And I think what’s really interesting with Mercury, it’s really our approach to providing auditability back to the reports and documents in context, that’s going to be a real time saver for users because it’s going to enable them to act quickly on that information that they’re getting back and then move on with a high level of confidence to the next steps that they’re taking. So really, throughout the whole development process leading up to the data release, we conducted extensive user research. And through this, we really validated how important it is to deliver that auditability of the data back to our users. And we’ve already gotten great feedback on this and how we’re approaching that in the client meetings that we’ve had as well as the other features that it offers like suggested and next steps to guide users to insights that they might not have even realized were available.
The second pillar of the AI Blueprint is what we call mild deep workflow automation. So our strategy is really way beyond building an answer bot and kind of stopping there. So we’re going user-by-user type, workflow by workflow to leverage GenAI and help streamline the workflows of each of our different client types that we know really well from over 40 years of supporting them. So you would have read in the release that the first release of FactSet Mercury is geared for junior bankers, but it will expand from there. But so for now, for junior bankers, in addition to being able to answer any questions that they have about companies, it can take a lot of steps out of the bank or pitch book building workflow process. So one great example of that is just by enabling them to simply ask for any chart in natural language and then have it delivered to them as a custom formatted FactSet Active Graph, which is then forever refreshable inside their pitch decks.
So they don’t need to search through a gallery. They don’t have to adjust any setting. It’s just super easy for them. And then the last pillar of our strategy is what we call mile-high innovation acceleration. We’re very excited about what GenAI unlocks in our own product development process, and our teams are busy here, building some really amazing solutions. But it’s also, I think, important for everybody to remember that we’re developing those things in ways that can be leveraged as building blocks by our clients in their own products. And I think our open flexible approach to this is very differentiated and unique. And we’ve already begun to commercialize our data solutions in that way. And I think there will be more demand for these building blocks as clients make more progress with executing on their own AI road map.
So those are the three pillars. And yes, we’re very excited about FactSet Mercury launching into our FactSet Explorer’s beta program last week.
Jeffrey Silber: All right, thanks. That’s really helpful. Thanks so much.
Operator: Thank you. [Operator Instructions] Our next question comes from the line of Owen Lau from Oppenheimer.
Owen Lau: Good morning, Thank you for taking my question. So just a follow-up on the previous question about AI for client side. I know it’s still early, but could you please talk about the demand and timing of monetizing Mercury or other AI products? How does it help the contract negotiations so far? And how much have you baked into your ASV growth guidance in 2024? Thank you.
Kristi Karnovsky: I can talk — this is Kristy. Thank you for the question. I can talk a little bit about the approach we think we’re going to take here. What I was talking about with mile-wide discoverability with FactSet Mercury, we see that really as a natural evolution of the FactSet user experience. And so some level of conversational experience would be included with our different workstation packages. And I know that this is going to help users unlock more value and improve their efficiency. For what we call the mild-deep workflow automation in our AI Blueprint, things like pitch book building and automation with GenAI for investment banking or portfolio performance commentary for the buy side and other mild-deep workflows.
We are currently working on the best commercial models for this. We’re going to be able to significantly streamline these workflows and drive a measurable level of efficiency for our clients. So there’s definitely going to be value there for them. And I think we’re going to be able to share more color in future quarters as we make more progress and we get a little bit further along in the FactSet Explorer beta program. Yes, so hopefully, that gives a little bit more color.
Helen Shan: Thanks. And Kristi, I’ll add a little bit to what you just said to answer the second part of your question, Owen, which is monetization is one that everyone is trying to determine. I think that you’ll see — or we’ll see rather the benefits certainly come through from increased retention. And in fact, it’s allowing us to have some very interesting conversations with new clients. So we’re going to see how those come through, but it is not baked specifically into certainly the guidance for this year.
Owen Lau: Got it. Thanks a lot.
Helen Shan: Welcome.
Operator: Thank you. [Operator Instructions] Our next question comes from the line of Ashish Sabadra from RBC Capital Markets.
Ashish Sabadra: Thanks for taking my question. Just a question on the six, seven figure deals, which are in the pipeline. Are the closing of those contingent on opening up of the client budgets? And also on the last call, there was a reference around potentially client budgets opening up for calendar year ’24. And I was wondering, based on your — obviously, you had a lot of conversation on macro, but just based on the client conversation, what are the expectations for client budgets for next year? Any color on that front? Thanks.
