Dreamforce Wrap-Up – Enterprising Insights – EP38

Hello, everyone. I'm Keith Kirkpatrick, research director with the Futurum Group, and I'd like to welcome you to Enterprising Insights. It's our weekly podcast that explores the latest developments in the enterprise software market and the technologies that underpin these platforms, applications, and tools.

This week I'd like to talk about Dreamforce, which is Salesforce's annual user conference, covering all of the major announcements and key takeaways from the event. Then, of course, I'm gonna get into my rant or rave segment where I take one element in the market and I will either champion it or criticize it. So let's get right into it.

So Salesforce had their annual conference, it's called Dreamforce, and once again, they held it in San Francisco, California. Lots of people there. It was great to see them generate those large crowds.

I wanna say it was like 40,000 people, uh, perhaps a little more all coming together around the Moscone Center, uh, in, uh, downtown San Francisco. And really there were really a couple of, uh, major announcements, but really the main one that sort of overriding everything was Salesforce's full speed total commitment to what they're calling are, are their sales, or I'm sorry, their agent force, which is really their AI driven agents. Now, what is this?

What are these? What is this agent force? You know, it sounds like something like the, uh, air Force or something like that, but really what it is, it is the use of generative AI and other AI to create independent agents or technologies that are designed to conduct very, very specific tasks in order to ultimately help people do their jobs better, and then eventually, uh, complete tasks on their own.

So, uh, this agent force was a big deal because of the fact that if you think about what, uh, Salesforce has done over the past year with generative ai, they were talking in the past about this Einstein Co-pilot AI technology, which is, was really sort of designed around the idea of using generative AI to conduct, uh, to, to help people, uh, conduct tests in a very general way. Well, what Asian force is, it's using these, uh, this generative ai ai technology grounding all of that technology to very, very specific workflows, processes, and data help within Data Cloud to help organizations conduct work more efficiently, help smooth up friction and processes, and ultimately take action independently of its human. Now, I wanna be very clear that initially what we're going to be seeing is, again, a human in the loop, making sure that these things aren't kinda running amok.

But I think the ultimate goal, of course, is to get to a point where this technology can handle sort of low level repetitive tasks without human involvement or oversight, and with, and obviously the goal there is it allows humans to focus in on things that they do a much better job of, of handling. So things like connecting with customers, working through very, very complex situations and, and making decisions, higher level decisions that won't necessarily be solely based on data that you could put into, uh, data cloud. So, for example, you know, if you think of, uh, a typical customer service interaction where there's a product return, generally speaking, that's pretty straightforward.

Uh, customer has a problem, they tell the company what's wrong, and hopefully, uh, you know, the system has enough data, they could process that return, you know, basically, you know, marking down, okay, here's what was wrong with the item, or what, what happened with the transaction. And of course then a bunch of steps take place to process that re return in terms of making sure that the customer has the information and the, uh, materials to send that product back. And then, of course, all of the other backend things to make sure that all of that is accounted for within the company's, uh, commerce system or ERP.

Now, uh, when we're talking about, you know, doing things that are more complex, like, uh, perhaps having a return for a reason that is not covered under a, a, a traditional policy, uh, and again, uh, this, this example's not gonna be great because, you know, ultimately that the really, really challenging ones are the ones where it's some crazy situation that almost never comes up, otherwise, you would have a policy for. Uh, so in that case, a human would be much better able to handle those types of situations. So what we're talking about here are, uh, you know, processes that you have, generally speaking, tend to be fairly commonplace, you know, at least on the service side or on the sales side, you know, things like generating, uh, a, a quote or, you know, sending out a follow-up email, things like that, that are very, very well defined.

They're constrained by, uh, you know, parameters that obviously companies will have to put into place. Uh, but ultimately the goal is to take, you know, take, uh, essentially take AI and deploy it in a very, very constrained way to address a job that needs to be done. So now Salesforce announced this week several prebuilt agents that are actually available today.

So, um, for example, service agent, so, you know, this is what I was just speaking about, talking about handling, uh, service issues, um, that are designed to improve customer service efficiency. So, again, product returns, or if someone has a question about a product or if there's an issue with a service, routing them to the right, uh, department and then addressing their issue. Uh, so that would be service agent.

Uh, you know, sort of similar to that is something called sales development representative. This is an agent that will engage with prospects anytime of day, anytime during the week 24 7, what have you. Uh, they will answer questions, manage objections.

