Salesforce Bets Big on Enterprise AI to Drive Growth

Customer relationship management (CRM) giant Salesforce is seeing “massive” growth in its data cloud platform, fueled by interest in generative and agentic artificial intelligence (AI) from enterprises, according to one of the company’s senior executives.

In an exclusive interview with PYMNTS, Gabrielle Tao, senior vice president of product management at Salesforce, said the company is seeing robust demand for its data cloud services as enterprises realize they have to better organize their data to fully tap the power of AI.

“We make enterprise data ready for the agentic era,” Tao said, adding that the platform has seen strong growth in recent years. “We’re very excited to keep going and make agentic experiences world class.”

In the fiscal year ended Jan. 31, Salesforce Data Cloud booked $900 million in revenue, up 120% year over year. The company said the platform is used by nearly half of Fortune 100 companies. In Q4 alone, all of its top 10 deals included both AI and data cloud components.

Why all this interest in data alongside AI?

AI is fueled by data. Without data, AI is useless. AI is trained by data. It analyzes, makes predictions and learns new capabilities using data. It ingests text, images, videos, audio, code, sensor readings, math and other types of data — whether they reside in PDFs, emails, social media posts, spreadsheets, CRM systems, HR databases and many more media.

But in most companies, data is not organized well. It is in many places, batches are closed off from each other. Before data is ready for AI, it must be cleaned, unified, well-structured, governed and — ideally — real-time and searchable. Only then can AI be accurate, relevant and safe.

“Whenever companies say they have ‘unified’ data, a lot of times what that means is they’ve centralized the storage,” Tao said. The reality is that in many cases, “they’re not able to unlock the value of the data in real time for business applications and business agents.”

According to a 2024 Harvard Business Review article, senior managers often complain “they don’t have data they really need and don’t trust what they have. … The hype around AI exacerbates those concerns.”

Salesforce has been tackling this unglamorous but critical problem. Tao joined the company in 2019 to solve this problem by creating Data Cloud.

According to Gartner Peer Insights, the platform was given four out of five stars among 119 companies that have used it. However, in 2024, one 2-star review cited cost as a concern and a handful of 3-star reviews cited lack of good documentation, limited features and integration issues.

Salesforce’s Data Cloud competitors include Adobe Real-Time CDP, Oracle Unity Customer Data Platform, among others.

Read more: Salesforce to Launch AI Agents and Cloud-Based POS for Retailers

Down and Dirty With Data

To be sure, problems with organizational data have been around for decades. Many vendors offer to organize an enterprise’s data, such as Snowflake, Databricks and the cloud computing giants. They offer to unify data into data lakes or data warehouses.

Since the problem is not new, many companies are actually in some phase of data organization, Tao said. But unifying data remains a big lift for many.

“Probably 97% of the customers I talk to today, they have some form of data lake, data warehouse. Universally, that is pretty much true,” Tao said. “At the same time, the data is quite trapped in those places.”

That’s because they were built for analysts and data scientists, she said. For business users, “there is very limited ability to tap into the vastness of all that data.”

“How does that translate to something that makes sense for … the customer-facing agent performing the sales function?” Tao asked. She said Salesforce set out to make it accessible to business users.

Originally conceived as a customer data platform (CDP) in 2019 focused on marketing use cases, Salesforce Data Cloud has transformed into what Tao calls a “universal data activation layer” for all Salesforce applications, spanning sales, service, commerce, analytics and AI.

Unlike traditional data platforms that duplicate or copy data through complex extract-transform-load (ETL) processes, data cloud uses a “zero copy” architecture — something the company pioneered, according to Tao.

Zero copy pulls in metadata from disparate data sources without physically moving the data, allowing companies to harmonize, govern and activate their data in place. That means enterprises don’t need to unravel what they’ve built, Tao explained.

Tao said the result is a real-time, unified view of the customer that both human and AI agent workers can access without having to replicate or recode business logic across hundreds of systems. Since the AI uses the company’s own data, it mitigates hallucinations as well, Tao said.

Salesforce also offers a governance layer that lets companies do things like set access permissions for both human and AI agent workers. For example, companies can specify what data an entry-level sales representative can see and which objects a chatbot can access.

This ability will be key when using multilayers of AI agents, she said.