Docusign's CEO on the dangers of trusting AI to read, and write, your contracts

23:23When I joined and I very quickly realized we needed to completely reimagine our product roadmap, I really wanted to put product at the center of the company and say, we're going to articulate a new product roadmap and things are going to flow from there. That's not to say that we're not taking lots of customer input. After all, we've got 20 years of experience working in agreements and millions of customers. But we want the product vision and the overall architecting of that to be the guiding light for then how we go to market, how we spend, et cetera. Second area that I focused on was historically, marketing here worked in the service of sales.

24:05It was basically about creating qualified leads for sales. But I'm like, well, customers, this is an electronic contracting product. They should be able to just come in and do whatever they want to do directly, self-serve to the greatest extent possible. And, you know, I came from Google where every advertiser, including people who spend a billion dollar plus a year, places their own orders in the system. But at DocuSign, we couldn't do that. So that was another area. And really taking all the pieces of the company that worked on identifying, sourcing, and bringing new customers on and combining them in one team.

24:42And then the third area that I'm really pushing on is we've always felt like we needed to own the ball. And when you have an iconic, simple product, then you maybe don't need partners as much. But now, with this broader vision for agreement management, we really need partners to help us go to market and install and service the product. And so we're very focused on working with system integrators like the Deloitte's and Accenture's of the world, as well as distributors and resellers. Those are the big changes we've made. I have to say 7 ,000 people is a shocking number. The last number I heard was actually 4 ,000, which was shocking then.

25:17How are 7 ,000 people organized at DocuSign? Is it sales and marketing? Is it product? How does that work? Sales and marketing is the biggest area. We have about 1 ,000 account executives that cover everything from S &B accounts all the way up to enterprises. We're not a small company. We do over$3 billion in revenue. Our revenue per employee is pretty much in the median of the SaaS businesses. So sales and marketing is the biggest function. And it's everything from pre-sales to account executives to customer success people that help with implementation and support, of course. Product and engineering, we have about 1 ,000 people in engineering, 1200, engineering proper.

26:03And then there are product managers and designers and so on. So, you know, I think if you look at the number of countries and the complexity of the regulatory and compliance environments we're in, you know, that's a significant effort. And then now with IAM, I've pivoted pretty much all of the open investment dollars to the company in the last three years have been pivoted into this. So, we've gotten much leaner on the sales and marketing side, and we've got invested more heavily in product and engineering. IAM is the AI product. That's right. When you think about shifting that investment focus and then something like self-serve, you were at Google.

26:38You were at Google for, I think, 12 years, something like that. You've been the CEO of DocuSign since 2022. So you've been there a minute. Three years. How did you think about changing DocuSign? How was it organized before? And then when you got there, how did you think, how did you work through, okay, here's, I'm going to reorganize it for the opportunities I want to attack? I mean, I think the low-hanging fruit was helping the company pivot from just being a good machine for acquiring new customers, but not necessarily an amazing retention machine. So focused on customer retention and customer success and adoption.

27:13I think we sort of took that a little for granted. That was one area. Second, getting us to be much more efficient, as I alluded to, whether it was through building out digital channels or just streamlining our sales and marketing efforts. So that was a big immediate push and sort of low-hanging fruit things that were in my control early on. But the biggest change is really the product vision effort and pivoting the company to being product innovation-led and restarting the innovation engine of DocuSign. COVID was a seminal event in the company's history, not in a good way. I think everybody thinks that DocuSign is a COVID darling and that must have been so great for us because everybody had to use us.

27:54And look, there was some of that temporarily. But if you look at the long run, it didn't change the secular adoption trend of signing things electronically. But everybody literally fell asleep. If you're a sales rep and your revenue growth rate without much effort goes from 25 % to 60%, you're in the order-taking business. And on the engineering side, we didn't need to develop that much. We just needed to keep the servers running and scaling that. And so I think restarting that, hey, we can build something great, something new, and reclaim that innovation mojo. That was, I think, the most important transformation in the DocSense.

