Salesforce, Inc. (CRM) Earnings Call Transcript & Summary

Welcome to the State of Data and Analytics. This webinar is going to give you some insights from more than 10,000 leaders across the world and across different business roles: analytics, IT and line of business leaders. We're so excited to have you here. I'm Lisa Ginther Huh. I'm on our Tableau product marketing team at Salesforce. And it's my pleasure to work with Wendy. Wendy, please introduce yourself.

Hi. My name is Wendy Batchelder, and I'm the Chief Data Officer at Salesforce. Thank you so much for joining us today.

We would love to let you know, we, of course, are a public-facing company. This is our legal document to represent that fact, and we want to call that out for you. Of course, we are very grateful. And thank you so much for joining us today, taking time out of your busy, busy schedules to hear what we want to share with you in terms of these research insights. Let's get started.

First of all, we want to let you know that, again, we spoke with leaders across the world, 18 countries, analytics and IT leaders, line of business leaders, across different departments, various decision-making level from director plus, across multiple industries and diverse company sizes, mid-market, enterprise. We really covered the gamut to ensure that we were able to discern and determine a lot of perspectives from different individuals and to really suss out the types of connections and disconnects across decision-makers. Wendy, is there anything you'd look to add to this before we get into the narrative summary?

I'm just really impressed with the size and scope of the population that we were able to interview as a part of this process, because it really does give us a wide range of perspectives. And I think most research studies, at least that I've read, either focus on one persona or another. And this gives us such a well-rounded view. I found that really exciting and interesting as we were digging into this.

Perfect. The end goal is to really be able to pull together that cohesive story. And I think what we show here is what we've learned across all of these decision-makers and how they come together and partner across their business is that as we look at advances in AI, everyone, of course, very top of mind generative AI, it really hinges on, is rooted in, data and data trust. But we find that this is a bit elusive across different organizations, and it's a bit of a winding path to get to the road of success. So any thoughts on this? We're going to go ahead and kick off a poll. So before we get started, I would love to have the audience fill out their perspective on the first question to get us started. And Wendy, maybe you would love to give a bit of your take on what stands out to you as you went through the report and really took away this top 3 chapters, if you will?

Yes, you know what's really interesting, Lisa, is I have a lot of customer-facing conversations and have the opportunity to meet with a lot of other chief data officers and chief information officers. And it's quite fascinating because the conversations, you might expect, are varied across industries and folks might be fighting different challenges, but the themes are rather consistent. It's data strategy is very much the cornerstone of AI. We all care a lot about trust, and we're all trying to figure out how to do this effectively. It doesn't seem that one industry has cracked this code any better than another. We're all kind of struggling with the same problems.

I think that makes a lot of sense, and we are really interested to see what the audience has to say. So those are really interesting results. Now as we look at -- this was intentional to pick these very same question in terms of where your perspective is in the audience. 92% of analytics and IT leaders agree that the need for trustworthy data is higher than ever. So you can see that we are looking at 43% agree and 49% strongly agree. So with that, Wendy, would love to hear your perspective. I know that trusted data is so important and also the perspective of business leaders. Your take on this.

Yes. I think it's really easy for business leaders to look at data and say, "This just doesn't make sense," or, "I don't really believe the answer that I'm getting based on my professional experience." And that's sort of like the gut check. And what I have seen is that data is sort of presumed guilty until proven innocent. And the trust that we need in our data is to be able to show, in places where perhaps our gut doesn't match the data, why we're confident in the data. And that requires us to have trustworthy information, which also requires transparency. How do we know that the data is valid? How do we know that we can trust the source of it? Are we sure the quality is right? So we -- as data professionals, we have to be prepared to answer questions like that and do it in a very transparent way. And that percentage that we see here in these results around we should be able to trust our data, we need that now more than ever, that's not going down. It's, in fact, increasing. So that really spikes the demand for us to be able to have trustworthy data.

Yes. I agree on that. And I think it is really interesting there is near unanimity, and yet this entire point is the power of having a strong data foundation. I've heard you actually say recently that data is really so important and AI is like the Trojan Horse for data management. Tell a little bit more about that as you look at some of these numbers about the impact of AI and really how business and analytics leaders are thinking about AI. I would love to hear a little more about this Trojan Horse theory.

