How do you launch an AI startup if data is a very important component of that business? There are many ways, and I will provide the main two here:
(1) Spend tons of money at launch to collect data which will be used to improve your AI models. A good case study is Temu, an ecommerce company, which uses AI to soup recommendations on what to buy and wear. Temu spent close to $5 million during the last Super Bowl, America’s largest yearly event, to quickly collect data to improve its models. With millions on-boarded as a result of that massive media blitz, Temu has enormous data to quickly improve its technology.
(2) Partner with a company which has tons of data. That is for OpenAI, the owners of ChatGPT. By partnering with Microsoft, ChatGPT became a category-king product which would not have been possible without the decades-old data which Microsoft provided. Indeed, besides the code, the best raw material here is the data! And I posit that there are better AI models somewhere in the universities. But those schools do not have the datasets which Microsoft has across its product lines like Xbox, Bing, etc.
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If I have explained how to launch AI startups, what of a fintech company which focuses on payment (i.e. a paytech) in Africa? I can posit that starting a startup in most African markets with a clear mission to offer pure-payment is largely stale. To a large extent, in markets like Nigeria, Kenya, Ghana, etc, payment is not necessarily a major friction anymore. And if that is the case, launching a paytech company may be very hard. Indeed, the incumbents have built moats and getting to the castle may be challenging.
So, to launch a paytech, you need to add many other goodies as part of the product. Indeed, your payment must come with other services because just collecting payment will not take you far. And as you offer those services, explore partnerships because the most important layer now is distribution. Your tech means really nothing; what matters now is scale via partnerships since pure play organic growth is harder. Simply, you have to connect into promising ecosystems, thereby offering a lot more than payments to your customers.
Such could include inventory management solutions, accounting, project management tools, etc which many of your customers can use to run their operations. In other words, you need to offer more value than the ability for users to just move money via your channel. Those channels are everywhere and adding another one may not bring growth.
That is what everyone is doing: Flutterwave is in partnership with an Indian bank even as Interswitch expands Google Pay in Nigeria.
Interswitch, a leading African integrated payments and digital commerce platform company headquartered in Lagos, has integrated Google Pay on its Payment Gateway (IPG) platform.
This integration will enable individuals and businesses in Nigeria to make a wide range of financial transactions, including in-person contactless purchases, for goods and services, peer-to-peer money transfers, and more, using their mobile devices.
In this Tekedia AI in Business Masterclass courseware [we used AI to record some of the modules], we explain what happens with AI, Data and Value in AI Projects. In my last post here , I listed some ways to launch AI startups; this short video notes the sequence to turn that data into value. Remember: the most important phase is the AI readiness assessment, and there, you examine the business case and technical feasibilities for that project.
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Comment 1: What a valuable piece of content. ?
Prof, I completely agree with you.
It is very true that the most important phase is the AI readiness assessment, which involves checking for both the technical and business factors for successful AI projects.
It’s very important that development teams understand how serious this phase is.
It’s not just about technology. The most important factor is identifying valuable business problems where AI can provide significant business value and competitive advantage. Focusing on the business needs comes first. The business case justifies the investment.
Also, on the technical side,
From my opinion, focusing on data health comes first – Bad data equals bad models and so data collection, labeling, cleansing, security, monitoring has to be rigorous. I consider this part of the pain in building your own model or fine-tuning an existing one.
The importance of doing a readiness assessment cannot be overemphasized.
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