28:37It's still ongoing, but the pace of product innovation and product release is completely different now. And we actually had to throttle the number of releases we were doing because there was so much. It was sort of starting to overwhelm our customers and our sales team. We have so much stuff. Yeah. And there are so many opportunities. And that's an exciting place to be. Was that your pitch when you got the job? Very few people are going to ever interview to be the CEO of a multibillion-dollar publicly traded company. What was the pitch? I'm going to restart your product innovation? It was a dual pitch.

29:12I said, look, what I know I can do is I can come in and make things much more efficient. And of course, I used my digital marketing background from Google to make some suggestions on things that I thought we could do. Did you make just like a killer deck? You know, I didn't get time to make a killer deck. I mean, literally, I got a call from Headhunter on a Thursday night that the board wanted to meet with me for an hour and hear my thoughts on the future of DocuSign Saturday morning at nine. And I was working full time at that time. I had a big job at Google. And so I couldn't just take a day off.

29:48I suppose I could have, but I didn't. And so I basically had Thursday evening and Friday evening to prepare, you know, a short slide deck. And the reality in these kinds of situations, nobody expects, you know, the super polished multimedia thing. What they really are focused on is what are the thoughts, right? And you can use 10 point for your thought. That's fine. And so I was really focused on, look, I know I can make the company more efficient. I know there's gold on the retention side. And I believe that the agreement problem is fundamentally unsolved. But that DocuSign is the best position to capture that opportunity.

30:28I mean, we were a big DocuSign user at Google. Still is one of our largest customers. Huge e-sign and contract lifecycle management was our advanced contract management project that enterprises use. And yet I knew we were in the earliest possible phase of transforming how agreements get done and that there was so much opportunity. And so I just articulated my confidence in that. I knew enough that I couldn't, that it would be, I would lose credibility if I was too specific. So that took six to nine months to really say, okay, that's fine at a 50 ,000-foot level. How do you get to the real vision of what's the singular thing we're going to do?

31:11I mean, just as an example of that, it's easy to talk about all the steps in the journey. And I think people naturally gravitate towards the drafting and negotiating side of agreements because that's almost like what's in movies. Of course, that's what lawyers do. But we actually started at the end. We said, look, the foundational piece of reimagining agreements is to have an intelligent repository, to have a place where all your agreements are stored, and to be able to apply AI to that so we can tell you what's in all your agreements, start comparing them, and then ultimately close the loop to what happens under those agreements.

31:44So that's what we built. Is that what you mean when you say the problem is fundamentally unsolved? Well, that's part of it. But we've done, the way I would describe it is we've digitized the asset, right? We've taken what used to be an offline asset. We turned it into an electronic document. And we move it around electronically, mostly via email. And then hopefully we execute them electronically. And other than that, absolutely nothing has changed for the last 50 years in agreements. All other aspects of the workflow, they're just as inefficient, just as brittle, just as unpredictable. There's just as much time lost waiting for somebody in the process.

32:23You don't even know who they are or what steps they're on. Once you've signed, the agreements go to a deep, dark place. It might as well be a physical filing cabinet in a basement. In fact, it's probably harder to find now than it used to be when it was in the filing cabinet. Now it's in some SharePoint drive or email inbox or who knows. And so bringing all that together and presenting that information in a way that is intelligent, bite-sized, appropriate for the persona is incredibly valuable. I mean, every CFO wants to know what are the top 10 contracts where we have leveraged things we've negotiated for that we're not getting.

33:04Every head of sales wants to know what are their top 10 renewals and what are the three things my rep should be renegotiating. Every procurement, you can just go down the list. It is an obvious pain point that no one has solved. And so that's our opportunity. That was my pitch. And I think it was a good pitch. And I think the story is much better now than it was three years ago. I want to get into that. I want to talk about how you were implementing that, obviously, as part of it. I want to talk about whether it's working, which is interesting. But I want to ask the other decoder question first.

