How Nue Is Bringing Agentic AI To Revenue Operations

[00:00:00] - [Speaker 0]
So a big thank you to NordLayer for backing the podcast and supporting the kind of real world cybersecurity conversations that we need more of. Because as someone that records 65 plus interviews a month, I've personally seen a huge increase in browser based attacks over the past year, whether that be phishing, malicious extensions, account takeovers, the list is long. And it's all happening where people spend most of their time inside the browser. So NordLayer's new business browser, that's built to address exactly that. It blocks malicious sites before they load.

[00:00:37] - [Speaker 0]
It limits risky behaviors like uncontrolled downloads or data sharing and gives you visibility into how your team interacts with web apps. And it also helps you stay compliant by controlling access and enforcing policies without the need to rely on multiple disconnected tools. So for anyone listening that is thinking seriously about reducing risk in SaaS heavy environments, this feels like a smarter and more focused approach. And you can learn more about it by visiting nordlayer.com/browser. Let me know what you think.

[00:01:12] - [Speaker 0]
But now, let me introduce you to today's guest. Have you ever stopped to think about what really happens between a sales quote and the actual revenue that hits the books? And why does that gap still cause so much friction for businesses here in 2026? Well, my guest today is someone who has spent over two decades right in the middle of that problem, long before AI became the headline. Her name's Tina Kung, and she's the CTO and cofounder of a company called New.

[00:01:47] - [Speaker 0]
And she's built her career across companies like Oracle, Salesforce, and so many others. And she's seen firsthand how disconnected systems slow everything down just when businesses are trying to move faster. So today, I wanna look into how those experiences led to her building an enterprise platform designed to bring quoting, billing, and revenue data all into one place, but then layer intelligence on top so teams can finally see what's happening and act upon it. But what really stood out to me in our conversation was how she explained the shift from AI as something that simply informs decisions to something that actually executes complex business operations. So today, we're gonna get into the rise of product led growth, hybrid sales models, and how pricing structures are forcing companies to rethink everything that they thought they knew about revenue operations.

[00:02:49] - [Speaker 0]
And we'll also wash all that down with real world examples of how one of the fastest moving AI companies rolled out this kind of system in just eight weeks. It's a big name. You're gonna love this one. So if you've ever wondered why scaling revenue feels harder than it should or what transactional AI really looks in practice, hopefully, this will be one of those conversations that lets you join the dots. So a massive warm welcome to the show.

[00:03:22] - [Speaker 0]
Can you tell everyone listening a little about who you are and what you do?

[00:03:28] - [Speaker 1]
I'm I. So I'm Tina Kang. I'm the CTO and cofounder of New dot ai. So, essentially, what New does is a new enterprise grade quote revenue platform that offers transactional intelligence to help companies run sales and revenue operations and finance operations with far less friction. So we unify quoting, billing, usage, and revenue data into a single system of record, then apply intelligence on top of that so teams can see what's happening, understand why, and act accordingly and quickly.

[00:04:15] - [Speaker 1]
Right? So so that's what New does. And then so New was founded six years ago. It's been a while with the mission to unify quote to cash for all channels and all revenue models. And then ever since AI, you know, we entered into the, you know, generative AI era.

[00:04:40] - [Speaker 1]
We saw kind of tremendous market traction because in the AI era, everything moves extremely, you know, fast. Right? So companies from small to large, from, you know, first generation SaaS companies to the companies in the AI era, they all need to be able to, you know, revolutionize their pricing model, their revenue operations, their quoting, their billing so that, you know, they can make do really achieve business agility. Right? This is where we got a lot more market traction and then because we're able to really help companies improve their quote to cash across all channels and also for all different modern revenue models from the traditional one time selling of physical goods and professional services to subscriptions to all kind of crazy midterm changes of subscriptions to the newer model of credit burn down, commit burn down, and all that.

[00:05:54] - [Speaker 0]
Absolutely love that. And there's so much I wanna talk with you about where you are now and where you're heading, the kind of problems that you're solving. But if we go way, way back here, before everyone was excited around AI, I'd love to find out more about your origin story because it's not just about a new company putting AI onto something. You're someone that has spent twenty years, in the opportunity to cash space. And before cofounding New dot io, I think he was also VP of products and engineering at, there's a few companies I was looking.

[00:06:28] - [Speaker 0]
I think you also spent time at Oracle as well and Salesforce. But tell me more about your backstory because you must have seen a lot of problems there that that must be help pave the way, for the story behind New io where we are now. So tell me more about that backstory and and how you got here.

