B2B Vertical AI in FS - How to win?
As an AI startup in financial services - how do you find your right to win?
Its been a while since I wrote my last post. I was on the road for the entire month of Feb. Covering the West Coast (SF/Bay Area) and East Coast (NYC/NJ/multiple). Met many awesome people who directly or indirectly are thinking/doing something in AI and in many cases influencing it greatly. And I decided to write down my learnings and way forward in AI. and finally got some time over this weekend. I probably should keep all of this internal, but there are a couple of objectives that I want to achieve by sharing it widely — Get feedback on this post from my open network and discuss with other B2B AI founders.
At gAI ventures as some you might know, our focus is on AI products for financial services. There are many areas within banks, insurance companies, capital markets and even Fintechs where dozens or even hundreds of people are employed to do manual tasks and tedious processes. Our aim is to bring productivity and efficiency gains to these departments using AI. We partner with domain experts in niche areas to do that. Vertical AI = financial services
Okay. with that little background, let’s jump in.
Much of the discussion of 2023 and 2024 in AI was revolving around horizontal capabilities of foundational models. In 2025 as foundational model layer gets very competitive with Deepseek/Chinese models and other players launching powerful LLM models, it will get commoditised. As Satya Nadella said, OpenAI is now an AI products company that happens to have foundational models as well.
Now in 2025 attention is shifting to application layer. Today vertical AI is the talk of the town and for the right reasons. We’re entering the Vertical AI decade, where the winners won’t just be those with the best models but those with the deepest integration into workflows. Industry specific AI startups that embed deeply into existing workflows, automate high-value processes, and drive measurable business impact. US AI + FSI/fintech mkt is unraveling now…
US in general is way ahead in adoption than Europe or India or middle-east. From Morgan Stanley and similar Fortune 500 firms to small $25M AUM RIA I met, they are all doing something with AI tools.
Goldman Sachs CEO, David Solomon, said that AI is drafting 95% of an S1 IPO prospectus “in minutes” (a job that used to require a 6-person team multiple weeks)Stripe Engineering: Engineers reported spending 70% less time writing unit tests after implementing AI tools, fundamentally changing their test-driven development approach.
Amazon reported the impact of its AI system called Q: With Q’s code transformation capabilities, Amazon has migrated over 30,000 Java JDK applications in a few months, saving the company $260 million and 4,500 developer years, compared to what it would have otherwise cost Morgan Stanley built an assistant app for financial advisors to improve their productivity using AI and ability to answer client questions with huge digital memory of the org. AI to automate tasks such as summarizing meetings, drafting follow-up communications, and finding relevant insights within seconds. 16,000 financial advisors using AI Assistant. 30 minutes saved per client meeting. 10-15 hours saved per advisor per week. (Small to mid-size cos using tools like Jump, Zochs, and FTJust to be clear 90% of the market is still seeing it as pilots or POCs though. Good for innovation and good for startups! But its still early days in a 10 year long tech replacement cycle. we believe a lot of Software/SaaS that has been written in the last 25 years maybe rewritten - the unbundling has started and there will be rebundling in the future. This happened in Fintech as well from 2013 to 2023.
but AI is also a lot different. First of all there is too much competition in AI. Everyone wants a piece of the pie. and no one seems to be sitting idle. There is Box and Salesforce and other SaaS incumbents in many enterprise AI conversations. There is Accenture, Mckinsey and IBM in many conversations. And there are 1000s of startups in many conversation — big and small. There are indie hackers. And to top it all many enterprises and corporates want to try out things in-house first. So, as an AI startup, how do you win?* (will come back to this)
Another way to look at it is with this question someone asked - How to think about building products when anyone with Cursor, replit, v0, bolt etc can ship software in 24 hours and you're competing with thousands, not dozens. most founders follow a straight line: find problems, build solutions, repeat. but in an AI world, this approach is a fast track to commoditization. when anyone can identify problems and generate solutions in seconds, following the straight line only leads to Ok companies with no moats.
From an investment perspective, you will feel like 2021 all over again. There is a lot of investment happening in AI and some of it at crazy multiples. Reason is that large AUM investors look at things differently. They get exits from such investments in 7 to 15 years. So they are looking at a future value or terminal value and discounting their way backwards, so they feel that some of these companies could one day become so valuable. It will probably be worth it.
But for Pre-Seed & Seed Investors, Trace Cohen wrote a nice post on Why Vertical AI is the Best AI Investment and another good one by
As it happens with every new revolutionary tech that after the initial super bullish exuberance dies down … when shiny startups from silicon valley can no longer take the AI hammer and say everything looks like a nail. Well, we have seen this movie before.
The “AI can do everything” phase starts to cool down (not today), and we enter the “sober execution” phase in the future sometime where:
Hype-driven startups may fail – Those that didn’t find real product-market fit or relied too much on AI as a gimmick will struggle.