33:36Obviously, you're at Google for a long time. Google's decision-making, lots to say about decision-making at Google. What's your framework for making decisions? What did you take from that experience, and what do you do at DocuSign? Look, I loved my time at Google. It's an amazing company. I'm proud of the time I spent there, and I've always had a special place in my heart. One thing I do not miss is the seven-dimensional matrix of trying to make decisions. So I really try to avoid that here and have much greater clarity about who owns the decision, who needs to be consulted, and all those kinds of decision frameworks.

34:14But I'd say one thing that I took away from Google as a very positive lesson is my experience, and this is not limited to Google, but that the most effective leaders in tech can go super deep when they need to. and they need to be willing to do that. You know, management is not floating up in the abstract 50 ,000 foot above the problem and just admiring it and thinking you can just, you know, operate at that level. I think when hard decisions need to be made, one-way doors, things that are complicated, you need to be able and willing to really dive into the details. And I've always prided myself on that.

34:56And I think that's particularly important at a transformation moment because it needs more push from the top to get people to change, right? People naturally want to do things super incrementally. It's not unique to any one company. That's just how people are. And so if you're trying to get more radical change, you need to push. And in order to push and not break everything, you need to get deep in the details. So when we were deciding on the early architecture and what the key launch pieces were going to be for intelligent re-management, I was in daily whiteboarding sessions to help decide what that was going to be.

35:36Once we had set that direction, then you step back and you just let the team run. Then you maybe get to a decision point about, okay, well, how are we going to talk about it? How are we going to explain to people this newly re-imagined docusign? That was a big moment of getting super deep into deciding how we were going to message this to the market, how we're going to relaunch the company effectively. And then you've got to decide, well, how do we take all this capability and find the singular thing that we need to tell customers about to get them to try it and buy it? Because all software has 10x the functionality that people use.

36:13And finding that singular proposition is super important. So those are examples of things that I got much more deeply involved in. And I think that is essential to being effective. But then you've got to let go. So let's talk about the technology here. It's IAM. It's intelligent agreement management. That's the new system. That's where the AI focus is going. You've talked about it already a little bit, right? You have this library of documents you've signed. You can extract intelligence from them. I've got to say, I looked at the website just before I started talking, and how are we marketing this thing?

36:45And a lot of it is DocuSign will tell you what's in the agreement you're signing. And all of my lawyer red flags just went off. You should not let the robot interpret the document for you. And one of the reasons is, well, is DocuSign now responsible for the interpretation of this agreement? If I sign this and the AI hallucinated an interpretation of this clause and then I'm mad, do I get to sue DocuSign? I think the reason you're seeing that on the website is we literally just launched this for consumers last week. And so it's the most recent release. It's not generally – wouldn't generally be the top thing.

37:26But right now it's newsworthy and things that are consumer-facing tend to get more attention, as you know. Here you are in Decoder. With all that said, I mean, look, we've had agreement summarization for a long time. We provide it internally to companies that prepare documents, that send documents. We made a long time to launch it for consumers exactly because of some of the concerns that you raised. We wanted to get to a high level of accuracy. We wanted to make sure that we could position this as something assistive, but that we're not replacing a lawyer and you still need to get legal advice.

38:03But the people valued that sort of high level summary. And all of our very robust testing suggests that people are more comfortable and greater confidence and that they, at the same time, understand that when something's sensitive, they need to get to a lawyer. So, look, it's a delicate thing. We want to be the place that people trust the most for receiving and executing agreements. And so, not providing an AI service isn't really an option. And at the same time, you've got to have a ton of guardrails to avoid putting yourself a position with people who claim that they relied on you or that you are acting as a lawyer.

