Capital One’s history of adopting an information-based strategy is helping it achieve an unassailable competitive lead as an AI-first financial services firm leveraging machine learning for delivering superior customer experience.
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Anand Trivedi
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A very interesting blog indeed! Very relevant to all non-native speakers and can really appreciate the value creation myself as an international student at such a diverse place as Harvard. Would be interesting to know what the customer economics look like under their freemium model – what’s their monthly subscription cost and the typical time a user takes to flatten the learning curve on the app; therefore what s/he ends up paying vis-a-vis other options. Also, it’s a very interesting product because better the product and quicker the learning process, the faster the end user is likely to drop off from the app leading to revenue loss. So, how do you keep the user engaged post learning curve flattens will be interesting to note. Thanks once again for this great post!
Thanks, Irina for the wonderful blog! See and Spray use case is one of the most sought-after in precision agriculture since the models can be easily generalized across crop types enabling quick upscaling. It would be interesting to know how they have sourced their datasets up until now and the costs involved therein. Also, what customer land size do their technologies become feasible at since a large part of farming happens in developing countries, where a smaller land parcel size is an issue in the adoption of such advanced technologies? Probably, shared rental solutions can pave the way in such geographies. Thanks once again for the insightful blog!
Very interesting post, Kate! Clearly brings out the limitations of AI text-to-image converters in better-representing contexts as well as what humans do. In fact, that’s probably the reason why the latest versions of Dall-E 2 nudge the users to prompt the AI in a structured form to include (a) what object, (b) with what features, (c) in what context/background, and (d) what form of art is expected – to be able to build pictures closer to user expectations. Thanks once again, Kate, for this insightful post!
Haha, this is indeed so real in multiple aspects! 🙂 While blurring of the face was expected, I wonder why the Harvard ‘H’ would not appear. While the former clearly has disinformation concerns around it, is it the same for the latter as well and if so, would these boundaries be applicable to all publicly known brands?
Indeed, Chiwon! Some of these applications have accelerated our society’s biases like none before due to issues such as non-representative training data sets, inaccurate evaluation function definitions, and lack of developer diversity at the stage of algorithm development. Be it resume shortlisting (Amazon case) or processing of college admissions, voice-based personal assistants to text-to-image converters, we have seen that across use cases. How various evolving AI governance frameworks catch up to this reality would be interesting to see. Thanks for your post!
Thank you, Karthik, for bringing up Udaan! It is by far one of the most amazing Indian unicorn stories; especially for the amount of transparency, selection, and control that it gives to India’s wide retailer base on their sourcing, pricing, and quality of procurement. It would be interesting to see how the value distribution/margins across the supply chain are shifting pre- and post-Udaan. Thanks once again, Karthik.
Thank you, Yannik, for the post! I haven’t used Strava but experienced the positive peer pressure to use one from many of their community members. What’s interesting is how they gamified it through a social network setup, unlike many other similar apps in their early days that took a self-monitoring perspective to sports and workouts. What’s also interesting from the graph is 2019 onwards, the growth in revenues grew at a much faster rate than its user growth which clearly points to increasing pricing; and despite that, the growth rate remained good. What would be interesting to see is the relative growth rates of its competitors, especially more recent ones, in terms of both revenues and user base, and compare their pricing strategies to see if some of their user personalities shifted out due to higher pricing. Thanks once again for the wonderful piece.
Thank you, Irina, for this amazing post! I really admire TGTG’s business model and superior user experience around the gamification bit that makes it so much more engaging. On Kate’s point of one of TGTG’s significant user bases coming from the student community, the gamification part has in fact added to accelerated adoption of the app. I have seen many friends here even in Cambridge use it and get positively surprised and then refer it to many of their friends. Sometimes though, restaurants have ended up giving out food that is not a mix that’s very easy to store and preserve – for example, one of my friends once got 6 big loaves of bread that were fresh but very difficult to store and preserve. Possibly, a user rating of restaurants’ giving patterns may help solve such issues. Thanks once again for the wonderful post!
Very interesting post, Gigi! Building on what Jonathan mentioned, it would be interesting to also reflect on how traditional ChatBOTs were considered to be the next big thing around 2016-17, before the excitement waned off given the limited functionalities they could automate and poor RoI considering their training efforts! As you have acknowledged as well, the key challenge for them seems to be the very nature of a non-vertically integrated AI factory and its horizontal spread on use cases, and so, it would be interesting to see which sectors and specific use cases emerge as their biggest winners.
Really enjoyed reading this post, Aditya! Asian Paints was always one the most cutting-edge companies in India in supply chain management. Nthato – Just adding that they have also leveraged AR/VR big time to help improve customer experience as well as in training its sales staff. Thanks for sharing this, Aditya!
Fascinating post! Also in some sense, relates back partially to our DeepMap case wherein, an asset, in this case, a global AI model for object detection, is being built through customer fleet operations. It is also very interesting how they have engaged a key stakeholder, i.e., the driver, to capture value from the data collected and close the loop by guiding fleet operators’ decision-making not just based on vehicle capacities, but also on individualized driver capacities/behaviors.