For many years, business analytics had a simple shape. A company collected data, placed it in a warehouse, built dashboards, and asked people to read the results. Managers then used those reports to decide what to do next. This model worked, but it had one major limit. Data could show what happened, yet people still had to find out why it happened and what action made sense.
AI is changing that model. A new group of analytics startups now wants to take data much closer to the final business decision. Their products do more than create charts or answer simple questions. They can study large sets of data, find unusual changes, explain possible causes, and in some cases suggest the next action. The goal is simple: make business data useful at the exact moment a decision must be made.
This change has created a new market around AI-native analytics. It also puts pressure on older business intelligence companies such as Tableau, Qlik, Domo and ThoughtSpot. At the same time, startups such as Sigma, Sundial, Golden Analytics, Gravity and Credible Data show how fast the category is changing.
The Old Dashboard Model Is Changing
Traditional business intelligence follows a familiar path. A person asks a question, a data analyst writes SQL, data is prepared, a chart is made, and a manager reads the result. If the manager has another question, the analyst may need to repeat the process.
AI can shorten this cycle. A business user can ask a question in plain English and receive an answer without a long data request. More advanced AI systems can also explore the data on their own. They can check several possible causes, compare periods, spot unusual values and create a report.
TechTarget describes this as a major shift for the BI market. The old challenge was the translation of business questions into SQL, data models and charts, followed by the translation of results back into useful business insight. Generative AI can reduce much of that work, while AI agents can take the process a step further by carrying out analysis without a new prompt for every task.
This does not mean dashboards will vanish. Companies still need repeatable reports for sales, finance, operations and executive reviews. The change is that a dashboard is no longer the only way to understand company data.
Sigma Shows the Scale of the New Market
Sigma has become one of the clearest examples of this shift. In May 2026, the company announced an $80 million Series E at a $3 billion valuation. Sigma also said it had passed $200 million in annual recurring revenue in April 2026, with more than 2,000 customers worldwide and more than 100% year-over-year growth in its latest fiscal year. The company also reported more than 1.1 million new active users during that period.
Sigma now presents itself as an AI Apps and agentic analytics platform. Its larger idea goes beyond the normal dashboard. A company can place its data in a cloud warehouse and then create analytics, apps and AI agents on top of that data.
This matters because a business may not want a report that only says sales fell. It may want a system that notices the fall, checks the cause, tells the right person and helps start the next step.
Sigma calls this a governed runtime for business. The idea is that AI work should still follow company rules for access, security and data control. This is important because enterprise customers cannot simply give an AI system unlimited access to sensitive company data.
Sundial Focuses on the Decision
Sundial takes a similar idea but puts the business decision at the center. In 2026, the company raised $16 million in Series A funding, which brought its total funding to $23 million. The round was led by DJ Patil at GPV. Sundial says its main goal is to reduce the time needed to reach quality decisions, rather than simply give users more charts.
The company was founded by former Meta executives, including Julie Zhuo and Chandra Narayanan. Its platform brings data work, analytical methods and AI into one place.
Sundial has also added several features through 2026 that show where AI analytics may go next. Teams can now set scheduled routines for daily, weekly or monthly analysis. These routines can send short updates to Slack. The platform can also connect with Google Workspace, GitHub, Slack, Notion and Linear, so an analysis can include both company data and the documents or discussions that explain that data.
This is a key change. A number alone rarely tells the full story. If revenue falls, the reason may sit inside a product document, a customer note, a software change or a team discussion. AI analytics tools now seek that wider context.
Business Context May Become the Real Moat
One of the hardest problems in enterprise AI is not access to data. It is understanding what the data actually means.
A company may have a metric called revenue, but that word can mean different things across teams. Finance may have one definition. Sales may use another. A product team may look at a related measure with a different date rule.
An AI system that does not know these details can give an answer that looks correct but is still wrong for the business.
Credible Data is focused on this problem. In July 2026, the company raised a $10 million seed round from Gradient, SignalFire and K5 Global, along with angel investors. Its platform uses an open-source semantic modeling language called Malloy to turn company-specific metrics, definitions, entities and relationships into business context for AI agents, analytics tools and applications.
This could become one of the most important parts of the AI analytics market. The value may not sit only in the AI model. It may sit in the trusted layer that tells the model what each metric means and how company data connects.
ThoughtSpot and Mode Show Another Path
The market is also moving toward a mix of AI, analyst tools and self-service BI. ThoughtSpot’s $200 million acquisition of Mode is a strong example.
Mode is known for code-first analytics, with SQL, Python and R tools for data teams. ThoughtSpot has focused more on natural-language analytics and business users. The combined product seeks to connect both sides.