38:55Yeah, I'm just curious about the dynamic there. I was talking to a colleague of mine just before we started, and I said, they've got this new product. I'm going to ask him about it. And what she said to me was, oh, that makes sense. I already take the agreements and paste them in a chat CPT. Exactly. It's happening. But in that case, right, you don't get to go in front of the judge or file the complaint or even send the threatening letter being like, this is what ChatGBT told me the document I was signing says. Well, I think opening eyes probably. They're probably worried about that. They might be worried about it.

39:27The consumer's going to do it. But I think it was like that fact pattern is like, well, that's your fault, right? I signed that. I pushed the button. I indicated consent after having my identity verified in the database next to the summary is a different fact pattern. So what are the guardrails that you thought of? I mean, because it's going to happen, right? Someone's going to blame you for a mistake that they made or that the system made. What are the guardrails that made you comfortable with that or that made you think we're not liable at all? There's two answers to that. I mean, there's sort of what got it comfortable legally.

39:59I think we got very comfortable that all the language of the scammers and how it was done graphic and so it was fine. But I don't think that that's the big question. To me, it's more of a moral question, right? Are you doing the right thing for the customers? Feel good about that. And we got to a place where there was no question in our mind that this was better for consumers and that to continue to uphold trust in our platform as a place to come and execute documents, that we were the best position to provide this kind of additional advice and context. In fact, it would be dereliction duty not to provide it and that we needed the best possible job we could to make sure that people understood that this was context.

40:42But if this is something that's really sensitive for them, they should get a lawyer. Of course, they don't today. So I think we're improving the situation both for consumers but also, by the way, for companies. and so that was our motivation and it's impossible to eliminate people are going to claim things I think we're doing the right thing and we felt like we were taking not just the necessary precaution from a legal perspective but necessarily from a company values and morals perspective and this felt right so that's how we got to it We need to break here for a quick minute we'll be right back

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43:58Welcome back. I'm talking with DocuSign CEO Alan Teegason about the risks of AI in legal work. The reason for me asking about liability and whether the system is going to be wrong is that LLMs as a core foundational piece of technology still hallucinate. Right. This is the flaw with LLMs. You know, if you talk to any of the big AI CEOs, if you talk to Sunar Barchai, he will say it's also their feature. Right. The reason that they are creative is because they are effectively hallucinating. And if we unbox that in. That's a creative repositioning. But, yeah. You said it, not me. This is the dance, right?

44:34You want an LLM that can do a bunch of creative writing. You need hallucinations. You want an LLM that's going to do legal analysis on a library of documents that maybe is 20 years old. You really don't want it to hallucinate. How are you constraining the LLM to make sure it does what you want it to do? I mean, so if we pivot to the folks that use our document to use our tools to prepare documents, there are a lot of guardrails that we put in. I mean, we have a whole risk scoring thing. We, of course, benchmark you versus your existing templates or playbooks, as they're often called inside of companies, and highlight things that deviate from that.

45:14And this is true both for agreements that the company prepares themselves and for third-party agreements that they receive from others, because, of course, most companies are takers of terms from other companies, right? And so there's a lot of workflow there. We also don't fully automate flows. we put deliberately humans in the loop at key decision points and I think that will persist for a very long time companies are very averse to the risk of automation that's going rogue I was we talked about this over Google for a little while there was a lot of efforts of automating the commerce process and this is for buying toothpaste And both consumers and retailers were very resistant.

46:05And even with all the new stuff, I think it's getting better, but I still think there will be a lot of hesitancy, and those are much lower stakes than agreements. But put that into practice for me, just with the products you're talking about today. I am a company. I've got 20 years of agreements in DocuSign. I want to know, how have my terms changed over time? And I asked DocuSign to generate that intelligence for me. What's the guarantee that it's not just hallucinating, that it doesn't go through the first five years in the rag process and then it just decides to get bored and makes up some contracts that never existed?