Well, it's a fun analogy. So we'll just -- like let's take that for what it's worth. But I do think that this moment that we're having around generative AI, especially not to mention, I'll call it, traditional AI that we've all been using for many years, that those conversations are now happening at a much higher level of the executive team and now in the boardroom, which wasn't necessarily the case even a year ago. And so this emergence of AI and the desire to use it also requires us to then determine: but can we trust the output? And you've heard probably a lot of buzzwords or things about like, oh, the AI hallucinated, or it gave us a response that was wild or totally out of left field. And that's why this additional focus on data management and really understanding how we are confident and comfortable with the data that we're feeding some of these generative AIs or LLMs, these big models, we need to be really comfortable and really confident that the data that's going in is trustworthy so that we can better truck the output from AI. And this elevation of the conversation to the C-suite and into the boardroom just adds additional pressure to that conversation. So now more than ever, I'm seeing executive teams and boards really look towards chief data officers or chief data analytics officers and saying, "Are we sure we can trust the output of this AI?" Which then in turn sparks the conversation around, well, how are we managing data? And so this result that we see here around the AI power being hinged on data is not surprising, but it is elevating the conversation to new heights.

Yes, I think that's fair, Wendy, especially when you think about the benefits with regard to ever faster, increasingly accelerated business decision making that's on point, able to drive operational efficiencies, and yet it's still the same old your data in is as good as data out. So it's just really interesting. But it does kind of give this is the moment, the time for data, if you will, data management, high priority. There's also, I would say, the -- really, the point of data maturity and the preparedness, the analogue of really moving up and to the right on the hockey puck of maturity to drive the ability to take greater advantage and prepare for AI. Talk a little bit about that. It's, I can see, 2x basically.

Yes. I guess I was a little surprised that it wasn't 3x or 4x as far as the likelihood of companies being able to support. So it's really interesting because there are industry differences between data maturity. A lot of that comes down to regulation and industries that were pushed to be more data, I think, managed, like financial services and health care. I think they're more prepared to take on this AI revolution, but there's also a little bit of fear around controlling and protecting information in those sectors. So it's sort of interesting. But the data maturity we're really talking about is, is data connected? Can we get access to it easily? Are we able to see the lineage and where it's coming from? Can we measure the quality of that data easily? Do we know who owns it, who is responsible for caring for it? And when you have really good processes in place to manage all of those different dimensions, then it is a lot easier to apply AI to it, because you know where it is, you know who's responsible for it, you know if it's fit for purpose. And you can more easily access it to take advantage of its power. So like I said, I'm not surprised that it's not -- that it's 2x. I was only surprised that it wasn't more significant.

That's fair. That's fair. Now let's take a look as well, I know a subject that is near and dear to your heart -- in fact, we've got Paula Goldman, our Chief Ethical and Humane Use Officer at Salesforce, talking about the ethics, the guidelines, the guardrails. We certainly have looked at this. The data foundation and trust is so critical. Speak a little bit about this, if you will. I know this is near and dear to your heart.

Yes. I think it's really interesting to see ethics becoming a fast follow to the energy around AI. I think we saw that lag a bit more when it came to other technological innovations, but because of the concern around the broad use, the mass amounts of data that are being used to train and tune some of these large models, that there was a little bit of nervousness around, I think, just the general population or some folks that are maybe not as familiar with AI practices or really don't have the same, I would say, understanding of data, and then some that are very educated and very well-versed in this. There are also concerns, and this wide range of concern around how are we using this, how are we managing it, what is going to be used for and just, I think, some level of fear. And so you've seen this quick follow of conversation on ethical use of AI. Are we using it for good? What's the risk associated with this on a broad sense, in a very narrow sense? And it is something that Salesforce is taking very seriously. Our #1 value is trust. We've been very clear that your data is not our product. And I think that that position, companies really being clear and transparent about what they're using AI for and really expecting that out of the gate, is helping to address some of these concerns that we're seeing just surge as the adoption of generative AI is continuing to rapidly increase. And so I'm personally very pleased to see this come forward as fast as it has, because it helps us to build technology in a way that is safe for everyone.