They can schedule meetings based on other inputs, uh, you know, based on, you know, data held within the CRM or even external data. And what this is really trying to do is, again, address sort of the busy work or the administrative tasks that are very rot, that are very much, uh, you know, straightforward, allowing human sellers to focus on what they do much better, which is building deeper customer relationships. Um, a again, the focus is all on efficiency and productivity.

Uh, a couple other ones that they announced merchandiser, this is, uh, these are agents that are designed to help e-commerce merchandisers do, again, sort of very, very basic tasks, setting up their site, uh, establishing goals, uh, personalizing promotions, uh, creating and editing product descriptions, uh, identifying, you know, different insights and data, all of that type of activity that, again, humans had done in the past. But essentially, AI can be used to do it much more quickly, more efficiently, and let's be honest, in some cases, more, more accurately, because they'll be only looking at data and generally speaking, as long as the models have been pointed at the right, uh, information, which is a technique that Salesforce has been talking about quite a bit called retrieval, augmented generation, making sure those models are only grounded in sort of vetted content or vetted information held within Data Cloud. As long as they have that, they generally will not screw up.

Uh, and then of course, um, uh, one of the other things that's really important to mention is that, you know, these sort of out of the box agents are great, but the real power is of course, when you start to customize them to work within a particular business. So in that case, uh, they have something Salesforce announced, something called Agent Force Studio. Now, this is a suite of low-code AI builders that allow organizations to customize existing agents or build no ones from scratch.

So, uh, agent Builder will use sort of existing tools like Flows, PropTech templates, uh, apex, and other APIs to help configure these agents using low, using low code. Uh, this is really, you know, what this does is it takes the need to, um, deploy a developer to handle this and lets business users actually configure their agents by defining topics, providing specific instructions using natural language, and then creating a library of actions, uh, that the agent can choose from when the agent is doing its work. Now, uh, they also announced another couple of, uh, builders, a model builder, which is a low-code builder and control, uh, plane for registering, testing, and activating a ai, AI models and other LLMs of their choice across Salesforce.

What does this mean? Well, you know, the large LLMs, they teach some things very well, but listen, but realistically, they don't do everything well. And in some case, it's like using a, uh, you know, a nuclear bomb to, you know, take out a, uh, you know, a clay pigeon.

It's just overkill. It's too expensive, too much firepower. So what Model Builder does, it allows, uh, organizations to select the right AI model for the particular use case, uh, and industry.

Now, prop Builder is another tool that was announced where it allows users to customize out of the box prop templates with their own CRM or data cloud data. And this will enhance the output of generated results. So again, the idea here is you're taking these prompts and you're using information, uh, or using, uh, you know, structures held within their CM or data cloud to make sure that, uh, the prompt really only uses the data that you want it to, as opposed to just sort of a generalized approach.

Now, what does this really mean? Well, uh, the goal here for Salesforce is to make AI very, very easy to use and deploy. Their big mission, you know, throughout Dreamforce is obviously to publicize that they have agents, but also to really highlight the power that, that Salesforce has.

Taking a platform based approach, saying, you as an organization don't need to go out and build your own, uh, generative AI use cases and, and models on your own, because really that's not where, uh, companies often don't realize the level of skill, time, money, and effort it takes to do that and do that well. And another point, of course, is that it's sort of like the old plumber analogy. If I wanted to replace all the pipes in my house, I could probably do it, but if something goes wrong, then it's on me to fix it.

And of course, I might spend a ton of money at a ton of time doing it, and if something goes wrong, I may not know how, and I might wind up calling in somebody else to help out. And by that point, it would've just been cheaper to call in an expert plumber at the beginning. And that's what Salesforce is saying, is that they are building all of these tools, and they have this platform that makes it easier and more efficient for an organization to try to deploy AI that way.

Now, let's, you know, if we're gonna take a step back, of course, that's what they want then, is their, their overall business strategy strategy, which is to get folks onto that Salesforce platform, uh, to use, because, you know, they are, obviously, they've been moving toward a more of a consumption based model when it comes to data Cloud. You know, the more amount of data that is actually imported or used by Data Cloud, they make more money. Uh, same thing with, uh, with their AI strategy.

They are moving, essentially, one of the things that they talked about this week, which is really interesting, is they're talking about, I think it was called Salesforce Foundations. And with that is it's a program where they will, as Mark Benioff, uh, said, they are going to make it. So if you're enterprise level or above, you know, in terms of that being your customer level, you'll get access to all of the different Salesforce.