[00:06:48] - [Speaker 1]
Yeah. Yeah. That's also interesting side of the story as well because I got CS degrees, both bachelor's and master's. Right? And then after after I graduated, I came to Silicon Valley to pursue a software engineering career, and then I started in Ariba.

[00:07:10] - [Speaker 1]
Ariba was a enterprise I think it was enterprise procurement company, currently part of SAP. Right? And then I I started essentially my enterprise platform applications of engineering career then. Then I joined Oracle later on a kind of startup called Niku, which was acquired by CA, where I learned a lot about, you know, enterprise applications, enterprise platform, both at Oracle and CA. Right?

[00:07:43] - [Speaker 1]
At that time, I was able to really serve customers with our self where the customers include, you know, giants like AT and T, Walmart, Coca Cola. So I kind of learned a lot about how enterprise companies manage, you know, their software internally, right, to to use software to really help them manage business operations. Then, you know, I joined the whole SaaS move. So I joined, you know, Zora and later on SteelBrick, which was acquired by Salesforce. So at Zohra, SteelBrick, and Salesforce, I really kinda come back to the, you know, to the to my root of CPQ.

[00:08:29] - [Speaker 1]
I learned at Oracle, but applied to the modern kind of subscription economy. That was the whole thing at Zuora. K. And so I started really connect the dots, right, to to from my early very early days to the kind of, you know, the SaaS generation. And then later on, you know, I still break at Salesforce to really kinda see, like, oh, so CPQ and billing has evolved tremendously from the the early day early days while I was at Oracle, while everybody's selling bit machines and all that, to the subscription era led by all the modern SaaS companies.

[00:09:14] - [Speaker 1]
Right? And then also to, you know, kinda Salesforce served as a big platform trying to really manage the the the, you know, quoting opportunity and billing side. However, there's still a huge gap in between, you know, the CPQ side, which is kind of on the selling side, and the water to cash side, which is on the kind of the monetary operation side, there's a huge gap in there because CPQ ends with quoting, which is typically their own stand alone software. And then order to cash starts with order starts with orders and essentially ends in accounting ERP. But these two these two areas didn't really connect.

[00:10:10] - [Speaker 1]
And then they have very distinctive object models that prevent them from really integrating and connecting seamlessly. So I saw a lot of pains while working with customers at Sora, customers at Salesforce. Even back then customers at Oracle. Right? Even though these are really large enterprise customers, they have to invest in a lot of engineering resources in their IT, in their business operations just to, you know, connect these two these drawing systems together.

[00:10:48] - [Speaker 1]
And then that creates a lot of friction. Right? If you're just selling in the direct sales motion, you know, it's probably it was probably fine. But in the modern era, not only you're selling with, like, sales reps within the direct sales motion, you need to layer on PLG, product led growth. You need to also layer layer on partnership selling.

[00:11:17] - [Speaker 1]
Right? So with all these channels and now with, you know, all these, like, data coming from everywhere, If you have a revenue operation system that goes from quote all the way to revenue that is, you know, largely disconnected, it is gonna be really hard to change your pricing model, to change your revenue, you know, revenue motions, revenue or revenue selling motions. Right? So essentially, you know, there's just a lot of pain operating as a modern business. And these are large companies, not mentioning, like, you know, smaller mid market companies that just really trying to trying to move very fast, you know, to go up market or to explore a different market.

[00:12:13] - [Speaker 1]
They need different selling motions, different revenue models, but the systems are becoming the blockers for them to move fast. Yeah. So that was actually the inside of me before the AI era. This is why we found, you know, we found it new. But AI era essentially just expedite everything.

[00:12:37] - [Speaker 1]
So everything moves, like, 100 times faster. And companies are really chasing the wave to to to understand how how do I, you know, optimize our pricing? How do I how do we change to this new cool pricing model that is a best fit for the AI era? Right? And then now with all these systems that this joint together, they couldn't.

[00:13:01] - [Speaker 1]
That's why they came to New to really find try to find a solution that can help them move faster and make more changes faster.

[00:13:11] - [Speaker 0]
And it's a great journey that you've been on. And, yes, we've seen AI in the last couple of years. If we go back just a couple of years before that, I think you said that, you founded the company or cofounded the company, about six years ago. That must have been right in the heart of the pandemic. Was did that help or hinder you?

[00:13:27] - [Speaker 0]
Because there there's so many so many big changes going on around that time. Did it allow you to just focus on this while, there were so many lockdowns, etcetera? What what was going on there when you founded the company?

