Salesforce recently held its annual Dreamforce event, where it unveiled a host of products tied to the new Einstein 1 Platform. This platform aims to enhance productivity and customer engagement by blending both internal and external corporate data with generative AI technologies. It does this by improving analytics, automation and discovery—and especially by offering predictive suggestions for marketing campaigns, sales opportunities, customer service interactions and more. If it pays off, it could give a big boost to Salesforce’s market-leading position in CRM.
“In a way, this AI revolution is actually a data revolution,” Salesforce cofounder and CTO Parker Harris said during his part of the Dreamforce keynote, “because the AI revolution wouldn’t exist without the power of all that data.” The presentations at Dreamforce suggest that Salesforce understands just how much companies rely on high-quality data to power their AI dreams—a topic I’ll explore more in this piece.
You need enterprise data you can trust—and the tools to make the most of it
My own journey with Salesforce has spanned more than two decades. During that time, I’ve dedicated substantial effort to analyzing data to guide business decisions. As a marketing and sales leader, I learned that having access to accurate data was only a starting point, because I also needed to cultivate trust in the data throughout my organization. Salesforce clearly understands the need for trust too, especially given the sensitivity people and companies (rightly) have about their data being used by AI. That’s clear from the level of thought the company has put into the Einstein Trust Layer, which safeguards enterprise data as it interacts with external large language models, or LLMs. (More on that below.)
Many of you reading this have your own perspectives on Salesforce based on your experiences with it. Generally speaking, reactions to Einstein 1 probably fall into three groups: the enthusiasts, the skeptics and those reserving judgment until they have a chance to try the new features firsthand. While many industry professionals praise Salesforce for its robust capabilities and adaptability, there is a contingent who find navigating it somewhat overwhelming. This isn’t necessarily a criticism of the software itself but rather an acknowledgment of its breadth. With a platform as sprawling as Salesforce, a learning curve is inevitable.
Beyond that, many users have had negative experiences with Salesforce (and other CRMs) because of bad data in the system. For instance, I’ve been struck over the years by the number of sales pros I’ve known who didn’t seem to realize all the ways that bad data or misconfiguration could hinder an organization from fully leveraging Salesforce’s features. It’s worth keeping in mind the old adage: garbage in, garbage out. Reliable outcomes hinge on the correctness and integrity of the data fed into the system.
The good news is, the introduction of Einstein 1 at Dreamforce suggests that Salesforce fully acknowledges this reality, heightened for the age of AI. As this new platform is implemented for Salesforce’s customer base, I’ll be evaluating its ability to connect to different data sources and ensure data integrity, then share my insights with you.
Details of the Einstein 1 Platform
During his part of the Dreamforce keynote, Harris devoted considerable attention to a foundational element of the Einstein 1 Platform: the Salesforce Data Cloud. The Data Cloud is a comprehensive data platform tailored to help businesses consolidate and align customer data. This gives users access to accurate and up-to-date information from any part of the Einstein 1 Platform, including the Sales Cloud, Service Cloud, Marketing Cloud and other areas. As shown in the diagram above, the Data Cloud can also pull in real-time data from outside applications from partners such as Google and Microsoft, or from countless other sources connected to Salesforce via the AppExchange cloud marketplace or MuleSoft. All of this is in service to giving businesses a complete, transparent and actionable perspective on each of their customers.
Much of the Dreamforce keynote focused on Einstein Copilot, a generative AI-enhanced conversational tool integrated across all Salesforce applications. This is where the rubber meets the road for AI within the Einstein 1 platform. Users can ask questions or give instructions to the AI in a text window that works like a chatbot; the system will draw upon information in the Data Cloud and (selectively) external LLMs to produce answers or carry out tasks. Marketers might use this for discerning audience clusters, building campaigns or creating personalized email messages. Sales teams can identify leads, create sales pitches and automatically book meetings. Customer service reps can quickly find the best resolutions for client issues and generate tailored responses. In these and many other cases, Einstein Copilot removes friction and boosts productivity for the task at hand—much like similar features do within Google’s and Microsoft’s productivity apps.
