At its recent Connections event, Salesforce unveiled a series of announcements and offerings built upon its generative AI strategy. The company is calling this new set of tools its AI Cloud, although one might think of this as a sub-feature set across each of its primary applications clouds (e.g., Sales Cloud, Marketing Cloud, Tableau).
Salesforce, like many others in the sector, is betting big on AI, for obvious reasons. The company has been throwing around the “AI-first” tagline at recent events, to reinforce just how much of an investment it is making in the area. But taglines aside, the Connections event was a coming-out party of sorts for the full line of generative AI-powered offerings.
Before jumping into the AI Cloud, let’s revisit the idea of a “full journey go‑to‑market (GTM)” model. This model requires the orchestration of customer engagement across both pre-and-post sale customer journey milestones. Most GTM models tend to focus primarily on the pre-sale stages, thus leaving out critical engagement opportunities to drive lift in the lifetime customer value.
What’s Inside AI Cloud?
As Salesforce puts it, it is bringing AI to every part of Salesforce with this new addition to its Cloud portfolio. There are new AI-powered tools that align with nearly all major Salesforce offerings, including: Sales GPT, Service GPT, Marketing GPT, Commerce GPT, Slack GPT, Tableau GPT, Flow GPT, and Apex GPT. The company has also announced what it is calling the Einstein Trust Layer — a set of tools designed to drive enhanced privacy and security as its customers adopt generative AI use cases built from both public and private Large Language Models (LLMs).
The Einstein Trust Layer also makes AI Cloud open and extensible, meaning that businesses can choose the right model for the right task. For example, businesses can choose to use LLMs from Amazon Web Services (AWS), Anthropic, or Cohere — or they can use Salesforce LLMs developed by Salesforce AI Research. Businesses can also use their own domain-specific models that they have trained outside of Salesforce. This last point is important, as we see more and more regulated industries or industries with highly specific data sets opting to use their own LLMs in the future as adoption of GPT and other generative AI tools becomes more commonplace.
AI Cloud integrates several Salesforce technologies, including Einstein, Data Cloud, Tableau, Flow, and MuleSoft, to provide generative AI that requires less upfront configuration. This means that businesses can use AI Cloud to automate tasks, generate insights, and create personalized experiences for their customers across a number of customer-facing and internal processes and use cases. These include using generative AI to create content such as email subject lines (if not the marketing email content itself), build optimized landing pages and forms, turn digital events like webinars into blog posts, instantly edit videos into short snippets for use over social media channels, create custom lead target segments, translate marketing content across multiple languages with less human effort and costs, and so on (more on this below).
Some of the customers who are already using AI Cloud include AAA Auto Club Group, Gucci, Inspirato, and RBC US Wealth Management. These businesses are seeing the value of Salesforce’s new AI-powered capabilities, such as the ability to automate tasks, generate insights, and create personalized experiences for their customers.
During the Connections event, Salesforce outlined multiple use cases and benefits around how the AI Cloud will augment the value of the core go‑to‑market portfolio. Here are some of the benefits they outlined:
- Data privacy and security: The Einstein Trust Layer ensures that data is kept private and secure — as noted below, data security and privacy regarding the use of LLMs is still a major concern.
- Open and extensible: Businesses can choose the right model for the right task. This is important, as vendors that only allow limited models, or access to only a single proprietary (or public) LLM, can limit the effectiveness of customer initiatives.
- Enterprise ready: AI Cloud integrates with Salesforce technologies to provide a seamless experience.
- Automated tasks: AI Cloud can automate tasks, freeing up employees to focus on more strategic work.
- Generated insights: AI Cloud can generate insights from data, helping businesses make better decisions.
- Personalized experiences: AI Cloud can create personalized experiences for customers, increasing customer satisfaction.
Go‑to‑Market Specific Updates
Salesforce has been making noise around a generative AI-powered marketing Cloud since its Trailhead DX event early in 2023. But the AI Cloud announcement has codified its AI-powered GTM Technology Stack story even further.
