1 00:00:00,120 --> 00:00:03,960 Speaker 1: This is Bloomberg Business Week with Carol Messer and Tim 2 00:00:04,000 --> 00:00:07,760 Speaker 1: Steneveek on Bloomberg Radio. Well, very lucky to have with 3 00:00:07,840 --> 00:00:11,760 Speaker 1: us this afternoon, a trio of CEOs. We're to our 4 00:00:11,840 --> 00:00:14,240 Speaker 1: second right now, and it's the CEO of the enterprise 5 00:00:14,280 --> 00:00:17,960 Speaker 1: cloud data management provider Informatica, the company reporting results on Wednesday. 6 00:00:18,120 --> 00:00:21,720 Speaker 1: Investors and analysts, well, they really liked what they saw. 7 00:00:22,000 --> 00:00:25,360 Speaker 1: Informatica boosted its adjusted operating profit guidance for the full year, 8 00:00:25,400 --> 00:00:28,560 Speaker 1: the guidance beating the average analyst estimate, and matty Yesterday's 9 00:00:28,560 --> 00:00:31,600 Speaker 1: shares surg to close more than sixteen percent higher and 10 00:00:31,640 --> 00:00:35,400 Speaker 1: continued hire today by another one point six percent. We've 11 00:00:35,400 --> 00:00:38,639 Speaker 1: got the company's CEO. I'm at Wallya joining us right 12 00:00:38,680 --> 00:00:42,720 Speaker 1: now this afternoon. Good to have you with us. It's 13 00:00:42,760 --> 00:00:44,280 Speaker 1: good to have you. It's good to talk to you again. 14 00:00:44,320 --> 00:00:46,720 Speaker 1: It's been more than a year since since we last connected. 15 00:00:47,360 --> 00:00:49,400 Speaker 1: What worked out so well for you for the quarter 16 00:00:49,640 --> 00:00:50,800 Speaker 1: and what's your outlook for the year. 17 00:00:52,000 --> 00:00:53,760 Speaker 2: Pleasure to be in the show, Tim, thanks for having 18 00:00:53,840 --> 00:00:56,160 Speaker 2: me while we had a great quarter and a great 19 00:00:56,200 --> 00:00:58,480 Speaker 2: first half. Well, I think look, we've been a company 20 00:00:58,480 --> 00:01:01,120 Speaker 2: that has the flipped to the cloud business model, all 21 00:01:01,200 --> 00:01:04,680 Speaker 2: led by a platform intelligent data management cloud. And what 22 00:01:04,720 --> 00:01:06,600 Speaker 2: the analysts saw and they've been expecting, is that we 23 00:01:06,640 --> 00:01:09,080 Speaker 2: exceeded our expectations for growth in the top line a 24 00:01:09,200 --> 00:01:13,720 Speaker 2: cloud err crew thirty seven percent. And obviously in these 25 00:01:13,720 --> 00:01:18,440 Speaker 2: transitions people typically lose profitability. We actually exceeded our non 26 00:01:18,480 --> 00:01:21,039 Speaker 2: gap operating income and a unlivered free cash flow and 27 00:01:21,080 --> 00:01:23,080 Speaker 2: we raised the guidance for that for the year. 28 00:01:23,360 --> 00:01:25,920 Speaker 3: So I think growth on the top line boosting the 29 00:01:25,920 --> 00:01:26,520 Speaker 3: bottom line. 30 00:01:26,520 --> 00:01:28,760 Speaker 2: I think in a time like this, that's hard to 31 00:01:28,760 --> 00:01:30,360 Speaker 2: do and that's what our teams accomplished. 32 00:01:31,240 --> 00:01:33,120 Speaker 4: Well, that's the great news I do want to read 33 00:01:33,200 --> 00:01:37,319 Speaker 4: you from Cities Take on your earnings. They described the 34 00:01:37,360 --> 00:01:43,040 Speaker 4: fundamentals as still underwhelming, with aggregate growth continuing to decelerate, 35 00:01:43,120 --> 00:01:46,720 Speaker 4: but having said that, they do have a great outlook 36 00:01:46,760 --> 00:01:49,680 Speaker 4: on the stock. So talk to me about how you 37 00:01:49,680 --> 00:01:52,960 Speaker 4: would respond to that. What would you say about the fundamentals? 