AI integrates into the workflows – Instead of being standalone AI tools, AI gets embedded into existing software and enterprise systems.
Regulation always catches up – Governments start shaping policies around AI safety, copyright, and employment impact.
ROI expectations will rise – Companies demand real cost savings and productivity gains, not just “cool demos.”
We’ve seen this before with cloud computing, blockchain, VR/AR, and even the internet itself. The survivors are usually the companies that:
1. Solve a real problem (not just “AI for the sake of AI”).
2. Have strong distribution & sales execution (AI startups need more than just good tech).
3. Evolve with AI’s limitations (understanding where AI is actually useful vs. where it’s just hype).
Now let’s deep dive into the main part of this story.
*So dear founders, how do you win?
Industry first and problem first approach - In every technology hype cycle we have seen that it comes back to identifying problems first and then thinking about the solution. As an example from one of our own startup - theAudit.ai
First thing is to have discussions and brainstorm about the problem statement for months before you decide - Lets go and solve it. Audit is a lengthy, costly and hard to deal with process. Building Audit AI, Anzar (has 18 yrs+ experience in compliance and audits), Kushal (CTO) and I asked lot of questions to ourselves:
Why compliance and audit space? Why is this bundle of tasks bundled this way? Why did they design Audit like this in the first place? Why only sample transactions, we could look at (controls testing) all the transactions. And so on.
—> then comes the solution part that maybe we can use AI to build the best product possible. this is the right order I guess.
To further illustrate the point read this note:
Choose the right business model and how will you position your AI offering:
you can start with Service as software - This is the most interesting. people are calling it by various names. Dharmesh shah of Hubspot called it Waas and Raas . Owning the flow end to end = new AI-native way = Service as software.
essentially you promise outcomes and you deliver outcomes and you get paid for it. Infact, today when many enteprises/corporates are reluctant and unsure about data security and other issues it is even better that you don't ask the customer to adopt AI. In fact, in most cases, the customer does not have to learn about AI and how to adopt it. They just want to get things cheaper, better, and faster. Gushworks is a good example https://www.gushwork.ai/
another way is to follow the vertical saas playbook. As Sangeet Paul said - Vertical software repeatedly followed a playbook of
(1) gaining adoption with a focused use case like Harvey AI (offering tools primarily for drafting, reviewing, and researching legal documents)
(2) rapidly rebundling adjacent capabilities around that initial use case.
Long-term competitive moats in vertical AI are created by rebundling around the initial use case, using the unique rebundling benefits that AI provides.
Market selection, strong market need and GTM:
Pick a great market - first hand experience - the US is way ahead in the adoption of AI than EU/India or many other regions. 3 large fortune 500 cos I met there recently have so much going on in AI and so does small companies in the same space (already spending on AI tools). Across the length and breadth of corporate America you can take a niche and you will get customers for your B2B AI. And your product will actually make money and have margins. Compare that to India or ME where it can be challenging.
It's difficult to sell in the US as well, especially for first timers. You need previous relationships and connects. You need face time. You need to embed in the culture. You need local teams. You need capital for events and wining and dining folks. And most importantly you need to excel on the product side - in the US the best product wins. Unlike some other markets where the best distribution wins
Strong market need - Dharmesh Shah of hubspot recently said - The best way to win is not to start by developing a strong product but to discover a strong market need. If there's strong market need, but you have a weak product, you can iterate and make the product better over time. You can control your product iteration. You can't control market pull. I couldn’t have said it better.
Also what will the GTM team look like (probably needs another full blog).
and what is your sales philosophy? I read an AI startup founder wrote it well - Sales isn't just about closing a deal. it's about creating relationships where everyone wins. You help your customer win first and make money in the process and not the other way round. Try to be an amazing partner first. I can’t emphasize how the long term thinking helps your sales function. think about your sales philosophy
Pricing is another area which needs a lot of thinking. depending on your AI product proposition and whether you are selling seats or outcomes or some other way.
Old SaaS Model → Per-seat pricing (e.g., Salesforce).
AI Model Shift → Usage-based, value-based, or AI-Agent-driven (e.g., Harvey AI, Gushworks).
Look at some data below:
For further reading on pricing models there is an interesting read - X post by Aaron Levie of Box - This provides a completely new growth vector for software companies in AI, and has major implications to software business models. For a wide number of use-cases, there may be little to no connection between the number of users on a platform and the total amount of usage of AI Agents that use the software -- and what the value is that those Agents can deliver. And for many use-cases, the idea of a user seat being tied to the agentic workflow altogether is unnecessary when agents are just running around completing work in the background for an enterprise.
If you’re an AI founder in financial services, the time is now. Let’s connect!
what’s working for you, and what challenges are you seeing?