Marketing GPT is the blanket name for the major AI additions, and the injection of AI spans the gamut of marketing use cases. Now, users can build and segment target lists more efficiently with AI making suggestions based on a number of factors like automated cohort analysis, recent win rates in Sales Cloud, and other attributes that are time consuming, if not nearly impossible, for humans to quickly put together. This is a strong addition to a GTM toolset, as it takes significant guesswork out of list building and bases it on real individual behaviors and other data points not typically available at a such a granular level. This also allows users to better identify potential opportunities within existing clients (i.e., the “full journey” concept) by analyzing data such as product use, and other trigger behaviors. Growth leaders now have better tools to more readily identify and engage with the right people inside the customer base to optimize lifetime value.
The speed of generative AI also allows marketers to get to 1:1 personalization with less effort — where the AI can identify and build dynamic experiences for targets and prospects. In that vein, Marketing GPT can more quickly assemble account based marketing lists to power ABM campaigns. The issue here is the accuracy of the data and how encompassing it is. Unless you have access to broader sets of data, “pointing the AI” towards only the data in Salesforce may not be super effective. Salesforce has added more data enrichment offerings inside its GTM portfolio of products, but these may incur additional fees.
Cross-channel analytics and optimization is also a cited use case. And, of course, the GPT tools can be used to more quickly develop content, optimize content like landing pages and email subject lines, etc. In short, it looks like there isn’t a facet of the Marketing Cloud (the cornerstone of its GTM technology Stack) that hasn’t been infused with intelligence capabilities. The company says it can help companies cut campaign operations costs by as much as 27%, and while we did not see many concrete examples of that kind of efficiency at the event — it can be easily surmised that a lot of time and effort can be reduced across common marketing processes with these new AI additions.
Obstacles Still Remain
Salesforce concedes that there is still a gap between mainstream acceptance and adoption of AI-powered technology in the enterprise. There persists a negative perception or uncertainty around security and privacy issues, as well as an adoption gap, even among those most eager to use generative AI in the workplace.
A recent Salesforce-sponsored survey of 4,000 employees found that although 61% of employees use or plan to use generative AI at work, nearly 60% of those who plan to use this technology don’t know how to do so using trusted data sources or while ensuring sensitive data is kept secure. In addition to security and adoption issues, more than half of the respondents also cited issues around bias and inaccuracy of the outputs from generative AI. While there are significant obstacles, it is not hard to see that generative AI is part of the future of business, and Salesforce has made some significant “stake in the ground” moves with its AI Cloud.
The Takeaway
Salesforce is doing the obviously right thing in embracing generative AI for its GTM Technology Stack. The beauty of generative AI and GPT tools is that they can further democratize the use of technology and bring insights to more individuals inside the enterprise. But what could be an even bigger boon for Salesforce here is the way that the simplistic user experience of tools like GPT can drive far greater adoption of core CRM, when users see the CRM as a tool that brings value and not just as something into which they have to constantly enter data. The company can finally flip the script on the adoption issues that have plagued CRM for decades. And for a company that generates revenue from individual seat licenses, getting more people to see value — and getting traditional CRM users to not just use, but see value — in the solutions is a huge factor to driving strong retention and expansion.
But even as Salesforce continues to be a CRM company on the surface, it is clearly becoming more and more of a “data value chain” company. Recent acquisitions, and the fact that it is continually improving how users can leverage large data sets without a lot of overage charges (as we covered in our last brief on the company) greatly improves users’ ability to use Salesforce as a platform for aggregating, augmenting, analyzing, and taking action on the customer data value chain. The new AI Cloud will make it even easier to drive insights from disparate Salesforce, and even non-Salesforce applications and data sets, and companies will pay a pretty penny for efficient — and accurate — predictive business insights.
In addition, the ability for companies to build full-journey engagement models with Salesforce as the backbone can potentially be improved via the AI Cloud. Between the Einstein Trust Layer and the introduction of a prompt-based UX that will enable faster, more in-depth process automations — users can now join up various applications and supporting tools across the Salesforce portfolio to create a seamless customer experience in ways that weren’t previously possible without incurring significant effort and costs. For example, a user can now pull data from the Data Cloud to power campaigns in Marketing Cloud, which then optimizes the converted leads prioritized in Sales Cloud, which then creates post-sale engagement triggers in Revenue Cloud — the possibilities are exciting for Salesforce users.
Ultimately, the success of the AI Cloud will all come down to Salesforce’s execution of their vision — maintaining a super simple user experience across the AI Cloud, and continuing to make this a clear value-add for Salesforce customers.