38 00:01:53,800 --> 00:01:54,560 Speaker 3: Thank you Madison. 39 00:01:54,720 --> 00:01:56,480 Speaker 2: Well, look, I think when you're going through a cloud 40 00:01:56,480 --> 00:02:01,240 Speaker 2: business transition, that's why our investors focus on a revenue 41 00:02:01,240 --> 00:02:04,120 Speaker 2: always takes ahead because of the whole accounting revenue recognition, 42 00:02:04,280 --> 00:02:07,160 Speaker 2: and I think this year our revenue from an accounting 43 00:02:07,200 --> 00:02:09,520 Speaker 2: point of view has taken the hit because of obviously 44 00:02:09,560 --> 00:02:11,760 Speaker 2: the cloud ratible recognition model. 45 00:02:12,000 --> 00:02:13,359 Speaker 3: But the cloud business. 46 00:02:13,000 --> 00:02:15,600 Speaker 2: That's growing very well sets up very well for as 47 00:02:15,680 --> 00:02:19,840 Speaker 2: we look forward finishing this transition this year. So that's 48 00:02:19,840 --> 00:02:22,000 Speaker 2: what it is. But it smooths out and look at 49 00:02:22,240 --> 00:02:27,040 Speaker 2: arr it basically shows you the intrinsic business grew very handsomely. 50 00:02:27,320 --> 00:02:29,440 Speaker 2: When you look at the entire cloud industry growing thirty 51 00:02:29,480 --> 00:02:31,360 Speaker 2: seven percent, was ahead of the market. 52 00:02:32,680 --> 00:02:35,120 Speaker 1: I mean, what are you hearing from customers right now? 53 00:02:35,440 --> 00:02:40,000 Speaker 1: We talked earlier in our program about advertising companies seeing 54 00:02:40,040 --> 00:02:43,640 Speaker 1: a pullback in advertising from some of the biggest tech firms. 55 00:02:43,680 --> 00:02:45,600 Speaker 1: We've seen some of the biggest tech firms do a 56 00:02:45,720 --> 00:02:48,800 Speaker 1: massive number of layoffs just this year. What are you 57 00:02:48,840 --> 00:02:49,840 Speaker 1: hearing from your customers? 58 00:02:51,080 --> 00:02:52,600 Speaker 2: Well, I think look, if you step back and look 59 00:02:52,600 --> 00:02:56,360 Speaker 2: at the enterprise software market, customs are generally cautious. I 60 00:02:56,360 --> 00:02:59,680 Speaker 2: think stock market has a different performer than how enterprise 61 00:02:59,720 --> 00:03:03,359 Speaker 2: it budgets out. I think people are thoughtfully spending. It's 62 00:03:03,360 --> 00:03:07,960 Speaker 2: still a very cautious environment. There's deal velocity is still 63 00:03:08,000 --> 00:03:10,000 Speaker 2: not as it was before all. 64 00:03:09,840 --> 00:03:11,760 Speaker 3: Of those things happening. But what's happening in. 65 00:03:11,720 --> 00:03:14,800 Speaker 2: That is people are prioritizing, They're spent towards things that 66 00:03:14,840 --> 00:03:16,840 Speaker 2: I can be creative to the top line or to 67 00:03:16,880 --> 00:03:21,040 Speaker 2: the bottom line, and areas like data, data, analytics, security, 68 00:03:21,200 --> 00:03:22,000 Speaker 2: those are the things that. 69 00:03:22,080 --> 00:03:24,000 Speaker 3: Move up the stack, and that's what I'm hearing. And 70 00:03:24,080 --> 00:03:26,800 Speaker 3: of course right now, JENNI is there. 71 00:03:27,320 --> 00:03:29,560 Speaker 2: Clearly everybody's talking about it, and I think when I 72 00:03:29,560 --> 00:03:31,640 Speaker 2: talk to enterprise customers, they are in the middle of 73 00:03:32,120 --> 00:03:34,160 Speaker 2: so where of all the things that I can do, 74 00:03:34,520 --> 00:03:35,360 Speaker 2: how do I begin? 