Salesforce Inc. is expanding its artificial intelligence platform with new data management and governance capabilities, aiming to address what the company says is a crisis in enterprise AI adoption where more than 80% of projects fail to deliver meaningful business value.
The San Francisco-based software giant announced Thursday a suite of new tools designed to create what it calls a "trusted AI foundation" for enterprises struggling with fragmented data, weak governance, and security concerns that have hampered AI deployments across corporate America.
"We're seeing a lot of these AI projects really failing, and a lot of it's because customers still have fragmented data, they still have weak governance, they still have poor security," said Desiree Motamedi, Salesforce's senior vice president and chief marketing officer, in an exclusive interview with VentureBeat. "They really want a way that they can bring AI at scale that has the accuracy, the context and the control."
The timing of Salesforce's announcement comes as the company prepares for its annual Dreamforce conference next week, where CEO Marc Benioff is expected to showcase the company's vision for what he calls the "agentic enterprise" — workplaces where AI agents work alongside humans across every business function.
Why most corporate AI initiatives crash and burn before reaching production
The scale of AI project failures has become a significant concern for enterprise technology leaders. According to a RAND Corporation study, poor data quality, inadequate governance frameworks, and fragmented system integration are the primary culprits behind the high failure rate of corporate AI initiatives.
This challenge has created both pressure and opportunity for enterprise software providers. While companies face mounting pressure to deploy AI capabilities, many are discovering that their existing data infrastructure isn't equipped to support reliable AI applications at scale.
Salesforce's response centers on what Motamedi describes as three core capabilities: ensuring AI outputs are grounded in unified business data, embedding security and compliance controls into every workflow, and connecting AI agents across different platforms and data sources.
"The Salesforce platform is a $7 billion business," Motamedi noted, highlighting the significant revenue opportunity the company sees in addressing enterprise AI infrastructure needs. "This is a significant opportunity where we're seeing meaningful differentiation from other vendors in the market."
Inside Salesforce's new AI tools designed to fix enterprise data chaos
The company's latest announcements include several technically sophisticated solutions aimed at different aspects of the enterprise AI challenge:
Data Cloud Context Indexing represents Salesforce's approach to handling unstructured content like contracts, technical diagrams, and decision trees. The system uses what the company calls a "business-aware lens" to help AI agents interpret complex documents within their proper business context.
"A good example is a field engineer who uploads a schematic for guided troubleshooting," Motamedi explained. "Now they have that capability at their disposal, because it's right there in that view."
Data Cloud Clean Rooms, now generally available, allows organizations to securely share and analyze data with partners without exposing sensitive information. Using Salesforce's "zero copy" technology, companies can collaborate on data analysis without actually moving or duplicating datasets.
The clean room technology extends beyond traditional advertising applications to sectors like banking, where institutions could "detect fraud, and they want to be able to do it with some of their partners. They could now do it in hours versus weeks," according to Motamedi.
Tableau Semantics addresses one of the most persistent challenges in enterprise data management: ensuring consistent definitions of business metrics across different systems and teams. The AI-powered semantic layer translates raw data into standardized business language.
"We use terms like ACV or churn that have specific definitions within our organization," Motamedi said. "Making sure AI understands those definitions, and then having a standardized layer across organizations, really makes this seamless for enterprises."
MuleSoft Agent Fabric tackles what Salesforce calls "agent sprawl" — the proliferation of AI agents across different platforms and vendors within large organizations. The system provides centralized registration, orchestration, and governance for AI agents regardless of where they were built.
How Salesforce plans to battle Microsoft, Google and Amazon for AI dominance
Salesforce's comprehensive approach to AI infrastructure positions the company in direct competition with Microsoft, Google, Amazon, and ServiceNow, all of which are vying to become the dominant platform for enterprise AI deployment.
The company's strategy relies heavily on integration advantages that come from building AI capabilities into an existing platform used by thousands of enterprises. "The power of the platform" lies in the fact that "all of this is natively into the platform. So these capabilities are just there, and they work and they work seamlessly together," Motamedi emphasized.
This integrated approach contrasts with point solutions that require custom integration work. "Some of these point solutions, if you want these things to work together, you got to build those integrations. You got to have developer teams to make that happen," she noted.
The company's pending $8 billion acquisition of data management company Informatica, expected to close soon, will significantly expand Salesforce's capabilities in enterprise metadata management — a critical component for AI accuracy.
"For the last 26 years, Salesforce has been rooted in our platform approach — we've built the metadata layer from day one," Motamedi said. "But with Informatica, we're going to see metadata across the entire enterprise, and that gives us another layer of accuracy for AI responses."
Early enterprise customers reveal the reality of scaling AI in large organizations
Despite the technical capabilities, Salesforce acknowledges that enterprise AI adoption remains in early stages. The company reports having "over 12,000 live deployments of Agentforce" — its AI agent platform — but Motamedi describes a wide range of organizational readiness.
"Every company has a mandate right now to figure out how they can incorporate AI," she said. "We see very interesting ranges from people who are just getting started to people who are like, we're going to build like 80 different agents within their organization."
Early customer implementations include AAA Washington, which is using Salesforce's unified data foundation to improve member experiences across roadside assistance, insurance, and travel services. UChicago Medicine is leveraging the platform to ensure reliable patient interactions while enabling healthcare staff to focus on complex, human-centered care.
The maturity curve for enterprise AI adoption means "it's going to take a couple years to see it fully, fully embraced, but we already see the path," according to Motamedi.
What Salesforce's AI governance push means for the future of enterprise software
The broader implications of Salesforce's strategy extend beyond technical capabilities to fundamental questions about how enterprises will manage AI risk and governance. The company's emphasis on built-in security and compliance reflects growing corporate awareness that AI deployment without proper controls can create significant business liability.
Recent incidents involving AI agents accessing sensitive information or providing unreliable outputs have made corporate leaders more cautious about scaling AI initiatives. Salesforce's approach of embedding security directly into AI workflows — including automated threat detection partnerships with CrowdStrike and Okta, and built-in HIPAA compliance for healthcare applications — represents an attempt to address these concerns while accelerating adoption.
However, market skepticism remains. CNBC's Jim Cramer recently noted concerns about Salesforce's performance despite strong quarterly reports, suggesting that investor expectations for AI-driven growth may be outpacing actual business results.
The company's success will ultimately depend on whether it can help enterprises bridge the gap between AI experimentation and production-scale deployment. As Motamedi framed it: "We really believe that we have a trust layer for enterprise AI with all of these new announcements, and we're really helping companies move from cautious pilots to transformative action."
Whether that vision becomes reality will depend on Salesforce's ability to prove that integrated platforms can solve enterprise AI's trust problem better than the patchwork of point solutions most companies rely on today. In an industry where 80% of projects fail, the company that finally cracks the code on reliable, scalable enterprise AI could reshape how business gets done — or discover that the technical challenges run deeper than any single platform can solve.