46:38Look, I think the stakes and the opportunity for hallucination in an extraction scenario are far lower than in a drafting scenario. But it's going to draft a report, right? You're going to lay your hand. Yes, it'll show. It'll highlight things. Well, I mean, you've got to link to the sources. You've got to have all of that to sort of show your work and the logic of the decisioning. But look, there's no amount of automated decisioning that's ever going to be 100 % perfect. I think we're getting to an extremely high level of accuracy. I mean, we have the largest agreement repository in the world.

47:17You know, when we got started, this is a very interesting story. So when we got started with IAM, we have petabytes of agreements because of our assigned product. But we did not license those agreements specifically for processing and AI. I don't think people would have consented anyway. And so we said, we're not going to use any of that data. We're going to require customers to, on a one-on-one basis, consent to allow us to process individual agreements. and we're going to put in product functionality so they can opt out at any time. So that meant that when we started, we only had access to public agreements, which is what the big LLMs have as well.

47:56And we had a very good level of accuracy in our extractions as rated by human eval. And then we turned the loose on actual agreements inside these companies. And the accuracy fell 15 percentage points. Wow. Out of 100. Yeah. That turns it from super useful, not perfect, but super useful, first drafts, first table type of thing to, wow, I feel like I have to redo everything this thing does. And we've now worked our way back mostly to our original starting point, and we think we can go much further. And that's because we now have 150 million private consented agreements in our AI, and we're adding tens of millions every month, which is orders of magnitude bigger than anybody else.

48:39And these are all the complex private agreements that aren't generally publicly available. So I think we have a hidden huge AI advantage in accuracy. That's not just, you know, no one's ever going to get to 100. So it doesn't take away from your point that there could be cases where, you know, you need to have human eval and oversight. But I'm just saying we're going to be in a much better place to get to a place where you get the real productivity benefits because we can be much more accurate. You're not training your own models, right? It seems you're obviously using foundation models from other companies.

49:12Yeah, so we started with OpenAI and GPT, where we use Azure as our first public cloud provider. And since then, we've added other frontier models. And so we score them against each other and use the best of the best. And it's, I mean, it's an incredible innovation pace. And so, and capital investment that's going in. And the cost per token, right, is the cost per unit, if you will, has just been dropping precipitously. And so for a company like us, for whom that's a cost of goods, that's amazing. Yeah. I can now go to customers up to a pretty large size and say, let's give us one of the grants.

49:50It's included in your price. Tell me about the dynamics of the AI pricing per token. Because the idea that the prices are falling requires some amount of competition in the market, right? It requires the ability for you to switch. Are you building your system so you can switch from model to model? Are the models different or differentiated enough? We already have. And look, early on, you have to say, GPT was head and shoulders ahead of other frontier models. And over time, so we started there, plus our internal models. And we had five years of experience in building and using AI models internally.

50:29Over time, it just became clear the Frontier LLMs were great, OpenAI in particular. And for a little while, they were kind of the only game in town. But now, there are a lot of choices. We've got excellent results with Gemini and other models. And so, we have a good range of choices. But even if you're with one vendor, the cost of processing an individual document has plummeted. When we got started that summer of 23, I was very worried that we'd have to have this very complicated pricing structure tied to the number of agreements that you were uploading because the cost to process them and reprocess them all the time for all these things people wanted to do felt like it was going to be prohibitive.

51:15And now, up to a fairly large company, I just included in your subscription. And it's a totally different game. I mean, it's like a factor of 100. So do you think the models are interchangeable? I mean, you're talking about them all getting good and they're getting terrific. I mean, I don't think they're perfectly interchangeable. But for our purposes, for the applications, I mean, obviously the models have different personalities in the consumer context. And for certain specific workflows like coding, there's some models that I think have some specific advantages. But for document extraction, we're able to get very good results with multiple models and multiple of the top frontier models.