[00:13:40] - [Speaker 1]
Yeah. That's a really good question. It's definitely interesting time during the pandemic because we just started the development engineering. The pandemic really helped us to really be heads down and focus on product development. Right?

[00:13:55] - [Speaker 1]
So I was working with our founding engineers and our they were already go to market teams to just to understand, okay. So what kind of you know, how do we develop the product features? You know, how do we quickly pivot for the market we're trying to serve? How how do we get the the first couple or the first five customers and serve them well? Right?

[00:14:23] - [Speaker 1]
So that was a really actually good time for us to focus. And after we got out of the the the pandemic, it was time really time for us to, you know, start the real go to market motion to to have real sales cycles, you know, marketing and everything. So I would say the first three years or or two and a half years was very interesting, very helpful for us as well just to make sure we have a really solid foundation of the product. Because our product, if you see my background, right, I've been, you know, in the enterprise platform application for two decades before I started new. So our product since day one, what you know, had the foundation.

[00:15:09] - [Speaker 1]
We built the foundation to serve enterprise level businesses. So it wasn't meant for, you know, like, a smaller SMB business because there there's a there's actually pretty a lot of kinda quote to cash or quote to CPQ and billing solutions for SMBs already. Right? The market is pretty, I would say, saturated. But if you go to mid market and go to enterprise level, enterprise market is pretty much only few companies out there that can serve this level of business with operational agility.

[00:15:54] - [Speaker 0]
It's such a great story. It's almost like the stars were perfectly aligned for you because you spent three solid years building those foundations, making sure everything was ready, and they had that everything in place. But then, of course, literally, at that perfect moment, AI then arrived on the scene, or should I say generative AI. And then you've got this mission now to build a unified AI powered platform that makes revenue operations more elegant, intelligent, and beautifully simple. So tell me more about how AI and automation are are now redefining go to market operations because you've been there from the beginning and seen so many big changes, and you're going out to solve very real problems.

[00:16:36] - [Speaker 0]
But tell me more about that because we hear a lot around the hype, but not necessarily the the real world problems that we're solving here.

[00:16:43] - [Speaker 1]
Yeah. So, yeah, when AI came, right, I I think everybody was like, you know, now what what do I do with AI? And our customers especially, you know, came to us to say, you know, now what what needs to be changed, right, in terms of pricing model to in terms of, like, our selling motion. If you look at AI first companies, almost every single AI company is almost, like, fantastic. Right?

[00:17:11] - [Speaker 1]
They started with the PLG motion, product led growth. Right? For example, if you look at OpenAI, they start with track GPT for consumers. And then now they are selling, you know, to enterprise. They're they have a really good enterprise selling motion.

[00:17:27] - [Speaker 1]
They but, you know, the first, you know, customers they acquire are, you know, consumers. Everybody's just started using ChatGPT and, you know, are just really impressed by the capabilities of it. Right? So and then, you know, for for me, myself as well, ChatGPT, I use that as consumer and then later on use ChatGPT in in, you know, in the company with the team edition. But we see a lot of AI companies go go with this.

[00:18:02] - [Speaker 1]
Right? So they have naturally been through, like, a PLG selling motion with, you know, self-service channel to a enterprise selling motion with the direct sales channel, and then there's you know, they layer on partner channel. So that's that's fantastic for us to really understand, hey. How new can actually help them. Right?

[00:18:25] - [Speaker 1]
So so ever since the AI era, we also have started our self-service product line so that it's in our initial vision all the time. But, you know, after the AI era, we quickly developed our self-service channel so that, you know, we'll you know, our customers will be able to really sell with all self-service, direct sales, and partner channels all combined. So their data is completely unified. Right? When they're able to see insights of their data, they will be see they will be able to see in insightful data from all of their channels.

[00:19:08] - [Speaker 1]
And then also new AI was also launched second, like, q three last year to help our customers to use, you know, AI agents to be able to quickly create new quotes, new orders, check out their billings, and be able to do any kind of amendments to the to their subscriptions as well. All under in the chatbot so that they don't have to go through, like, a lot of clicks on, you know, all pages to be able to make all these complex, you know, initial quotes and order creation and the, you know, subscription amendments that used to be really, you know, complex operations. The new UI make it much, much easier, but new AI made it agentic. Right? So that it's AI would be able to analyze, you know, the white spaces, being able to, you know, recommend upsell opportunities, being able to not just recommend, but actually make it happen so that you can see the transaction created.

[00:20:19] - [Speaker 1]
You can see the quote created, order created, see their PDF, you know, set the quote set the opportunity code, close one. And then the fun part is AI would congratulate you saying, hey. Congratulations for opportunity. This close one. Now this order is activated.

