Agentic Analytics: Salesforce Envisions Business Intelligence for Everyone
Autonomous AI agents are like having an analytics expert in your pocket
Welcome to the Cloud Database Report. I’m John Foley, a long-time tech journalist, including 18 years at InformationWeek, who also worked in strategic comms at Oracle, IBM, and MongoDB. Connect with me on LinkedIn. This post is sponsored by Salesforce. The views and analysis are my own.
Turning data into insights has always been the objective of business intelligence. But that’s never as easy as it sounds.
The challenge is that BI programs and infrastructure can cost millions and may require a cadre of data engineers and scientists for good results. Even then, data-driven intelligence may not be at the fingertips of every employee who might want it, need it, or use it.
AI agents promise to change that. They have the potential to automate much of the upfront work—integrating, synthesizing, cataloging, and segmenting data—while also delivering data-driven intelligence to more people through apps and other points of engagement. Each week, I’m seeing vendors introduce new capabilities for agent-enabled analytics and BI.
With all of this top of mind, I reached out to Salesforce’s Chief Data Officer, Michael Andrew, who is knee-deep in these emerging technologies, including both the challenges and opportunities. Andrew, who is responsible for providing analytics to Salesforce’s 75,000+ employees, is well familiar with the roll-up-your-sleeves effort that goes into data transformation.
“It takes effort and work to make sure you have the data connected to be able to build the processes out, but we think that’s the promise of the platform—that you truly can get that holistic understanding of all of your customers,” says Andrew. “And then you can take the right action for that customer.”
The primary components of the Salesforce Platform that enable this kind of comprehensive view are: Agentforce, for building and managing AI agents; Tableau Next, the company’s all new agentic analytics solution; and Data Cloud, a data lakehouse where petabytes of data are integrated, aggregated, and activated.
Having covered business intelligence for many years—going back to Walmart’s enterprise data warehouse in the mid-1990’s—I started our conversation by asking Andrew how the market is changing. He pointed to three trends.
Merging of operational data and analytical data. “These worlds are converging,” Andrew says. This isn’t a new phenomenon but it’s catching on as more organizations seek to respond in near real-time to digital activity. Databricks’ recent acquisition of Neon and Snowflake’s pending buy of Crunchy Data allow for this blending of capabilities. Databricks and Snowflake are both accessible via Salesforce’s Data Cloud.
Farther-reaching distribution of analytics with AI agents. In many places, there has been more demand for analytics than there are analysts or data scientists to meet it. Now, with AI agents tapping into Data Cloud, every person can have a data analyst as a digital sidekick.
Rise of the agentic enterprise. More businesses are catching on to the possibilities of deploying autonomous AI agents that work side by side with humans in areas such as sales and support. “You can start thinking about that in every function,” says Andrew.
The future of analytics?
AI agents that function as data analysts, or with some of the skills of data analysts, are surfacing in various ways in the Salesforce ecosystem.
Tableau Next—an AI agent-enabled upgrade to the popular data-visualization tool that Salesforce acquired in 2019—now provides personalized agentic analytics. Tableau Next has been integrated with Data Cloud, and it works with Salesforce’s Customer 360 apps (Sales Cloud, Service Cloud, Marketing Cloud, etc.). It also ties into Slack, so users can request, create, and share metrics and dashboards in Slack channels, canvases, and direct messages.
Tableau Next is “a re-platforming” of Tableau, says Andrew. “We see this as the future of agentic analytics.”
What makes this possible is a semantic layer in Tableau Next that gives agents access to unified business data. With appropriate permissions, AI agents can access data in Data Cloud, then do what Tableau has always done—generate dashboards and data visualizations. Because this is within in Slack and other apps where collaborative work happens, it brings near-real-time analytics into workplace conversations.
Tableau Next agents are one way to surface data and BI in more apps and devices, but not the only way. Analytical capabilities are available in some of the prebuilt Agentforce agents, and these skills can also be added through customization or by partner add-ons in AgentExchange.
Democratizing BI
As analytics appear on the screens of more and more users, it gets us closer to the democratization of business intelligence, which has been a long time coming. “A few years from now every person in a company could have a data analyst or data scientist in their pocket, so to speak,” says Andrew. “That’s pretty incredible.”
As more employees get fast and easy access to actionable data, that leads to a more analytics-driven enterprise, or what Andrew metaphorically calls omniscience—the state of knowing everything there is to know about a customer relationship.
Andrew explains: “The agent has omniscience of everything that has been done with the customer—every meeting, all the content, all the Slack channels. Imagine a place that knows everything that has ever happened with a customer, all the business context, how the business interacts.”
That’s the vision. The reality has typically been more prescribed than that, with BI reports available to execs, managers, and the rest of the workforce on a need-to-know basis. Tools like Tableau, Power BI, Looker, Domo, and Zoho have moved the needle on ease-of-use with connectors and no-code transformation and dashboards. Autonomous AI agents that proactively populate everyday communications channels with timely intelligence promise to take that to the next level.
Notice I haven’t said that agents deliver insights. Websters defines insight as “the act of apprehending the inner nature of things or of seeing intuitively.” I think of that as a distinctly human function, although I may be forced to reassess as AI gets ever closer to human intelligence.
How we consume and act on BI is changing, too. For years, citizen data scientists have been the vanguard of do-it-yourself analytics. But they are relatively small in number compared to what has been unleashed by LLMs. Now, most everyone uses GenAI to probe for information. We’re all becoming prompt experts.
Get the foundation right
Agentic analytics may be a logical next step for organizations looking to infuse data-driven decision making into more parts of the business. As mentioned earlier, that requires getting data from transactional/operational systems to analytics systems, post haste.
Salesforce supports this through the combination of Data Cloud and Flow, its automation tool that lets customers define “signals” that trigger actions such as sending an email or notification. Flows can also initiate a user interaction or an approval process.
This is especially valuable for customer engagement and the sales cycle. It’s a way for managers of a hybrid workforce, comprised of humans and AI agents, to divvy up tasks appropriately. For example, a hot lead might go straight to a sales professional, whereas a general inquiry gets handled by an AI agent. “Now the agent’s having a conversation with the customer because maybe they were early in the buying process,” explains Andrew.
Data as a product
But wait, let’s tap the brakes on this idea of making more data available to more people. What about data security and access control?
These projects need governance and guardrails. Salesforce is addressing this in its own analytics program by implementing a data mesh approach, where data is managed as a product. That entails knowing the sources and lineage of data, where and how it’s stored, data-quality steps, and attention to access privileges for both humans and AI agents.
“You really have got to get the foundations right,” says Andrew, “because agents are going to expose any data problems.”
The good news is that, based on what I’m seeing in the industry, a growing number of business and IT leaders recognize the need to address the issues of data readiness that stand in the way of enterprise AI. There’s still a lot of work to be done, but awareness is the first step.
Anyone who believes that business intelligence provides competitive advantage—and that would include the vast majority of business leaders—might be predisposed to see the potential of having in an analyst in every pocket, a.k.a. agentic analytics.
“Analytically-driven organizations outperform their peers. They make faster and smarter decisions,” says Andrew. “Agents turbocharge that—the same assets can be super-accelerated and everybody can have analytics at their fingertips.”
That’s the opportunity, but we’re at the beginning of the adoption curve. I will be watching for organizations that move ahead at the speed that AI agents are advancing.