Uh, you'll get access to Data Cloud and all relevant Salesforce clouds, uh, on a freemium basis, meaning you'll have a limited amount of functionality. But the idea is that you'll be able to use the technology deploy agents and see how all of that really works for your organization. You know, being able to access data in any of those, uh, different applications and obviously have a model around it into, uh, uh, data Cloud.

Now, there's, there's two things there. One is obviously it's a stickiness play there. They obviously want organizations to, uh, you know, to, to start using all of these different applications because once you start using them, it's that much harder to not use them.

Uh, the second thing, of course is, uh, if we think about what they're trying to do with pricing it, again, it's, you're, they're giving them, giving their customers a certain amount of users enough to get them hooked. Uh, it's kinda like giving a kid, you know, just enough candy to get 'em to come back, you know, over and over and over again. And there's nothing wrong with that approach.

I think it's actually, uh, uh, quite, um, quite good in that it, it does sort of, you know, feed into what Salesforce needs to do in terms of growing revenue. As AI becomes more commonplace, uh, it will be used more, more usage obviously equals more consumption of, uh, Salesforce and what they have to offer, where they might run into, I don't wanna see a problem, but perhaps a challenge in the market is of course, is looking at other vendors out there that are taking it a step further and saying, we're not going to just charge you, uh, based on consumption. Meaning, okay, you had x number of customer interactions, but we're actually going to say, we're gonna charge you based on outcomes.

Meaning, uh, we're not going to charge you if you can't resolve if a customer is unable to resolve a particular problem, issue, complaint, what have you, uh, solely using an agent. And the idea there is they are really saying, we trust our technology so much that, um, you know, that we are not gonna just charge you like consumption. We're gonna, I actually look at the outcomes.

Now, realistically, we're not gonna know how this is a goal gonna play out for quite some time until customers have had time to really get to know this, uh, get to know this approach, you know, deploy it in the wild, you know, without any sort of, uh, you know, pilot program or, or, uh, POC constraints there. I I do think it's also gonna be very much dependent upon the type of company, the industry in which they operate, um, and all of that kind stuff, all gonna play into it. 'cause for example, if we look at something like, uh, retail, retail, generally speaking, you're not going to have that many sort of very, very difficult, uh, back and forth, you know, transactions or, or, or interactions, which by the way, the way Salesforce is looking at it is, uh, an interaction would not be just, it's not like they're trying to rack up charges going, okay, uh, a customer contacted the agent and the agent wrote back and went back and forth 15 times.

No, to them, that's one interaction. They're not trying to nickel and dime customers. Uh, but the more that happens, that's more compute that Salesforce is going to wind up, uh, accumulating costs for, as opposed to, uh, a simpler interaction where it might be, you know, one or two back and forth.

And where this becomes really interesting is if we look at different industry segments and different use cases, something like healthcare, I could see them being that becoming the norm. Most people don't interact with their healthcare providers for something simple, uh, at this point. Um, you know, nobody wants to call, uh, unless there's a problem.

And usually that problem is a little bit more complex. Uh, it requires a lot of back and forth. Uh, similarly, if you look at companies like, uh, telecommunication services providers, uh, they typically have had pretty awful customer service scores because of the, the level of complexity, uh, in, in their offerings, particularly because they tend to not want to offer the same packages to every customer.

They wanna do it very personalized, but of course, that makes it a much more complex situation when customers want to change packages, add features, delete features, so on and so forth. So in that case, I think organizations are gonna have to take a really hard look at how they might deploy agents, and in which way, in terms of, you know, how they consume and how they price or how they pay for it makes the most sense. Uh, and that'll be on both sides from the vendor perspective like Salesforce, as well as the consumer, uh, the customer.

Um, so that's, I I think all of that's gonna play out. But I think that the big takeaway here is that, um, you know, Salesforce is clearly leaning very, very heavily into this agent approach. I think Mark Benioff made some, you know, pretty bold predictions there in terms of the number of agents that will be deployed by the next Dreamforce.

Uh, I don't have the number off the top of my head, but it was, it was a lot. And I think the challenge, again, is going to be demonstrating effectiveness of these agents, uh, both in terms of, you know, do do what they say they're gonna do. And, you know, uh, he challenged everyone to go down and check out all the demos, which were great.

And I sat in on a couple, and the demos looked very, uh, very good. But I would expect that, I can't see why they would, uh, you know, why do we put a bunch that that didn't work well out on an exhibit floor? Uh, I think the, the bigger issue is how are they going to work in a real world environment, uh, particularly when things aren't neatly tied up.

What I mean by that is most organizations do not just hold data within Data Cloud, they need to pull it in, uh, you know, from other sources. Now, that brings me to another sort of big point about, uh, Dreamforce. Another big thing they kept talking about was their more open ecosystem approach.

