Top 5 ai-native business intelligence and analytics tools

Business Intelligence (BI) has been an essential part of business strategy for decades, but the landscape is changing rapidly with the advent of artificial intelligence (AI). Since the launch of ChatGPT in November 2022, large language models (LLMs) have made AI more accessible and practical for businesses of all sizes. While AI has often been used as a marketing buzzword, LLMs are the first truly transformative technology that mimics human reasoning, offering unparalleled potential for enhancing decision-making processes.

As AI becomes a key differentiator, nearly every business intelligence platform has been racing to integrate it into their offerings. However, many of these tools were built long before the rise of true AI, limiting their ability to fully leverage its capabilities. This article explores the emerging field of AI-powered business intelligence, why AI-native platforms matter, and the top tools reshaping the market today.

What are Business Intelligence and analytics platforms?

Business intelligence and analytics platforms are tools and technologies that help organizations collect, process, and analyze data to drive informed decisions. At their core, these platforms transform raw data into actionable insights through dashboards, reports, and visualizations, empowering businesses to identify trends, monitor performance, and uncover growth opportunities.

For startups and high growth businesses, BI tools are particularly valuable. They enable data-driven decision-making without requiring a large technical team, leveling the playing field and offering a competitive edge in crowded markets. Whether it's optimizing marketing campaigns, streamlining operations, or forecasting revenue, business intelligence and analytics platforms are indispensable for high-performing organizations.

Why does AI-native matter (and its benefits)?

While traditional BI tools have been effective in providing insights, they often rely on legacy architectures that pose challenges in the AI era:

1. Legacy abstractions on SQL: Many BI platforms were designed to simplify SQL-based queries, building custom abstractions and intermediate languages that now limit flexibility. Instead of letting the AI generate SQL queries, which it’s well trained on, using troves of data on the web, it now has to accurately generate a different, more obscure, and oftentimes closed source, language.
2. Drag-and-drop interfaces: While user-friendly, these interfaces can restrict the depth of analysis and adaptability to modern AI capabilities. An AI interface requires an entirely different user experience and must be thought about from the ground-up.
3. SQL-centric workflows: Traditional tools focus heavily on SQL, which is great for pulling reports, but not so helpful when it comes to doing more advanced analysis or deriving insights from the data. For example, identifying outliers and generating data visualizations is best done in Python, which AI is very adept at generating.

AI-native platforms, on the other hand, are designed with modern AI at their core and built to leverage its strengths. These platforms break away from legacy constraints, offering features that are inherently more flexible, scalable, and user-friendly. They harness LLMs not just as add-ons but as integral components, delivering transformative capabilities like natural language queries, predictive analytics, and automated insights.

It's worth noting that some legacy platforms have developed entirely new AI-driven tools to stay competitive despite the rest of their platform existing long before ChatGPT. For the purposes of this discussion, "AI-native" refers to products built from scratch with AI as a core component, even if they’re part of a broader platform that pre-dates AI.

The benefits of AI integrated in business intelligence tools may seem obvious, but it's worth spelling out for completeness:

1. AI can help drastically boost developer productivity. Github published a research article showing that even experienced developers experience a 55% increase in task completion when using AI and Fabi.ai customers have reported a 90% decrease in analysis turnaround time.
2. AI can also help semi-technical individuals explore data on their own, increasing data literacy and decreasing the number of requests that get to the data team. Fabi.ai customers report an 80-90% decrease in tickets.
3. AI can empower the business team to explore data on their own. If the data is well defined and pre-vetted by the data team, a completely non-technical stakeholder can drill in on their own, giving them more confidence in the data.

Suffice to say, AI-native BI is gain rapid adoption and will likely accelerate through 2025.

A brief detour: Are LLMs really AI?