[00:20:36] - [Speaker 1]
The invoices are sent to our customers, and then you can see the invoice PDF right in front of you. Right? So those are the, you know, a lot of major updates we have done both for our customers and also for our own AI, right, to help customers optimize their experience in the AI era.

[00:21:01] - [Speaker 0]
And for people listening and they're hearing about this rise of transaction level intelligence connecting product finance and GTM data, then we've got Agentic AI coming in as well. But they're also wanting to find out more about why they need it, why is it solving, any or what problems is it solving for me? What measurable difference does it make? Why do modern SaaS businesses need AI driven insights to stay agile today? Tell me more about that that big value proposition that you're offering here.

[00:21:31] - [Speaker 1]
Transactional AI, we see is the real inflection point. Right? So a lot of peep a lot of AI today essentially, you know, apply AI over your, let's say, customer service data, right, to tell you, like, what happened to help you with troubleshooting, to help you with next steps. And then also there are predictive AI tells you what might happen. Right?

[00:21:58] - [Speaker 1]
So that that those AIs already are available. However, transactional AI actually does the work. So for example, you can create a quote with all the complexities. Right? So for example, you are selling in an enterprise motion where you are selling with different regions, having different currencies, have different level entities, different pricing for different regions, and all that stuff.

[00:22:29] - [Speaker 1]
This is very this is a there's a lot of complexity behind these. Which price point are you going to going to select? How do you know that, you know, when you create a quote, the prices you select are available for this region? All kind of complexity baked inside of these these transactional AI. So as a sales rep, imagine that you can just, you know, tell AI, create a quote for this customer.

[00:23:00] - [Speaker 1]
And then with a 100 seeds or with a million dollars commit committed spending for the next year or for the next three years. And every single year, I will need a 10% price uplift. Right? You know, there's a lot of information in this one prompt already. Right?

[00:23:21] - [Speaker 1]
And then AI will be able to identify potential potential identify all the complexities and resolve to a price book entry that can actually can be used to create a transaction. And then would actually execute the multiyear contract renewals, midterm changes, and price adjustment for you. So this shifts AI from basically decision support to operational leverage. Right? And the new is a transaction level, you know, upper you know, revenue operation system.

[00:24:00] - [Speaker 1]
So this level of transactional support, transactional level intelligence support is very important for our customers.

[00:24:10] - [Speaker 0]
My next question was gonna be about asking you to bring to life exactly how companies could use, new dot I o to scale complex monetization models and maybe share a customer story that would really bring it to life even more. And before you join me on the podcast, I I was doing a little research today, and I saw the perfect customer story there. I was reading that OpenAI, used, new, and, also, they rolled it out in just eight weeks. So tell me more about that case study or or that real user story there of they had a challenge. They got up and running in eight weeks and the difference that it made.

[00:24:50] - [Speaker 0]
Tell me more about that because it's a great story, isn't it?

[00:24:53] - [Speaker 1]
Yeah. Yeah. Definitely. OpenAI is apparently, you know, the, you know, we're the most successful AI companies. Right?

[00:25:06] - [Speaker 1]
So so, you know, when they chose new, they had a very tight schedule to go live. Right? And as you know, OpenAI, they they roll out new products at lightning speed. So that's how that's how they handle internal business operations as well. Right?

[00:25:29] - [Speaker 1]
So every single week, we check on, you know, how we're doing and then what are the goals we need to achieve this week. Essentially, we're running the eight week go live process in a very stringent way. Right? So every every week of of go to to reach and everything. However however, I think the beauty of the implementation process is it's very simple or very, you know, very simple to use new to be able to create the product catalog as with complexity and to be able to customize according to their specific needs.

[00:26:13] - [Speaker 1]
Right? Especially this is what happens to enterprise businesses. They don't all run the same process, you know, having the same, you know, user. They required very different user experiences for different operations. So to be able to customize new in a very easy and elegant way has also helped with the implementation.

[00:26:38] - [Speaker 1]
And and later on, you know, that was, like, about a couple years ago. Right? Later on, we have been, you know, establishing a very good business partnership relationship with OpenAI so that all the new features we added, right, you know, we have been partnering with OpenAI, especially one on the AI features as design partners so that we'll be able to understand and listen to their business needs and be able to leverage that. We'll be able to incorporate that into our day to day, you know, feature design and implementation as well.