And what does that mean? Well, they are really kind of driving up the number of partners that they have through their Agent Force partner network. This is an open ecosystem that allows Agent Force agents to complete complex tasks, uh, by training together actions, you know, not only across Salesforce, but a whole network of other third party systems and agents.

Uh, what does this mean? It means if you have data held in, you know, AWS or Google Cloud or Workday, zoom, um, I think, uh, IBM and Box are the other partners that were announced as of this past week, it will be easy to get data from wherever it's held within those services, and then have Agent Force actually act upon that data. Why is that important?

Well, as I was just starting to say, uh, very few organizations operate in a heterogeneous stack environment, meaning they only use one platform, one vendor. Most have a number of different, uh, platforms, a number of different applications, whether due to the fact that, you know, they put things together piecemeal over time, which is the usual case, or, and this is an important one, they wanna make sure that there isn't, you know, they're not locked into a sole vendor for both in terms of contracts. You know, you don't wanna have all your one basket there.

But also from a risk perspective, as we've seen with, uh, some unfortunate, uh, cyber hacking, you know, uh, issues, you wouldn't wanna have all your data locked up. Most companies would not wanna have all of their data locked up in a single platform, uh, you know, just from a risk perspective. So I think Salesforce is doing the right thing here by taking by, by meeting customers where they are in terms terms of wherever they're holding their data and allowing them to deploy these agents across that work, across the enterprise, as well as these other different, um, uh, applications and, uh, data stores.

So, uh, let's see here. Lemme think if there's anything else that really kind of jumped out at me. Um, I think there was a, a couple of announcements there, um, that, that also kind of jumped out.

One was with Data Cloud, they're now supporting, uh, the ingestion of what they were calling unstructured data. And that turn is a little bit of a point of contention among some folks, meaning that, you know, some say, well, technically all data is structured. It's just, there are certain degrees of it.

But I think what Salesforce is referring to is things like, uh, video data held within video, uh, data held within chat, data held within audio files, things where the data itself is not, you know, uh, formatted in a neat sort of, you know, rolling column format. That's very easy to, uh, process to capture and process. So why is that important?

Well, a lot of, a lot of organizational information is held within those sort of unstructured data formats, and allowing Data Cloud to ingest that and process that information means that AI can work on it. And, uh, even more importantly, you know, as we start to see things like, you know, or, or applications like Slack become the place where workers go to not only, you know, collaborate on projects, just, you know, talking back and forth, but also pulling in other, you know, types of data, whether that, you know, it, the data is held within Data Cloud, or it's held within Tableau, which is obviously another, uh, uh, Salesforce application. When you have all that, you wanna make sure that you're able to access and utilize all types of organizational data.

Uh, as we see video and audio and, and some of these other formats become much more commonplace, uh, it's important that the AI and users are able to access that, uh, very, you know, easily. So I think that was another important announcement. And then I think, um, I think the other thing, there were other announcements here, uh, within their different clouds.

I think there were some announcements for, uh, uh, AI enhancements for field service, certainly some for, uh, marketing Cloud, you know, further, you know, enabling, uh, more streamlined management of campaigns and, uh, you know, use of marketing insights, all of that kind of stuff. Um, and, and I have a, a research note that is gonna be out in the next day or so if it's not out already, uh, that I, I wrote in conjunction with my colleague Paul Nati, where I go through all of the major announcements, uh, from Dreamforce. So I encourage you to take a look at that.

But I think the, the, the biggest takeaway here for me is that, um, ultimately what Salesforce is really doing is trying to set up a platform that makes it easy for workers to access information from anywhere, activate that in, you know, whether it's, uh, a human worker interacting with being data or an AI agent, or most likely a combination of the two, and being able to do that from whatever application they might be working in or preferred using. Uh, a lot of the talk was about using Slack as that sort of central, uh, place where you'd be able to pull in data visualizations from Tableau or pull, you know, data from data cloud or, or information from Commerce Cloud, all of that. That's all great.

I think it's, it really is reflective of the way that work will be done in the future. And particularly as we move from a very forms based, uh, approach and moving into one where there's gonna be a lot more sort of natural language based querying of information using generative ai. Basically, instead of, you know, pouring through a spreadsheet, you'd be able to just say, tell me what my sales were for the last week.

What's performing well, what's not, you know, you know, interacting with the data in a much more natural way. The only way that really works efficiently is if the AI has access to all of that information wherever it is held. And ultimately it all needs to be set up in a way that there is a single source of truth, meaning there can't be millions of copies of different, of the same data all over the place.

