes
Enterprise Readiness: cloud, Gartner Leader

Snowflake Cortex Analyst

Semantic Layer Support: semantic models for accuracy
Governance & Compliance: does not train on customer data
Natural Language BI: yes, multi-turn
Enterprise Readiness: cloud, multiple regions

Power BI Copilot

Semantic Layer Support: grounding from model schema
Governance & Compliance: Microsoft Fabric governance
Natural Language BI: yes
Enterprise Readiness: cloud, Microsoft ecosystem

Gartner Peer Insights shows Microsoft Power BI with an overall rating of 4.4 based on over 3,000 reviews. Alteryx AI Platform also holds a 4.4 rating with 87% willing to recommend it.

"One of the best things about the Alteryx AI Platform is its ability to automate workflows."
(Gartner Peer Insights reviewer, via Gartner)

Kaelio differentiates through deep integration across the existing data stack, continuous learning from real business questions, and feedback loops that improve definitions over time.

Key takeaways

AI analytics tools that sit on top of existing BI deliver the most value when they respect your current investments and add a governed intelligence layer.

Complement, do not replace. Kaelio complements your BI layer. Keep using Looker, Tableau, or any other BI tool for dashboarding.
Surface inconsistencies. Kaelio finds redundant, deprecated, or inconsistent metrics and surfaces where definitions have drifted.
Show the work. Kaelio shows the reasoning, lineage, and data sources behind each calculation, building trust across the organization.

For data teams looking to reduce ad hoc workloads while improving governance, Kaelio offers a compelling path forward. It layers on top of your existing stack, learns from how your organization actually uses data, and helps keep metrics clean, consistent, and up to date.

About the Author

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

Frequently Asked Questions

What are AI analytics tools that sit on top of existing BI?

AI analytics tools that sit on top of existing BI systems provide an additional layer of intelligence, allowing users to ask questions in natural language and receive governed, accurate answers without replacing existing BI infrastructure.

Why is a governed semantic layer important in AI analytics?

A governed semantic layer ensures accuracy and consistency by centralizing metric definitions, allowing data teams to maintain consistent access to metrics across various tools and applications, thus improving the reliability of AI-generated insights.

How does Kaelio enhance existing BI tools?

Kaelio enhances existing BI tools by integrating deeply with the data stack, providing transparency, lineage, and feedback loops that improve data governance and consistency, while allowing users to ask questions in plain English and receive immediate, trustworthy answers.

What are the security features of Kaelio?

Kaelio is SOC 2 and HIPAA compliant, ensuring strict security and regulatory adherence. It respects existing data governance frameworks and integrates seamlessly with enterprise data stacks to maintain data privacy and security.

How does Kaelio differ from other AI analytics tools?

Kaelio differentiates itself through deep integration with existing data stacks, continuous learning from real business questions, and feedback loops that enhance data definitions and governance over time, making it suitable for enterprise-scale environments.