Joel Fishbein: Great. Thank you so much.
Operator: Thank you. Our next question is from Brent Bracelin with Piper Sandler. Please proceed with your question.
Hannah Rudoff: Hi, all. This is Hannah Rudoff on for Brent today. Thanks for taking my questions. Just the first one for me is what we saw testing and plans to implement generative AI like in our recent CIO survey. Are you getting any sense that enterprises are putting a hold on new deals or bringing a hold on large renewals as they evaluate this new field?
Mark Anderson: Yes. Hannah, it’s Mark here. I’m not getting any sense at all from customers that they’re wanting to park our projects. I think it’s really the opposite. I think generative AI allows us to be able to get an even more green and even less experienced spreadsheet user and give them tools to be able to be like a data ninja. And generative AI just really helps us do that faster and better with less error. And so lots of examples that Suresh can talk about there.
Suresh Vittal: Yes. And just picking up on Mark’s comments there. We found the generative AI technologies that we announced are really a tailwind in adoption. We saw, as I said earlier, with Auto Insights, we saw 35% of our customer base latched on to Magic Documents. We’ve seen great adoption of our workflow summary capabilities. This is a generative AI powered capability that helps our customers manage documentation and governance and workflow insights. We saw, as we announced the AI work, the generative AI workbench all that enables our customers to build AI-powered workflows and applications. We launched the initial design partner program earlier this month, and we already saw nine out of our 10 largest customers enrolled into the design partner program. So everything we’re seeing is a positive adoption of the generative AI capabilities we’ve been introducing into the market.
Hannah Rudoff: Okay. That’s super helpful and good to hear. And then second one for me is for Kevin. I guess what is contemplated in ARR guide down from a renewal perspective? Are you assuming smaller renewals or downgrade? Are you factoring in deal push-outs in the normal renewal cycle?
Kevin Rubin: Yes. Thanks, Hannah. I would say it’s a couple of things. So one, we’re certainly taking a more conservative posture around any expansion that is not tied to a renewal event in the back half of the year. As we said in the prepared remarks, we actually saw a pretty strong retention and renewal rates as it related to the business in the first half. So we’re expecting that the macro continues to be challenging as we’ve experienced it thus far. But we really took a much more conservative approach to any expansion or upsells that are not attached to a renewal event this year.
Hannah Rudoff: All right, great. Thank you.
Kevin Rubin: Thanks.
Operator: Thank you. Our next question is from Sanjit Singh with Morgan Stanley. Please proceed with your question.
Sanjit Singh: And I want to take maybe just a quick couple of second break on the sort of the macro and some of the sales execution issues and talk about agent a little bit. Mark, from your perspective, like which aspects of the AiDIN platform are sort of more table stakes increases the overall competitiveness of the platform makes you more sticky versus kind of discrete monetization pricing opportunities onto themselves?
Suresh Vittal: I’ll take that, Sanjit. This is Suresh. So as we described our AiDIN strategy, we said there’s opportunity for us in three big domains, one in helping make everybody a more proactive analyst inside the enterprise by allowing us to help them automate a lot of the road task through generative AI capabilities. You saw evidence of that with some of the announcements we made. Second, we said we could help our customers do more advanced analytics, do generative AI, powered workflows, build analytical applications, and most importantly, trained these generative AI models, large language models using their unique data sets without having the data leave their four walls. We think that’s a significant opportunity of bringing large language models, training large language models, deploying them inside the enterprise.