51:55and they are advancing at roughly similar rates. This leads me to basically the bubble question, right? If you're saying we're delivering a bunch of value, but the cost of the models is so low that I'm just bundling into my existing subscription costs, unless the customer is so big that they're using massive amounts of tokens, how do you expect the model companies to make any money, right? Like that's a race to the bottom where if you're not charging additional margin, you're not going to pass any on to them. How do you expect that to reconcile? I mean, look, I am obviously paying, writing a large check to Google.

52:31So it's not that it's insignificant. It's just that the incremental is value of an additional. But you're getting more usage for the same dollar over time, right? I'm getting more usage for the same dollar. And look, they've gotten so much more efficient. The models have gotten better. The hardware has gotten better. The amount of cappings that's gone in is now being replicated is incredible. And so I think some of that is sort of already in the water. But to your point, look, I think they will all, you know, add value in different ways. But I do think that the LLM models, it's a highly competitive space.

53:09And you've got to add value above or below that. And you can see that in the strategies. You know, OpenAI is now really doubling down on both their consumer and enterprise efforts. Google, of course, has investments in the hardware side, on the cloud stack. They use their AI for YouTube and ads and the other vendors. And Tropic is being incredibly successful with their coding product. And so you're seeing different strategies playing out for leveraging a strong position in the foundation model space. Yeah, I'm curious about that because you have the perspective, right? You run a big enterprise software product.

53:48You know, ChatGPT announced DocuGPT and the idea that they're going to come and attack your moat, which is the database of identities and the flow. Like that seems not a big threat. But I look at that big sweep that you just described and most of those companies, they think their money is in enterprise use, right? That's the first big set of customers. It's big businesses with budgets that need to get more efficient and increase productivity. And maybe that's happening and maybe it's not, right? I think that's still an open question. It's starting to happen, yeah. Yeah, something's happening. And then you have the big consumer products.

54:25And so you see Google and Meta saying, well, we already have ad technology. Meta is going to move all of its stuff to GPUs, and we're going to serve Reels ads that way. And it doesn't matter how many GPUs we buy because it's already being monetized. Google is the same way. Then you have OpenAI, which announced an advertising product last week, right? We're going to do ads in ChatGPT. Do you see a path forward? I guess the question in relation to DocuSign is, do you think that industry is stable enough for you to bet on in the long term as it works through all these machinations? Because this is the core technology of your growth.

54:59And then second, you have experience at Google. You have experience running an advertising business. How do you think that's going to go? There were a lot of questions in there. Let's start with the – I think from a DocuSign perspective, so far it's all been goodness, right? It's dramatically expanded the value that we can deliver to customers, the scope of our services, and in a way where we could leverage the R &D of others, but preserve a really meaningful competitive advantage through our data, through our workflow, through our trust. And I think it's been a huge win. I am not, I mean, as you alluded to, I don't believe that the big LLM providers are going to provide, you know, agreement management solutions.

55:39I think they're going to look for others to build applications on top of their systems like we have. I suppose it's possible that there could be some of that capability that seeps into standard products, as we saw with, you know, you can do it in a summary, et cetera. I feel like it's been amazing in opening up a much broader market opportunity for DocuSign, and we are running as fast as we can to capitalize on that. And I think the cost dynamics so far have been super favorable to us. And I frankly, I expect that to continue. In terms of the other part of your question on the ad side, I think it was always inevitable that OpenAI was going to get into the ads business.

56:24I think you can't really do a scaled consumer services play without advertising these days. And so there was just a matter of time before they got there. It'll be delicate to incorporate ads into an assistive AI experience. But, look, it was delicate for Google to do that with paid search, but they did. And I think that turned out to be a huge value unlock. So I think that prize is very significant. And I actually think they're late in launching it. They should have done it earlier. I think the transformation of having all that context of the full journey you've been on and the ability to fully close a transaction potentially takes that kind of intentful activity to the next level.