'cause you run into syncing issues and it just becomes impossible to manage. So I think that is a, uh, a, a great step in the right deck direction. You know, again, just to reiterate questions I have, obviously pricing is gonna be an issue as they move forward, uh, as always.

Uh, and this is something that's, that was raised with me in a customer meeting. Um, you know, there's, there's the question of, you know, is generative AI something that any business will only want to work with one vendor? I don't believe it is.

I think that a lot of companies are going to, yeah, yes, they may have AI that, uh, they may base ev most of their activities around a platform, whether it's Salesforce, ServiceNow or, uh, Oracle or, or you name the, the platform. You know, there's a certainly room there. Uh, but I do think there is going to be a bit of risk mitigation by doing some homegrown development, you know, using other vendors as well, because that is the way that you manage risk, uh, with ai.

Uh, and then of course, the, the trend that also kind of supports this view is most vendors are taking a very open approach and they're trying to drive partnerships with o other vendors to make sure that data can flow to elegantly in both directions. So, uh, again, I think that, uh, Salesforce, uh, had a very good dream force. It looked like it was very well attended.

Uh, from what I heard the entertainment was, was good. I didn't actually get a chance to check out pink at Oracle Park, but, uh, uh, you know, uh, I, I suppose that, uh, everything went well there. So, uh, with that, I'm gonna wrap this section up, and if anyone, uh, has any other feedback about Salesforce or Dreamforce, please do let me know in the comments.

Now, I'd like to move on to my rant or rave segment. Now, uh, this is where I take one item in the market and I will either criticize it or champion it. And this week I actually have a rant.

Um, one of the things I've seen over the past couple of weeks, uh, or actually more than a couple of weeks, it's really been this whole season, has been when I go to conferences and I hear vendors, uh, or leadership from, from various vendors, uh, take the old, um, you know, approach of talking, you know, not just talking about the benefits of their platform, but, but a Albert really criticizing the performance of their competitors. Now, I know that's a tactic as old as, you know, uh, the world itself, but in a lot of ways I think it does the market and their customers a massive disservice. It's, you know, there, there's two elements here.

One is, uh, a lot of the stuff, it's just blatantly commercial saying, I, I'm, you know, my AI can, can beat up your ai. Well, of course you're gonna say that there's nothing new there. You're not getting beyond what is really important, which is what is it that specifically that you do better than, uh, the other competition.

And really it gets lost in there word. The first, you know, sort of emphasis is on the other company, can't do this, they're having trouble doing this, they're having trouble doing this. That seems to be the focal point when really it should be.

Let's talk very specifically about what are the situations in which my, uh, solution will excel that I think has much more value, much more relevancy, uh, which really does resonate, uh, in the market. Now, these companies will say, yes, we, we do talk about that, you know, with case studies and so forth. But I think that when you CEO leads with a very, very bold state saying, only we can provide this type of stuff, this type of functionality with ai, it automatically sets up the, uh, you know, the conference attendee to, you know, have a bit of skepticism, uh, you know, at everything else that is said from there on.

Now, the second reason why this is not necessarily a good strategy is there's an old saying that you should never, you know, you should never really punch down at your rivals. You should only punch up. And there is a little bit of punching down at some of the conferences I saw, which really doesn't do you any favors because it almost puts you on that same level.

Now, the other challenge is, you know, even if you are punching up, sometimes that can also backfire, because if you really don't have that advantage, it makes you look not credible. Now, I understand with all of this, it's about messaging. It's about trying to position the company in the best light in terms of functionality, but really what's all gonna come down to is where are these customers or potential customers, you know, seeking to apply AI and then how the message should be, how can my solution apply very much, uh, or very directly to your situation, your somewhat unique industry or use case, whatever that might be.

And I think down the, uh, you know, as after you get past the high level of keynotes, we do get a little more, a little more of that. But I think the challenge is, is making that messaging stand out above the, my AI could kick your spot message, which I don't think does anyone any favors. And in many cases it's not necessarily provable anyway.

So, uh, just more noise. And yeah, I don't think it does anyone any favors, particularly buyers, and they're trying to make decisions in terms of their own strategy. So with that, that was my ran for the week, and of course that's all the time I have today.

So I want to thank you all for joining me here on Enterprising Insights. I'll be back again with another episode next week focused in on the happenings within the enterprise application market. So be sure to subscribe, rate, and review this podcast under preferred platform, and I'll see you next time.