Episode 135: The Future of RevOps with Tina Kung

So, we need to prevent any kind of hallucinations on the AI, especially when deploying AI over finance because these are actual money transactions, not just summarization and predictive analysis, and that's what New does, making our platform a lot better.

Hi, my name is Demetri Panici and I'm a content creator, agency owner, and AI enthusiast. You're listening to the AI Agents podcast, brought to you by Jotform and featuring our CEO and founder, Aydogan Tank. This is the show where artificial intelligence meets innovation, productivity, and the tools shaping the future of work. Enjoy the show.

Hello and welcome back to another episode of the AI Agents podcast. Today we have Tina Kung, the founder and CTO of New IO. How are you doing today, Tina?

Doing great, thank you for having me.

I'm really excited to have you on. First, tell us a little bit about yourself and what you do at New IO, and how did you get into AI in the first place?

I'm Tina Kung, co-founder and CTO of New. We founded New five years ago to streamline the entire quote-to-revenue journey for modern businesses across all sales channels and revenue models. Back then, GenAI wasn't really taking off, but our platform was designed to adapt to hybrid revenue models. When GenAI took off a couple years ago, we saw a dramatic increase in people wanting to innovate on their revenue models, and that's where New fits perfectly.

New is essentially a transactional intelligence cloud platform that helps companies run revenue operations with far less friction across all sales channels and revenue models. We unify quoting, billing, usage, consumption, and all revenue data into a single system record, then apply intelligence on top so teams can see what's happening, understand why, and act quickly.

For engineering and RevOps leaders, New removes a huge amount of load with fewer brittle integrations and clearer data flows, providing systems that scale as the business grows.

When you founded New, what was the initial mental framework or spark moment that made you say this needs to be here? Tell us more about that founding story.

We are a Silicon Valley-based company and talked to 70 different companies ranging from small startups to giants like Nvidia, Splunk, and Snowflake before their IPO. The unanimous pain point was that quote-to-revenue was segmented and broken. Even selling on one revenue channel was hard to streamline, and adding multiple channels created data everywhere that was not integrated. In the AI era, bad or segmented data means AI won't work effectively because AI needs data coherence and context to avoid hallucinations.

Because we operate in revenue and finance, data accuracy is super important, especially for AI. It's not easy to build an AI-first revenue operations system because it takes years to build up accurate data to run real intelligence on top.

There's often a misconception about what you can build out of the box with AI versus the years of experience needed to properly guide powerful AI. Many RevOps AI tools do data summarization and machine learning, but New focuses on transactional intelligence. We don't just summarize data; we summarize a customer's entire revenue journey from quotes to payments and churn analysis.

Our AI creates actionable insights and actually takes actions for you. For example, you can do whitespace analysis to recommend renewals with price uplifts and upsells, and AI can execute those actions by chatting with it to renew, upsell, generate quotes, orders, and invoices automatically. New is much more than summarization; it acts on insights.

What industries do you find are really looking to take advantage of your product and service?

New has two sweet spots. First, fast-growing modern AI companies with hybrid revenue models across multiple channels, like OpenAI, which started with subscription and now has enterprise sales across partners. Second, successful first-generation SaaS companies trying to catch the AI wave by adopting new revenue models and sales channels, needing to revolutionize their enterprise systems to support quote-to-revenue journeys.

What is your approach when onboarding new clients? Is it hands-on, self-service, or a mix?

Our sales are very high-touch because quote-to-revenue touches sales, revenue, and finance. We work closely with front-end sales, revenue operations, and finance teams. Customers often have hybrid sales channels including direct sales, partners, and self-service channels like product-led growth and in-app billing portals.

Working with modern AI industry growth in revenue model innovation helps us build our product roadmap. We also have large customers in Europe where we helped automate complex quote-to-revenue journeys with hybrid revenue models and integration with ERP systems, realizing the full value of New across the board.

