At Momentum91, we've been hard at work understanding AI and their use-cases for mid-market and large firms. Late last week, I had the opportunity to share everything we've learnt so far with eChai Ventures at GVFL Limited.

Here are some notes:

👉 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐢𝐬 𝐞𝐯𝐞𝐧 𝐦𝐨𝐫𝐞 𝐝𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭
Other people also have AI. A lot of folks are building on top of AI and so there is increased competition in almost every vertical. Every niche software has like 3 bootstrapped competitors who are going to market via SEO. Also, product differentiators are difficult to communicate in the age of low attention spans. If you are starting distribution today, it needs a thorough re-imagination.

👉 𝐀𝐛𝐨𝐮𝐭 𝐍𝐚𝐫𝐫𝐨𝐰/𝐖𝐞𝐚𝐤 𝐀𝐈
Instead of building systems that are kind of smarter at doing a lot of things, you might want to consider building multiple and separate systems that are very smart at doing a few things extremely well. Enterprises/companies will separately pay for solving specific problem areas and addressing their use-cases. The game is depth, not breadth.

👉 𝐍𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐬
Companies like ClickUp and Notion have done an amazing job at bringing AI within their products. But they are still not AI native. They were conceptualised when AI was not around. They have fit AI retrospectively. There is a large opportunity for building Native AI experiences. Platforms and products that are conceptualised from ground-up in the age of AI. An example of this is a project management system where I can assign tasks to both humans and agents. It will demand a lot of capital and ingenuity but it can be done. Here's a good question to ask: What other 'systems of records' exist that can be made AI native?

👉 𝐒𝐨𝐥𝐯𝐞 𝐟𝐨𝐫 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞 𝐝𝐚𝐭𝐚
AI will solve for analysis, predictions, pattern recognition etc. Most firms do not have accurate data. If you can figure out a way for a business to collect accurate data about their prospects, customers, employees, products, sales, etc - using AI, you can do anything and everything. Figure out a way as to how you will solve for collection, storage and organisation of accurate data.

There were a few other things that we discussed.

👉 Co-pilot and Autopilots
👉 AI wrappers
👉 AI & Funding
👉 Verticalising LLMs
👉 AI Safety
👉 Real-life implementations of AI by companies we have not heard about

This conversation was meant to be a starting point for folks thinking about building in the AI space. It was by no means exhaustive - but sort of a summary of all the research Harsh, Jay, Koushikram and I have been doing for the past few months.

If you want the deck, let me know in the comments and I'll send it over 😇

Thank you Jatin for the opportunity.

Until next time :)