A data team can explore data with code, create trusted models and then make those results available to business users through natural-language search. This creates a bridge between expert analysis and everyday business questions.
That model matters because most large companies will not remove their data teams. Instead, AI may help those teams handle routine work while they spend more time on complex problems, data quality and business strategy.
New Startups Are Taking Aim at Legacy BI
AI-native startups have one clear advantage: they do not need to carry decades of old product design.
Golden Analytics is one example. The company came out of stealth in April 2026 and launched a public beta in June. It raised $21 million through two seed rounds during the year. Its CEO, François Ajenstat, previously served as chief product officer at Tableau and Amplitude.
Golden creates charts and written analysis from user questions. It also has a control that lets users decide how much work the AI should do itself. This gives analysts more control instead of forcing them to accept a fully automatic process.
Gravity takes a more autonomous approach. Its Orion product acts as a virtual analytics co-worker. It can perform recurring analysis, investigate unusual results, study possible root causes and prepare reports, slide decks and dashboards. Gravity raised a second round in April 2026, taking its total funding to $10 million.
These products show two possible futures. One gives analysts a powerful AI assistant. The other gives companies an AI analyst that can look for important changes without waiting for a question.
Analysts Are Not Going Away
The rise of AI analytics does not mean the end of the data analyst.
Large companies still need people who understand data quality, business rules, statistics, customer behavior and company strategy. AI can handle routine analysis, but a human still needs to judge whether a result makes sense and whether a proposed action fits the business.
TechTarget notes that large companies are already testing AI-native analytics tools, but most are unlikely to move their full analytics setup to a new platform at once. Existing BI products still have a strong role in KPI reports, repeatable dashboards and strategic reviews.
The likely result is a new division of work. AI can handle more of the routine analysis. Analysts can focus on harder questions. Business leaders can get answers without waiting days for a report.
The Move From Insight to Action
The most important change may be the move from insight to action.
A normal dashboard may tell a sales leader that revenue fell 8%. An AI analytics system could go further. It could find that most of the decline came from one country, one product or one day. It could check whether the result came from a real business issue or a data problem. It could then prepare a short explanation for the sales team.
The next step could be an action. An AI agent might send an alert, create a task, update a workflow or prepare a report for a manager.
Sigma already describes this direction through its AI Apps and agents, while Sundial has added scheduled analysis and connected business context. These products point toward analytics as a part of daily work rather than a separate reporting task.
Vertical AI Analytics Could Grow Fast
Another major opportunity is vertical software. Instead of creating one general AI analyst for every company, a startup can focus on one industry or function.
Finance is a strong example because banks, insurers and other regulated firms have strict rules around data, risk and decisions. A vertical product can include industry terms, workflows and controls from the start.
The same idea can apply to healthcare, supply chains, sales, marketing, pricing and customer success. A specialist system may know the key metrics and common problems of its market much better than a general analytics product.
This could create a market with both broad platforms and specialist tools. Large platforms may serve many teams, while smaller startups may focus on one high-value business decision.
Trust Will Decide the Next Phase
The biggest challenge for AI analytics is trust.
A wrong chart can waste time. A wrong AI decision can cost money, hurt a customer relationship or create a serious compliance problem. Companies therefore need clear data definitions, source records, access controls and audit trails.
This is why semantic models and governance have become central parts of the market. Credible Data focuses on trusted business context, while Sigma stresses governance and controlled access. Sundial has also added workspace controls that let administrators limit access to specific databases, schemas, metrics, models and documents.
The winners in this market will therefore need more than strong AI. They will need reliable data, clear business context and enough control for companies to trust the result.
A New Era for Business Analytics
AI analytics startups are changing the role of business intelligence. The old model placed the dashboard at the center. The new model places the decision at the center.
Sigma has shown that the market can support a large AI analytics platform, with $200 million in ARR and a $3 billion valuation reported in May 2026. Sundial has raised $23 million around its decision-focused approach. Credible Data has raised $10 million around trusted business context. ThoughtSpot has paid $200 million for Mode to connect expert analysis with AI-powered self-service BI. Golden Analytics and Gravity show how new AI-native products can challenge older BI models.
The common idea is clear. Companies do not need more data for its own sake. They need better answers from the data they already have.
The next generation of analytics may therefore look less like a wall of dashboards and more like a digital business analyst. It can watch key numbers, find unusual changes, explain what may have caused them and help a team decide what to do next.
That is the real shift from dashboards to business decisions. The value of analytics is no longer just in showing what happened. It is in helping people understand what matters and act on it with greater speed and confidence.
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