Dreamforce 2023 has started. It is the first post-Covid physical Dreamforce. The event has more than 40,000 participants from all over the world, which is almost small, considering past events.
As usual, Dreamforce was opened by a keynote that was accompanied by a flurry of announcements. In an interesting twist, the keynote was accompanied by an analyst watch party held by Salesforce’s analyst relations. There was a “competing” watch party by the CRM Playaz .
Not surprisingly, the topic of AI was front, right and center of the keynote after some emphasis on a culture of giving and the celebration of Salesforce as the #3 software vendor worldwide with an expected revenue of $34.8B US while continuing to lead the CRM market by a considerable margin. This is, indeed, quite an achievement.
However, the focus of the keynote was set in the watch party by Salesforce’s Chief Enterprise Strategist Bruce Richardson with the following posting in the chat: “ gen AI to boost global economy by $4.4 trillion. Since the release of ChatGPT in November 2022, it’s been all over the headlines, and businesses are racing to capture its value. Within the technology’s first few months, McKinsey research found that generative AI (gen AI) features stand to add up to $4.4 trillion to the global economy-annually. Source: McKinsey (August 23, 2023) “
Consequently, much of what Salesforce currently does, capitalizes on this opportunity and helps businesses work with this opportunity. The key vehicles for this are the new Einstein 1 Platform and Einstein Copilot that comes together with a low code development environment Einstein Copilot Studio.
Einstein Copilot is an “ out of the box conversational AI assistant built into the user experience of every Salesforce application “. The Einstein Copilot Studio is “ an easy way for companies to build an entirely new generation of AI-powered apps with custom prompts, skills, and AI models “.
Both are residing on top of Salesforce’s Data Cloud and are going into pilot state now.
The Einstein 1 Platform essentially fuses Salesforce’s Data Cloud and Einstein, based on a single metadata framework with the goal of enabling AI powered workflows that raise productivity.
Everybody, and their dog, are talking generative AI these days (count me in). AI, as in automation, efficiency, and productivity, is an important, if not the most important topic these days. Many a company has made announcements mainly basing on and leveraging the huge buzz that OpenAI has managed to create. Few have delivered outside from some fairly simple to build scenarios, and most of them have concentrated on the same use cases. Nearly none of them speaks actively about, or embraces, the notions of privacy, security, and trust. Yet, most of the systems need extensive access to corporate, or even sensitive personal, data to lead to satisfactory results. I am yet to see a vendor that offers a dedicated AI security architecture, apart from Salesforce. Some may have one, but they fail to talk about it.
The second important topic these days is the increasing importance of conversational, i.e., conversational user interfaces. As I already wrote back in 2017: Voice an text will replace point and click .
From a topic point of view, Salesforce clearly gets it. This is clearly shown by the statement “ entirely new generation of AI-powered apps with custom prompts, skills, and AI models “. The interaction with applications will shift more and more from a computer centric point of click metaphor to a human centric communication metaphor. Sure, visual user interfaces will continue to have their place — think dashboards or charts — but they will overall blend into a human centric interaction model. To be effective, this needs lots of machine learning that exposes knowledge and functionality via AI models. This will be the case for user interactions as well as interactions with external parties like customers, suppliers, partners, you name it.
The foundation for this is data. Clean data. Acknowledging that data will never be perfectly clean, basing it on an enterprise-wide harmonized meta data infrastructure is the way, also to address the topic of data islands. In my eyes, the underlying “database” of business applications will more and more become a data lake, meaning a lake house architecture for business applications. The challenge for Salesforce will be that the company is not the strong business software vendor that creates a metadata infrastructure. Instead, it needs to compete with the likes of Microsoft and SAP. SAP is especially interesting here as — although seemingly smaller by annual revenue. SAP commands the as per yet most important system that a company has: the ERP. Around which system will companies build their metadata infrastructure, their ERP or their CRM? Related to this question: What will be the core platform? I guess that this is a question that is yet to be answered.
Benioff’s keynote was refreshing from an AI point of view. It was probably the first one this year that did not only rave about the fascinating prospects that generative AI “changes everything”. Yes, he said that, too, even added to it — it changes “everything and anything”.
Salesforce also addresses the number one concern not only of customers but also of executives. Trust. During the keynote, Benioff and Salesforce did not only address the obvious business opportunity but also covered the very valid and real concern that was also an outcome of the recent State of the Connected Customer report.
External AI systems are a major concern for companies that use them. They do not know how the input data, including prompts is used, stored, and proliferated. They also do not know how accurate the responses of especially LLMs are.
In his keynote he said “ LLMs are very convincing liars “. I think that is a very apt description.
Again, Salesforce clearly gets it.
Still, there is ways to go. There are many questions that are to be answered when it comes to the effective, efficient, and ethical usage of AI, as I have also written in my recent column article If AI can’t be trusted, efficacy and efficiency won’t matter on CustomerThink. Some major open topics are explainability and the question how, and using which data, the own models are trained. Salesforce delivers pretrained industry specific models yet states in its tenets of trusted, ethical and humane AI that “your data is not our product”. Pretrained models and this tenet are seemingly at odds with each other. This should be possible to be resolved easily. The topic of explainability is a hard one, but needs to be covered, too, as the sixth tenet rightfully says, “ transparency builds trust “.