[00:27:25] - [Speaker 0]
Well, we've talked about your origin story today, your mission, the problem that you're solving, how companies like OpenAI rolled it out in under eight weeks. And along the way, you've also raised a lot of cash as well, so certainly want to watch there. But where do you go from here? What's your big focus now? What can we expect not just this year, but in the future?

[00:27:45] - [Speaker 0]
Anything you can share around the road ahead and what we can expect from you?

[00:27:50] - [Speaker 1]
Yeah. I think the this year is gonna be a really big year for for, you know, for AI and also for the modern SaaS company as well. You probably heard a lot of a lot of, you know, chitchat about SaaS is dead and everything. I think it's just people are just, you know, worried or concerned about, like, the traditional SaaS. How is it gonna how is it gonna involve in the in the new AI era?

[00:28:22] - [Speaker 1]
So this year, New's mission is really to be able to continue to streamline the quote to cash with with AgenTeq. Right? So we continue to to build out the transactional level AI for our customers and also, you know, our prospects as well continue to evolve on the AI road map So that will be able to really enable our customers to from that range from the the the fastest growing AI companies to the, you know, SaaS companies that really want to adopt modern AI in the AI era so that they can stay competitive, right, in the AI era as well. And then the other side, you know, for for new, we we have been having a lot of really big success in the CPQ quoting area. And then this is the year we're gonna we're gonna expand our road map throughout quote to quote to cash and then have deeper integration with system, especially ERP systems, you know, that that are closing in the AI era.

[00:29:39] - [Speaker 1]
Right? There's a lot of new ERP systems and also the existing ERP giants as well. So that new will be sitting really well well integrated in the ecosystem. So we can integrate with really, really well with CRM such as Salesforce and also all the modern or, you know, existing ERP giants so that we can continue to enable our customers with amazing quote to revenue journey.

[00:30:19] - [Speaker 0]
Exciting times ahead. And for anybody listening wanting to keep up to speed with some of those announcements as they drop and also work learn more about how they can work with you or your team, where would you like me to point everybody listening?

[00:30:33] - [Speaker 1]
Yeah. I think yeah. If you definitely welcome everyone to follow our our company on LinkedIn, and then we have a lot of really amazing contents on LinkedIn. And just follow follow Tina Kang on LinkedIn. So oftentimes, I'll post a lot of really unique, you know, product innovations happened happening anew, especially around transaction level intelligence that new AI brings.

[00:31:09] - [Speaker 0]
Love that. And I also love the mission that you've been on. Or should I say I love the story behind the company, the decades of experience. You know this industry inside out. You've encountered problems there, and then you built these foundations.

[00:31:23] - [Speaker 0]
And bot AI are now on this mission to give organizations transaction level intelligence that ultimately enables them to understand, predict, and optimize revenue in real time all through data and AI. Phenomenal work. I'd urge anybody listening to check you out. I'll include links to your LinkedIn, everything you mentioned there, and the content that you produce, and also the, OpenAI example as well. I think it's essential reading.

[00:31:52] - [Speaker 0]
But, Morneithi, thank you for sharing your story with me today.

[00:31:55] - [Speaker 1]
Thank you. Thank you so much.

[00:31:57] - [Speaker 0]
So what did you take away from our conversation today? For me, the big insight was how much of modern business friction still comes down to disconnected systems, even inside companies that are otherwise pushing the boundaries of innovation. And I think Tina made a strong case that AI only delivers real value when it moves beyond dashboards and predictions and actually takes action inside those workflows. And I also found it incredibly interesting how the pace of this AI era is finally exposing weaknesses that have existed for years. Pricing models, sales motions, internal processes, All these things are being tested at a level of speed that many organizations simply weren't designed for.

[00:32:46] - [Speaker 0]
They were built in a different time, an analog era. And here we are now, there's this idea of transactional intelligence. Yep. It sounds incredibly technical at first, but when you hear it explained through real world use cases, I think it becomes clear that it is about removing manual effort, reducing errors, and giving teams the ability to move with confidence. So if you are working in SaaS revenue operations or in fact any environment where growth depends on agility, I think there's a lot here reflecting on.

[00:33:21] - [Speaker 0]
But as always, I'll leave links to Tina and her work in the show notes. But I'm curious. What are you seeing as the biggest gaps between AI potential and the very world and the very real business outcomes inside your organization? As always, techtalksnetwork.com. That's where you'll find me.

[00:33:41] - [Speaker 0]
Please share with me your insights too. But that's it for today. Time for me to go, I'm afraid. But, hey, have no fear. I'll be back again tomorrow with another guest, and hopefully, I'll get to speak with you all then.

[00:33:54] - [Speaker 0]
Bye for now.