The Top 10 AI Business Intelligence Tools in 2025
Oct 10, 2025
Business Intelligence (BI) is undergoing a revolution. For years, companies relied on manual reporting or legacy BI systems that were powerful but complex. Now, AI has entered the scene, changing how teams interact with data, build dashboards, and make decisions.
But not all “AI BI tools” are the same. Some are legacy platforms with AI add-ons, while a new generation is AI-native.
Here are the Top 10 AI Business Intelligence Tools in 2025, with pricing, strengths, and limitations, plus why Dama is taking a different approach.
1. ThoughtSpot
Overview: ThoughtSpot pioneered search-driven analytics, letting users type questions and instantly generate charts. With its AI engine, SpotIQ, it now also delivers automated insights and anomaly detection.
AI Features:
Natural language search for data
SpotIQ auto-insights
Integration with cloud warehouses (Snowflake, BigQuery, Redshift)
Pricing: Starts around $1,250/month for the Essentials plan (~20 users). Enterprise pricing scales higher. See pricing
Pros:
✅ Strong NLQ (natural language query) capabilities
✅ Robust connectors for cloud data
✅ Enterprise-ready
Cons:
❌ Pricing scales quickly
❌ May be overkill for smaller teams
Best for: Mid-to-large enterprises that want powerful search-driven analytics.
2. Microsoft Power BI (with Copilot)
Overview: Microsoft Power BI is one of the most widely used BI tools. In 2024–2025, Microsoft rolled out Copilot in Power BI, which generates visuals, DAX measures, and summaries using AI.
AI Features:
Copilot to create visuals and summaries in natural language
Deep integration with Microsoft Fabric and Azure AI
Auto-generated narrative insights
Pricing:
Power BI Pro: $14/user/month
Premium Per User: $24/user/month
Enterprise Premium capacity: custom pricing
Pros:
✅ Affordable entry point
✅ Rich ecosystem with Office + Azure
✅ Copilot accelerates dashboard building
Cons:
❌ Copilot consumes compute capacity (performance bottlenecks)
❌ Still a legacy BI experience with AI layered on
Best for: Organizations already deep in Microsoft’s ecosystem.
3. Tableau (with AI & Tableau+)
Overview: Tableau, now under Salesforce, has integrated Tableau AI and Einstein Copilot into its analytics stack. The Tableau+ bundle enhances AI-driven experiences across the Salesforce ecosystem.
AI Features:
Einstein Copilot suggests dashboards, metrics, and explanations
Automated forecasting and predictions
Visual explainability tools
Pricing:
Creator: $75/user/month
Explorer: $42/user/month
Viewer: $15/user/month
Pros:
✅ Strong visualization & customization options
✅ Integration with Salesforce data
✅ Growing AI features
Cons:
❌ Pricing is high for scaling across large teams
❌ AI feels bolted-on compared to AI-native tools
Best for: Teams that value visual customization and are invested in Salesforce.
4. Qlik Sense
Overview: Qlik has been in BI for decades. Its associative engine makes exploring data easier, and with recent AI augmentation, Qlik Sense now includes natural language insights and AI-driven suggestions.
AI Features:
Augmented analytics with AI suggestions
Natural language interaction
Strong governance & enterprise features
Pricing: Quote-based (starts around $30–$70/user/month in practice). More info
Pros:
✅ Great data modeling engine
✅ Strong governance for enterprises
✅ Mature platform
Cons:
❌ Not as intuitive for non-technical users
❌ AI features limited vs. new-gen platforms
Best for: Large organizations needing governed, enterprise BI.
5. Sisense
Overview: Sisense is known for embedded analytics — putting BI directly inside customer-facing apps. Its AI features now focus on augmenting insights for business users and developers alike.
AI Features:
“Infusion Apps” to embed AI-driven insights into workflows
Augmented analytics with NLP
Developer-friendly customization
Pricing: Custom quotes only. Mid-market deployments often cost $20k–$50k+/year. Sisense website
Pros:
✅ Strong embedded analytics focus
✅ Customizable for developers
✅ Good for SaaS companies embedding BI
Cons:
❌ Complex setup
❌ Less suited to small teams
Best for: SaaS platforms embedding AI analytics for customers.