Modern LLMs, like OpenAI's GPT models or Anthropic’s Claude, are often considered a form of AI because they can perform tasks that typically require human intelligence, such as understanding and generating language. While the philosophical debate around what constitutes "intelligence" continues, LLMs stand out due to their ability to:

• Process and contextualize vast amounts of data
• Generate insights in natural language
• Adapt to diverse use cases across industries and modals

In contrast to traditional data science methods, which rely on predefined algorithms, LLMs learn from vast datasets, making them more flexible and adaptable. This distinction underpins their transformative potential in business intelligence.

Top AI-native Business Intelligence tools

To qualify as an AI-native BI tool, we’ve set certain criteria:

• Founded post-ChatGPT launch: The platform or tool should have been created during or after the rise of LLMs (circa 2022).
• Collaborative reporting: It must support the building and sharing of dashboards and reports within a collaborative environment. BI is only useful if insights can be shared with the business.
• Business-facing AI: The AI features should directly benefit non-technical business users, not just data analysts or engineers.

Here are the top AI-native BI tools reshaping the market as of January 2025:

Databricks AI/BI Genie (Starting at: ~$500-$1,500 per month)

Known for its robust data engineering and machine learning capabilities, Databricks has expanded into BI with its AI/BI Genie. This tool offers advanced predictive analytics and integrates AI directly into the reporting process, helping users make proactive decisions based on data trends. Great for teams who are already using Databricks and have the engineering resources to manage and maintain the AI.

A note on Databricks pricing: pricing is dynamic and highly dependent on the amount of data and type and frequency of processing. AI/BI Genie is included in the core platform price.

Snowflake Cortex Analyst (Starting at: ~$500-$1,500 per month)

Snowflake’s Cortex Analyst brings AI directly to your cloud environment. Designed for scalability, it uses LLMs to simplify complex queries and automate report generation, making insights accessible to both technical and non-technical users. Snowflake is a great option for teams that are already on Snowflake. However, it’s worth noting that building a BI dashboard with Snowflake Cortex Analyst requires a significant amount of engineering work, but they do offer step-by-step guides.

A note on Snowflake pricing: Similar to Databricks, pricing is highly dependent on your data and configuration and can fluctuate. Cortex Analyst is included as part of the core platform.

Zing Data (Starting at: Free then $12/mo/user)

Zing Data combines the simplicity of natural language querying with powerful BI features. Designed for teams on the go, with a mobile-first approach, it enables instant collaboration and real-time data exploration, leveraging AI to provide contextual recommendations and insights. This is a great option for teams that have consumers of BI reports in the field with online access to their phones (looking at you sales teams).

Zenlytic (Starting at: Undisclosed)

Although founded before the launch of ChatGPT, Zenlytic has embraced AI with a fresh perspective and was still early enough to embrace LLMs. Its AI-driven approach to BI focuses on making analytics more intuitive for business users in specific verticals, ensuring that everyone in the organization can derive value from data.

Fabi.ai (Starting at: Free then $199/mo for 4 builders)

We’ve built Fabi.ai to embrace AI from the very start, both for the builders of the reports as well as the business consumers. Data analysts, scientists and engineers can leverage AI code-assistant for both SQL and Python in Smartbooks in order to analyze their data no matter how messy the data is and share AI-powered reports with their stakeholders in just a few clicks. Fabi.ai is designed for teams that are looking to boost the data team’s productivity, increase collaboration and foster a data-driven culture in their organization and it works on any data source.

The Future of AI in Business Intelligence

The integration of AI into business intelligence is still nascent, but we’re going to start seeing a rapid transformation with how data teams and their stakeholders interact with data. As AI-native platforms continue to evolve, they promise not only to boost productivity for data practitioners, but also make data more accessible to a wider audience in the enterprise. Transitioning to these platforms do present their own set of challenges, starting with a shift in mindset and the building out of sufficiently wide and clean tables for the AI to leverage.

At Fabi.ai, we're proud to lead this transformation. If you want to try it out and see what AI can do for your team, you can get started for free in less than 5 minutes.