57:18You can literally almost go through the entire what used to be called the funnel, right, in one platform. That's incredibly valuable. And so it's a big prize. And that's why I think OpenAI is investing, and that's why Google is fighting hard and keep its position, and why Meta, I think, is a well-positioned, and why there are so many Meta people at OpenAI. So I think it was always inevitable. We'll see how it shakes out. I don't have a moral issue with it. I think it needs to be done well, right? I think it's – I think you can say a lot of things about Google, but I think they did a pretty good job of keeping those walls pretty separate.

57:57And I saw that when I was there. And you've got to maintain the integrity of the results people are getting while incorporating advertising experiences that are accretive to the users. That's a difficult thing to do. I was just at CS and you couldn't turn the corner without a marketing influencer jumping out of a bush and saying the funnel is dead because everyone has experienced everything randomly nowadays. That's a different podcast. That's a different podcast. Let me try to draw a connection between these two ideas. And there's a reason I asked them together. If you are looking at all the big model providers and they are saying our first opportunity is in enterprise and the enterprise customers are going to unlock a bunch of value and build products around them.

58:39Are they actually saying that? By and large, when I talk to these folks on the show – It doesn't seem like what OpenAI and Meta and Google are doing, but I mean – Well, I mean, Meta is, I think, a unique one. Maybe the exception that proves the rule because they have no enterprise business to speak of, so they have nothing else to say. Google – I agree there's a huge focus on enterprise, and we're very focused on enterprise. Of course. So you see that, and then I'm talking to you. You're building a scaled enterprise product on the back of these models, and you're saying they're almost interchangeable, and the rates are dropping because I can go get pricing terms because I can just switch.

59:12That's one dynamic of pricing that's happening in the industry. Then next to it, you have, well, what's the biggest prize in the history of the internet? It's search advertising. OpenAI is going to attack that. And we're going to take some of that share away from Google. And Google is certainly going to defend its territory. And the meta is going to do whatever meta is going to do. That's where you would decommodify your models, right? You would say, chat to be so much better. It's such a better consumer experience that you have no choice because all the users are here and that might be a zero-sum game.

59:39Those things are pulling in wildly opposite directions in my view, right? You have commodity models for enterprise, which where all the budgets are, and then you have deeply specialized consumer experiences where you can layer in advertising at high rates. Can you bring those together? That's why I asked if the foundation seems stable because eventually one of those things is going to be more lucrative than the other. and you might run out of enterprise model providers. And that industry might collapse down to one or two that charges high rates. I think both are so large that the biggest players are going to go after both with very different teams.

1:00:15So, yeah, obviously very familiar with Google, right? And Google Cloud is a standalone operating unit inside of Google and operates quite separately and necessarily so, right? In fact, I don't think Google Cloud could have become successful if it wasn't pulled away from the traditional consumer services and ad business of Google. And I think OpenAI is a smaller company, but again, I need to replicate that very different MO and go-to-market that comes in the ads business versus the consumer side of the house versus the enterprise side. That's a lot to take on for a young company. It's an incredibly capable team and they have a huge amount of IP.

1:00:53But that is a very ambitious undertaking to do both. And Tropic seems to have decided to focus exclusively on the enterprise and are really executing incredibly well. We're using their Cloud Code product, and it's fantastic. And so huge kudos to them. And then there'll be a host of other players that, you know, I think become either more enterprise or more consumer-focused. But I think Google, Meta, OpenAI are probably the three that are trying to do both. Are you structuring your contracts with these vendors to be long-term, short-term? How do you think about that? The contracts in that space tend to be relatively long-term.

1:01:35We and they want three-plus-year commitment-type deals. you can't I mean as much as people want to say oh I can just swap one for the other that's not really how things work if you're doing enterprise deployments so there's a significant investment according to a platform and the vendor of course wants to see the benefit of your growth and the upfront investment they're making and helping you come onto their platform and so they're multi, they're three plus year deals sometimes longer When do you think you'll have a feature set that's so robust using these tools that you can actually charge a premium to all of your customers?

