The News

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 meta data framework with the goal of enabling AI powered workflows that raise productivity.

The bigger picture

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. 

My analysis and point of view

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, means 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. Especially SAP is 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”.

However!

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”.

Now that the major waves of Dreamforce 2017 have settled, the announcements and a good part of the running commentary has been delivered, it is time for me to have a look at my pre-Dreamforce predictions.

Having been briefed before the event but unluckily not been able to attend (nor having had the time to write this piece earlier, I now have the advantage of having had more ‘thinking time” and can put the main announcements that we were briefed on into a bigger picture.

On the backdrop of an IDC study (sponsored by Salesforce) that postulates 3.3 million new jobs and an overall GDP impact of 859 billion dollar by 2022 in the “Salesforce economy”, the announcements basically revolve around one single topic: How to enable the employees (of Salesforce customers and partners) to deliver to this magnitude.

They were around

• Easier consumption of AI technology with Einstein, and improved IoT support,
• Opening up Trailhead to Salesforce customers in order to support company specific learning maps
• Enabling Lightning, as the platform to become fully themed, i.e. embrace the customers’ brands. Although technologically different I club the ability to create and easily upload branded mobile apps to the app stores into this
• Collaboration using Quip, the software that Salesforce acquired about a year ago, and a new partnership with Google

And in order to emphasize on the fact that they are serious about enabling people individually, Salesforce resuscitated the dot com prefix “my”. Thus myEinstein, myTrailhead, myLightning, mySalesforce, myIOT and mySalesforce got born.

A topic that might be slightly overlooked is covered by two sentences in the announcement of the Google partnership. “Also, Salesforce has named Google Cloud as a preferred public cloud provider to support the company’s rapidly growing global customer bases. Salesforce plans to use Google Cloud Platform for its core services as part of the company’s international infrastructure expansion.” With no further elaboration I can only interpret this as Salesforce following SAP’s multi cloud strategy.

What did I predict?

Following OOW17 and the SAP Hybris Summit in Barcelona I wrote that Salesforce is in a tight spot and that it is not nearly enough to just tick off the accomplishments that were delivered to promise. Looking at the current state trend of business systems I looked at Salesforce strengthening the eco system, adding power to the platform, and leveraging Einstein.

My point of view and analysis

While it seems that I was right on some level, there are some interesting rabbits that Salesforce pulled out of the hat.

Technology needs to serve the users and not the other way round and there are tasks that every user needs to get optimal support for. These are the tasks that need to be done often and the dull tasks that no one really needs to do. Plus, probably the tasks that no one comes around doing because of the previous two. Salesforce seems to have understood this.

Salesforce, in best Microsoft manner, enables this by creating point and click interfaces around complex technologies like (advanced) predictive analytics or IoT. The Salesforce platform also seems to benefit from Einstein permeating it more thoroughly. To me it appears like the days of separate clouds are numbered and that we are moving closer to a suite again – which makes sense as business processes extend across the boundaries of silo’ed functionalities.

I also think that Salesforce did a great move further embracing and empowering the ecosystem. There is a very clear message that Salesforce wants to stand for making it easy for their customers to focus on their customers’ needs. Whether this needed the “my” prefix or not…

By enabling developers, admins, and users, to easily create the applications that they need to more effectively and efficiently meet customer needs they are giving a clear message. On top of this there is an increasing amount of industry specific (Bolt) solutions. Extending the learning platform Trailhead to embrace customers, with branding and content, augments this. It offers Salesforce customers to build and monitor their own learning maps in a corporate environment.

Improved support for collaboration is another aspect of this. Having a platform like Chatter is one thing, leveraging an integrated platform like Quip is on an entirely different level. Quip live apps offer the promise of having all relevant data and documentation for a task in one place – kind of a Slack on steroids. Here also the new integration with the G Suite and Salesforce that will help making data available where it is needed will play its role.

With all these new and enhanced features Salesforce showcases that customer happiness is the end and that the employee is the means to make the customer happy.

The (long overdue) move to allow customers to make Salesforce applications fit into their own brand is only the icing on the cake of this message. While this capability sounds like a little thing it is actually a biggie as it fosters commitment and, so far, was a distinguishing factor in favor of Salesforce competition. It is good to see this now; and it appears to be simpler than in other tools.

All in all this is very good news for Salesforce customers and for their customers. I am looking forward to seeing more of this in action.

Salesforce seemingly starting a multi cloud strategy is an interesting development. While offering customers the in their eyes best cloud alternative (Salesforce, AWS, or Google) is a good thing, it will be interesting to see how these two competing ‘preferred’ clouds will be offered. Both parties have a genuine interest in attracting serious business workloads. It remains to be seen whether there will be a regional overlap or a distinction. It also remains to be seen how AWS reacts to this announcement.

Some words of caution

Salesforce is historically very good at showcasing complex scenarios. In sales processes against competition (especially SAP) Salesforce proves this times and again. Tools like the Einstein Prediction Builder are perfect examples for this. Being able to build a custom AI model on any field or object to predict business outcomes is a great thing. However, it is unclear how the underlying algorithm got trained or how the training got tested. Further, there are still quite some limitations. Some of them one can find out using the Trailhead Einstein Prediction Builder Trail.

As usual, and as with other companies, too, customers are well advised to look under the shiny hood that sales demos are.

Edit 12/28/2020: Changed Einstein link and removed IoT Link in first enumeration as old links were broken.