Data-driven decision-making isn't just best practice; it's a survival imperative. Business leaders are under immense pressure to back their arguments with data - 76% feel this acutely, according to a Salesforce survey. And while the volume of raw business data continues to mount, leaders' confidence in using their data for decision-making has dropped significantly, down 18% from 2023, to less than half of leaders overall.
This uncertainty is stifling executives' ability to navigate today's uncertain times.
"Most executives don't have their own data analysts on call," says Southard Jones, chief product officer of Tableau. "They also don't have the training they need to be really confident that they and their team are using the right data to help make the right decisions, especially as these decisions become more involved and more complex."
The solution lies in agentic analytics, the next evolution of business intelligence (BI). With agentic analytics, any business user - regardless of how data savvy they are - can collaborate with autonomous AI agents to automate repetitive, manual tasks like data preparation, and enable AI-powered insights and recommended actions delivered proactively into their preferred workflow.
Bridging the trust gap with agentic AI
Business leaders often leave valuable data on the table because it's too intimidating, complex or time-consuming to dig into. AI agents are the key to bridging this data-to-insight gap.
Solutions like Tableau Next, Salesforce's agentic analytics solution, proactively identify patterns and anomalies in the data, and with business metrics that users might not think to ask about. Through a native integration with Agentforce, Salesforce's digital labor platform, Tableau Next leverages AI agents to deliver insights in natural language, within a company's daily workflow through any app on the platform -- even without a specific inquiry.
That's important because one-third of business leaders say they don't even know what questions to ask their data, with execs and VPs feeling particularly adrift. Agentic analytics, running quietly and autonomously in the background, solves that problem, surfacing key information a leader needs to know about their business. And that's how to rebuild trust between a business leader and the data they rely on, Jones says. He likens it to navigating with a mapping app. There's no need to request continuous updates on better routes or potential traffic slowdowns, because the app has your back, reasoning over your data and keeping tabs at all times.
"When AI agents are running behind the scenes, it should be able to tell you whenever something critical is happening in your business," he says. "That's changing the game, democratizing data access."
When agentic analytics is working behind the scenes, it's also dramatically speeding up time to action, bringing recommendations along with its insights. For instance, Tableau Next features a skill -- a task or job that an AI agent can perform -- to execute data inspections.
The Agentforce Inspector continuously tracks a company's data for key changes, analyzes trends and predicts improvements to address concerns. For example, it can proactively notify a business about an increase in bugs found in a new product. Or, after noting a sudden sales decline, recommend launching a targeted marketing campaign to specific customer segments.
In addition to proactively surfacing insights, with Tableau Next's pre-built Agentforce Concierge skill, users can write a question in their own words and get both a written insight and an interactive data visualization in response, making it easy to understand, as quickly as possible. This moves beyond static dashboards many executives say aren't useful.
As Jones sees it, "if you ask most business executives today, they'd probably tell you they have too many dashboards. The disconnect is that a dashboard was probably created one month ago to answer a business question that's no longer relevant."
This dynamic, conversational approach allows for immediate answers that keep pace with evolving business needs, unlike static dashboards that quickly become outdated.
Tableau Next also eliminates the friction that comes when a user switches over to a dashboard in the middle of a workflow. Instead, agents bring insights to people where they work, whether that's Slack or Teams, email, Salesforce Sales Cloud or other applications.
This abstracts away all the effort it takes to dig into data to find answers, whether that's building a visualization or asking a question, making it easy for leaders to get trusted insights from their data.
Turning data into AI-driven insight
While agentic analytics holds immense promise for transforming raw data into actionable insights in any business workflow, its effectiveness hinges on the state of the underlying data. The issue isn't having enough data -- it's ensuring the data is clean, integrated and enriched with the necessary business context.
"Data should be deduped and consolidated to avoid skewing analysis, and unified to provide a 'single source of truth,'" says Jones.
And this doesn't have to be a manual effort. For example, instead of users manually cleaning up and changing data using complex steps (like traditional Extract, Transform, Load), the Tableau Next Data Pro skill gives smart suggestions on how to do it and can even automatically handle some of the complicated changes, saving time and effort.
This data also needs to be captured in a semantic layer for agents and humans to be able to extract meaning and insights.
"Most businesses struggle with data because they're missing a semantic layer, which bridges the gap between raw data and business users, contextualizing complex data and making it more accessible, understandable and usable," notes Jones.
Tableau Semantics serves as the semantic layer, providing Tableau Next and Agentforce with a unified understanding of business data. By establishing consistent definitions and context, it enables AI agents to generate accurate and relevant responses. This capability is significantly enhanced through its integration with Salesforce Data Cloud, which provides a comprehensive data foundation by unifying and federating customer and business data across various sources and systems. This powerful combination allows organizations to connect siloed data repositories and leverage a single data environment that feeds directly into Tableau Semantics.
"By adding Tableau Semantics and connecting it with Data Cloud," Jones continues, "we're making data more approachable to people who may not typically consider themselves 'data people,' while ensuring they have access to the most complete, up-to-date information across the entire organization."
Rebuilding business leaders' trust in their data
Agentic analytics isn't just changing business intelligence -- it's making rich, useful data available to everyone. With unified data and a semantic layer, agents can bring insights to the surface that would otherwise remain buried in data wastelands.
"Organizations can't rely on manual tools anymore," Jones says. "This new agentic analytics approach is a huge shift. It can help analysts be even more productive, but it's also helping the non-data-savvy person actually interrogate their data, have a conversation without needing to write a single line of code or rely on precanned or complex dashboards. It's giving business leaders the deep insights they need to make more informed decisions, and changing how companies innovate."