What are your thoughts on the unique pricing models in AI, like credit-based or subscription-based, and how companies optimize revenue without upsetting customers?

Many AI companies have significant B2B enterprise sales alongside consumer models. RevOps teams modernize revenue models to track revenue and margin effectively. New revenue models like commit burn down allow customers to commit upfront but use credits flexibly across subscriptions, consumption, and services, with unused commits rolling over, providing predictable and adaptive revenue.

Looking ahead, what are your goals for improving your product?

As CTO, I think about making our product significantly better. There are two perspectives: first, practical AI that is front and center in our platform to handle the complexities of quote-to-revenue flexibly and accurately while preventing AI hallucinations. Our AI lets users interact via chatbot to perform real transactions like renewals, price uplifts, and upsells automatically.

Second, we want to deploy AI in self-service portals so developers can build portals with embedded business logic easily. We also listen to customers and monitor revenue trends to lead and solve new pain points and evolve revenue models.

When did you officially start the company?

We started in 2020.

What has been the most surprising thing since starting during the AI cycle?

Initially, we focused on product-led growth because it was popular during the pandemic, but we realized 80-90% of prospects still focus on direct sales. We pivoted to focus on direct sales first and layered on self-service later, which was a big architectural change. The hybrid revenue model focus was not surprising because I knew the world would switch to it from my experience at Zuora and Salesforce.

After AI took off, everything started moving much faster, and we fit perfectly into this era of change.

What do you think about AI's future in coding and workplace usage?

We discuss this daily. AI-assisted coding is now standard; no one codes without it. The difference is the percentage of AI assistance. AI enables product managers to build prototypes and UX in ideation phases, and engineers can fully grasp designs and give feedback. The software development life cycle has changed with AI tools like Glean generating test plans, documentation, and code reviews. We use AI tooling in CI/CD and have chat-based operations to deploy and troubleshoot, enabling small teams to innovate faster and more reliably.

Hiring criteria have changed from pure coding skills to builders mindset, collaboration, communication with engineers and AI, and leveraging AI tools alongside coding and system understanding.

Those who think well about processes and how to do things effectively will succeed in this new world. AI changes the game so that steady flow and proper AI use can outcompete fast hands-on keyboard work.

AI has revolutionized many fronts of day-to-day work. For example, we use Glean for performance appraisals, generating self-appraisals quickly and securely, saving hours of writing.

What are some of the best ways you've improved your productivity as a founder and CTO?

My days are back-to-back meetings, but after 6 p.m. is my time working with AI. I work with six or seven AI agents simultaneously for coding, design, office work, and more. We have an AI champion Slack channel to share AI experiences and tools across teams. Marketing uses AI for lead generation differently from traditional methods. Quiet time with AI helps me understand the AI roadmap and what features are valuable for me and my team, spreading knowledge to avoid falling behind and improve work quality and life quality.

What are your thoughts on MCPs and how they help you?

I love MCPs. We have a Google Doc for engineering onboarding listing MCPs to set up, including our own, Salesforce, GitLab, Green Dot, and Jira MCPs. If a company doesn't have an MCP, we develop it. MCPs are our first deployment and product because they embed years of knowledge about transactions and orders into APIs accessible in the AI world. MCPs are model-agnostic, so customers can use any AI model with our MCPs.

MCP is a model contextual protocol standard that will become more common, enabling interoperability across AI industry tools. In the future, agents will interact with other agents through MCP discovery to gather information from integrated systems instantly and provide summaries and recommended actions without needing all data in a data lake.

For example, an agent can gather payment, finance, support, and ticketing data to assess renewal chances and recommend actions. MCPs enable processing renewals, generating quotes, sending e-signatures, and more, providing great value.

We could have an entire episode on MCPs because of their amazing value. Thank you for spending this extra time. Please leave a like and subscribe, and check out nue.io, a great platform for managing revenue across the entire lifecycle.

Thank you, Tina, for having me on the show.