At the same time, Salesforce has introduced Einstein Copilot Studio, which allows users to customize Einstein Copilot for specific purposes. Throughout the Einstein 1 Platform, there are multiple low-code or no-code options for tailoring workflows, along with powerful capabilities enabling more technical users to apply their programming abilities for targeted tasks.
Einstein 1 also continues Salesforce’s emphasis on data analytics, which was reinforced most emphatically by the company’s purchase of Tableau a few years ago. Like other parts of the platform, Tableau has been enhanced with generative AI to make it both more powerful and easier to use for data visualization, reporting and business intelligence (BI) functions. At Dreamforce, one Salesforce leader went as far as saying that the new functionality could turn any user into a data analyst.
Safeguarding all of the AI functionality is the Einstein Trust Layer, which protects customers’ enterprise data from ever being exposed to external LLMs. Besides being able to use their own AI resources, customers can turn to commercially available AI models from companies like OpenAI or Anthropic, or from Salesforce itself. The Trust Layer enables user queries in Einstein Copilot to interact with these models—while keeping sensitive internal information encrypted. The Trust Layer also vets AI-generated answers for bias, toxicity and hallucinations, plus it maintains an audit trail to enforce strong governance.
Connectivity
Acquired by Salesforce in 2018, MuleSoft serves as a bridge between applications and data sources, ensuring connectivity for business processes and system integrations. The MuleSoft Anypoint platform connects applications, data and devices, irrespective of their origin or location. This enables integration with a variety of other applications, including ERP, marketing and e-commerce systems as well as cloud services from AWS and other providers. It can also integrate with on-premises systems, including legacy databases, and a broad spectrum of devices, including IoT and mobile devices.
Salesforce leaders did give some love to MuleSoft during the Dreamforce keynote, but overall I wonder how much they care about connecting Einstein 1 with the majority of enterprise data that still lives in on-prem systems. It’s important to access data from functions such as supply chain, HR and finance that have amassed invaluable historical data over the years—yet these great pools of data often go overlooked.
One thing that puzzles me is Salesforce’s reluctance to forge more partnerships with top-tier ERP solutions. As useful as MuleSoft and AppExchange are, routinely pushing or pulling data through integration tools or connectors is suboptimal. By contrast, establishing direct partnerships would strengthen data integrity, reduce the proliferation of data sources and generally improve data infrastructure. It’s especially easy to make this case for harmonized ERP and CRM solutions, which would considerably enhance the customer maintenance cycle. Consider that Salesforce could manage marketing, sales and customer support processes, while an ERP system from SAP or Oracle could handle all the back-office steps of sales orders, financial ledgers, product management, supply chain, manufacturing and so on. Besides the ERP solutions just mentioned, Salesforce might consider partnering with Workday, NetSuite, Infor, Microsoft, IFS, Acumatica and Epicor.
Summary
As we wrap up, I want to emphasize another point about Salesforce’s partnerships, as well as the foundational data infrastructure and productivity tools the company offers its clients. Salesforce has entered strategic alliances with companies such as AWS, Microsoft, Google, Snowflake and Databricks to bolster data management for businesses. However, there’s a noticeable void in its partnership portfolio. Cloudera, a prominent player in enterprise data, is conspicuously absent. The reasons for this omission might range from direct market rivalry, to pricing issues, to potential integration challenges. Yet given the significance of data integration for Salesforce and the vast amounts of data Cloudera manages, addressing this partnership gap could become important in due course.
The introduction of Einstein 1 has generated significant interest. I am eager to see how effectively Einstein 1 enables businesses to harness their data for well-informed decisions. Because I want organizations to genuinely tap into the power of their data, I am curious whether Einstein 1 will meet the expectations set for it at Dreamforce. Judging by how CEO Marc Benioff and other leaders at Salesforce have presented it, Einstein 1 should enable businesses to not only utilize their data more efficiently but also make the most out of AI. Only time will tell if Einstein 1 will redefine the enterprise data landscape as profoundly as Dr. Einstein redefined the world of physics.
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