75 00:03:35,880 --> 00:03:36,760 Speaker 3: Where do I begin? 76 00:03:37,200 --> 00:03:40,160 Speaker 2: Which technologies do I bet on? And very worried about 77 00:03:40,200 --> 00:03:41,680 Speaker 2: all the governance. 78 00:03:41,200 --> 00:03:43,400 Speaker 3: Aspects of GENI. Those are some of the biggest things 79 00:03:43,440 --> 00:03:45,000 Speaker 3: that I'm hearing from customers globally. 80 00:03:45,280 --> 00:03:47,320 Speaker 4: One thing that I can stop thinking about too, and 81 00:03:47,360 --> 00:03:49,440 Speaker 4: I can't wait to get your take on is companies 82 00:03:49,480 --> 00:03:52,440 Speaker 4: that are changing up their capital allocation when it comes 83 00:03:52,440 --> 00:03:55,680 Speaker 4: to their it spend to invest in AI so that 84 00:03:55,720 --> 00:03:59,200 Speaker 4: they can ride the AI rally and wave here. I 85 00:03:59,240 --> 00:04:02,240 Speaker 4: wonder what you're ta take is on who should not 86 00:04:02,840 --> 00:04:05,960 Speaker 4: be over investing in AI right now? Are there any 87 00:04:06,000 --> 00:04:10,440 Speaker 4: types of firms companies that you think shouldn't be over 88 00:04:10,560 --> 00:04:14,280 Speaker 4: investing in AI and should stick to their typical it spend. 89 00:04:15,440 --> 00:04:16,520 Speaker 3: Madison's a great question. 90 00:04:16,839 --> 00:04:20,080 Speaker 2: I'll say, Ai or Jenny I in particular right now, 91 00:04:20,480 --> 00:04:23,680 Speaker 2: tremendously overhyped in the short term, and I would say 92 00:04:23,680 --> 00:04:26,000 Speaker 2: tremendously underestimated. 93 00:04:25,200 --> 00:04:28,760 Speaker 3: In the lockdow what you mean by that, but I mean, 94 00:04:28,839 --> 00:04:29,720 Speaker 3: like the hype. 95 00:04:29,440 --> 00:04:31,320 Speaker 2: Cycle around that is so high that to the point 96 00:04:31,320 --> 00:04:33,080 Speaker 2: that you were saying that people may walk into it 97 00:04:33,120 --> 00:04:36,000 Speaker 2: with ice closed because it's just the shiny object to 98 00:04:36,040 --> 00:04:39,039 Speaker 2: go do something, and to me, the thoughtful way to 99 00:04:39,120 --> 00:04:39,920 Speaker 2: do something about. 100 00:04:40,000 --> 00:04:41,760 Speaker 3: First of all, it's going to be an inflection curve. 101 00:04:41,880 --> 00:04:46,320 Speaker 2: It will help customer companies both increase productivity and increase 102 00:04:46,320 --> 00:04:48,920 Speaker 2: intelligence driving the entire P and L. But you need 103 00:04:48,960 --> 00:04:51,520 Speaker 2: to be thoughtful about it. Where do you spend which 104 00:04:51,560 --> 00:04:53,440 Speaker 2: projects are they going to drive the revenue line or 105 00:04:53,480 --> 00:04:54,160 Speaker 2: the bottom line. 106 00:04:54,160 --> 00:04:57,000 Speaker 3: An example for that is we have our clear gptem 107 00:04:57,279 --> 00:04:57,960 Speaker 3: our own. 108 00:04:58,360 --> 00:05:00,400 Speaker 2: Clear engine, which is part of our plag from the 109 00:05:00,440 --> 00:05:03,440 Speaker 2: GPT version of that, where customers are in private preview 110 00:05:03,440 --> 00:05:07,239 Speaker 2: working with it. Where insurance companies looking at the claims 111 00:05:07,279 --> 00:05:12,080 Speaker 2: fraudulent claims that they get, can they increase the preciseness 112 00:05:12,160 --> 00:05:14,479 Speaker 2: or efficiency of that from eighty eight percent. 113 00:05:14,400 --> 00:05:17,680 Speaker 3: To midnineties, and that's a massive revenue. So I think 114 00:05:17,720 --> 00:05:20,880 Speaker 3: companies have to figure out which use cases. 