6. Domo
Overview: Domo is a cloud-native BI tool focused on usability. It’s been expanding AI-driven modules for auto-generated insights and alerts.
AI Features:
Auto-generated visualizations
Alerts and anomaly detection
Prebuilt apps for specific use cases
Pricing: Custom quotes. Typically $30–$60/user/month. Learn more
Pros:
✅ User-friendly
✅ Wide integration library
✅ Cloud-first design
Cons:
❌ Expensive at scale
❌ AI feels assistive, not native
Best for: Teams that want a simple, cloud BI tool with expanding AI.
7. Looker (Google Cloud)
Overview: Looker (by Google) remains popular for semantic modeling and governed data access. AI integration is deepening through Google Cloud’s AI suite.
AI Features:
Natural language exploration
Integration with Google Vertex AI
Strong semantic layer for modeling
Pricing: Quote-based. Entry-level deployments often start around $30–$60/user/month. Looker
Pros:
✅ Strong governance & modeling
✅ Tight GCP integration
✅ Expanding AI features
Cons:
❌ Steeper learning curve
❌ AI more of an add-on than a core design
Best for: Data-driven teams on Google Cloud.
8. Holistics
Overview: Holistics is a developer-friendly BI tool that now offers AI-powered analytics and a natural language interface.
AI Features:
Natural language dashboard generation
Data modeling flexibility
Embedded analytics support
Pricing: Subscription-based, typically $50–$100/user/month. Holistics
Pros:
✅ Lightweight compared to big enterprise BI
✅ Flexible developer tools
✅ Emerging AI features
Cons:
❌ Not as enterprise-ready
❌ More technical setup required
Best for: Small-to-mid teams with technical resources.
9. Sigma Computing
Overview: Sigma is famous for its spreadsheet-like BI interface, making it easier for business users familiar with Excel. It has been rolling out AI to accelerate dashboard creation.
AI Features:
Spreadsheet-style interface with AI automation
NLP for querying data
Cloud-native architecture
Pricing: Quote-based, usually $25–$70/user/month. Sigma
Pros:
✅ Familiar interface (Excel-like)
✅ Easy for business users
✅ Strong for warehouse-native BI
Cons:
❌ Still requires warehouse setup
❌ AI features less advanced
Best for: Teams that love Excel but want cloud BI with AI add-ons.
10. Dama (AI-Native BI) 🚀
Overview: Unlike most tools above, Dama isn’t adding AI onto a legacy BI tool — it’s AI-native. Built with large language models in its backbone, Dama handles the entire BI workflow from ingestion → pipelines → dashboards in a single, AI-driven platform.
AI Features:
Ask in plain English → get a dashboard instantly
Context-aware AI that learns your schema & business rules
Human-in-the-loop: full transparency into queries
All-in-one: data import, pipelines, dashboards
Pricing: Usage-based, starting around $10k–$40k/year (cheaper than hiring a data engineer).
Pros:
✅ Built AI-first (not a bolt-on)
✅ All-in-one, not a fragmented stack
✅ Designed for non-technical teams
✅ Minutes to setup, insights on day one
Cons:
❌ Still in Beta (2025) → but rapidly evolving
❌ Best suited for startups and scale-ups right now (enterprise roadmap 2026+)
Best for: Startups, scale-ups, and fast-moving teams who want BI without the complexity of legacy stacks.
Final Thoughts
Most of the “AI BI tools” on this list are legacy BI platforms with AI add-ons. They’re powerful, but still complex, expensive, and often inaccessible to non-technical teams.
Dama is different. As an AI-native BI tool, it’s designed for the AI era, fast, transparent, and built to finally make data work for everyone.
🔗 Try it out: getdama.com
💡 Or book a demo: https://cal.com/dama-datamagic/product-demo