1:02:14Well, we kind of do that today. I mean, we went from being a sign provider. We're charging a substantial premium for moving to this AI-assisted suite. Because the value, and I can provide that value instantly, right? You sign with DocuSign, and I can turn on, and I can give you AI insights into your agreements day one. In fact, we deploy our new products as fast as we deploy signals. We're deploying under 20 days. Which is kind of unheard of. If you follow enterprise software, that's kind of unheard of, right? And frankly, a lot of that is just human stuff because the product's ready day one. Our customer agreements right now average around 19 months in length, but they're longer with big customers and tend to be shorter with smaller customers.

1:02:57That's a typical pattern. Yeah. I expect that our agreements will get longer over time. But we already charged a substantial premium for getting access to the AI features, and customers have been very willing to pay. we have over 25 ,000 customers live on this new AI platform in under 18 months. I'm always curious about that number. I hear these usage numbers from all the companies that deploy AI tools. And underneath it is sort of the, well, it just showed up one day, and we're counting that person as an AI user. AI overviews are there. Everyone loves them. It's like, I don't know about that.

1:03:26They just started in my face when I want them or not. Fair enough. Are you actually measuring happy customers using the tools because they want to or because the button is there and everyone just clicks it? Both. I mean, look, we give you a license for however many users you want. And we obviously track what's consumption of that license, what are people doing, how often do they access the repository, how many searches they do and extractions they do, how many document sendings and document executions do they do. And we keep a very close eye on that as for the fundamental health metrics. So I think product adoption leads to renewal and retention, right?

1:04:14And so we are very focused on that. And no, not everyone uses the AI features, but we're seeing really robust usage. And we're constantly looking for ways for more of the users to discover what's possible. I mean, this is one of the endemic problems in a press software. You build this stuff, but how do people actually figure out what's possible? And the good news is we have something that has a near universal value prop and can be deployed easily. There are all kinds of magic moments where, oh, did you know you could do this? We're making that available. So, you know, as you send a document or as you sign a document, hey, would you like an agreement summary?

1:04:58Would you like to know how this agreement pairs? Hey, did you know that we have all of them and you can see all this stuff? And that's a new world. It's exciting. I do love that the future of all software is some combination of, like, ultra smart Clippy and tooltips. There's something there. Someone should have a book on it. Hopefully we're providing more value than that. You're an enterprise software CEO. You've come. You've faced the gauntlet. So I'm going to ask you the hardest question of all. You use your own tools. You started off by saying you use your own tools. What is your biggest frustration with DocuSign and how would you fix it as a product, not as a company?

1:05:33I don't think our mobile experience is good enough. I would expect that everything should automatically flow. We should predict the next steps. And, you know, it all works. Obviously, millions of people sign with DocuSign daily. and often on their phones. But I think there's still some room for improvement there. And so I'm pushing the team on that. Well done. You actually answered the question. Most enterprise software CEOs don't. So I commend you for that. Alan, this has been great. Thank you so much for being on Decoder. Thank you, Neil. I really appreciate you having me. I'd like to thank Alan for taking the time to join Decoder today.

1:06:11And thank you for listening. I hope you enjoyed it. If you'd like to let us know what you thought about this episode or really anything else, drop us a line. You can email us at decoderatheverge.com. We really do read all the emails. You can also hit me up directly on Threads or Blue Sky. We're also on YouTube. You can watch full episodes at DecoderPod. And we have a TikTok and an Instagram. They're at DecoderPod as well. They're a lot of fun. If you like Decoder, please share it with your friends and subscribe wherever you get your podcasts. If you really like the show, hit us with a five-star review.

1:06:35Decoder is a production of The Verge and part of the Boxing Media Podcast Network. The show is produced by Kate Cox and Nick Stat. It's edited by Ursa Wright. Our editorial director is Kevin McShane. The Decoder music is by Breakmaster Cylinder. We'll see you next time.