115 00:05:20,880 --> 00:05:23,600 Speaker 2: Matter and don't just walk into it just because it's 116 00:05:23,600 --> 00:05:25,080 Speaker 2: the right now, the shiny object. 117 00:05:25,240 --> 00:05:27,880 Speaker 4: You said that it's underestimated long term, why aren't we 118 00:05:27,920 --> 00:05:31,760 Speaker 4: going to start to feel the power that AI has 119 00:05:31,839 --> 00:05:32,640 Speaker 4: in your view. 120 00:05:33,360 --> 00:05:34,800 Speaker 3: I think we'll see it in next year. 121 00:05:34,880 --> 00:05:37,200 Speaker 2: I think enterprises are always a little a step or 122 00:05:37,200 --> 00:05:40,480 Speaker 2: two behind consumers. All of us have used chat GPT, 123 00:05:40,680 --> 00:05:42,480 Speaker 2: My kids are using it. You and I are probably 124 00:05:42,520 --> 00:05:43,960 Speaker 2: using it. It's very easy to do. 125 00:05:44,040 --> 00:05:44,880 Speaker 3: It's the free. 126 00:05:44,760 --> 00:05:46,960 Speaker 2: Data of the Internet that we can access. But if 127 00:05:46,960 --> 00:05:49,720 Speaker 2: I'm an enterprise, it's your data. Privacy covenants are huge 128 00:05:49,760 --> 00:05:52,400 Speaker 2: issues around it, so on and so forth. I know 129 00:05:52,560 --> 00:05:56,600 Speaker 2: how our customers are playing with our product around GPT. 130 00:05:57,080 --> 00:05:59,240 Speaker 3: You will see use cases next year where they will 131 00:05:59,240 --> 00:06:00,600 Speaker 3: get tangible back be out of it. 132 00:06:00,720 --> 00:06:03,160 Speaker 2: But you know, like anything, it will take a curve 133 00:06:03,200 --> 00:06:04,960 Speaker 2: and I do think that the next eighteen months are 134 00:06:05,000 --> 00:06:08,400 Speaker 2: going to be very, very monumental, and I think they'll 135 00:06:08,440 --> 00:06:10,640 Speaker 2: be fun because we will see value coming out of it. 136 00:06:10,920 --> 00:06:12,920 Speaker 1: How are you attracting and retaining talent? Just in the 137 00:06:13,000 --> 00:06:14,640 Speaker 1: last forty five seconds that we have with you. 138 00:06:16,240 --> 00:06:21,040 Speaker 2: I think always focus on innovating. Innovation attracts talent. We 139 00:06:21,080 --> 00:06:23,840 Speaker 2: are blessed that in the world we live in data analytics, 140 00:06:23,680 --> 00:06:26,680 Speaker 2: it's the hot space to be so you know, talent 141 00:06:26,880 --> 00:06:29,560 Speaker 2: wants to work on those innovative projects, especially in the 142 00:06:29,600 --> 00:06:30,400 Speaker 2: world of AI. 143 00:06:30,480 --> 00:06:31,760 Speaker 3: There is no AI without data. 144 00:06:31,839 --> 00:06:35,240 Speaker 2: So we've been benefiting by these trends that are benefiting 145 00:06:35,320 --> 00:06:36,839 Speaker 2: our ability to hire good talent. 146 00:06:37,800 --> 00:06:39,160 Speaker 1: All right, I think we're going to have to leave 147 00:06:39,200 --> 00:06:42,360 Speaker 1: it there. I really appreciate you taking the time this afternoon, 148 00:06:42,640 --> 00:06:44,960 Speaker 1: Amid Walia. Good to talk to you. Like I said, 149 00:06:44,960 --> 00:06:47,720 Speaker 1: it's been over a year since we last spoke. Really 150 00:06:47,760 --> 00:06:50,400 Speaker 1: appreciate you joining us and taking us through the numbers 151 00:06:51,000 --> 00:06:53,160 Speaker 1: and also the outlook on AI for the remainder of 152 00:06:53,200 --> 00:06:55,719 Speaker 1: the year. That's the CEO of Informatica. I'm a Walia 153 00:06:55,920 --> 00:06:58,039 Speaker 1: joining us on this Friday afternoon.