I’m excited to share this conversation with Ankit Raheja. Ankit is a lead product manager focused on AI, data, and APIs at CDK Global. During this conversation, Ankit discussed the AI product development lifecycle,  metrics for AI products, and how product managers could start their AI journey with small steps.

Links

Ankit on LinkedIn

CDK AI Survey: What Automotive Leaders Think About Artificial Intelligence

DeepLearning.AI: Start or Advance Your Career in AI

Transcript

[00:00:00 ] Himakara Pieris: Welcome to the smart product show. My guest today is Ankit Raheja. , to start things off, could you tell us a bit about, , your current role and how you're using AI, as a product manager?

[00:00:11 ] Ankit Raheja: Absolutely. Currently I am a lead product manager at CDK Global.

[00:00:20 ] Ankit Raheja: CDK Global is the largest Car dealership software company in the United States, we power more than 15, 000 dealership location. So, so that's why it is one of the most biggest force but which you haven't, which you haven't heard about because you do not interact with it directly, but I'll tell you 15, 000 plus dealerships are using it.

[00:00:53 ] Ankit Raheja: And. We are embedded across the whole user journey, starting from [00:01:00 ] the front office. Front office is when you go to a dealership for purchasing a car and getting all the different warranties and insurance options. Second is the fixed operations. The fixed operations is the car services that you get done when you go to a dealership.

[00:01:21 ] Ankit Raheja: Then there is some back office. You can imagine dealerships need to take care of like inventory of the parts. And the vehicles and there are many more other things and last but not the least these dealerships need Massive infrastructures to run so we are embedded across all these four Parts of the user journey, the next question that you mentioned about Like where exactly we have used ai so so I have been in the ai space since 2013.

[00:01:55 ] Ankit Raheja: It was a combination of data and AI. In past, we [00:02:00 ] have used AI across companies such as Cisco, Visa, and state compensation insurance fund. We have worked number one in the customer support. Use cases, then we have worked in market segmentation, use cases that visa and finally healthcare fraud detection, use cases that state compensation fund currently where I'm using AI at CDK, we are leveraging it across multiple ecosystems.

[00:02:33 ] Ankit Raheja: Number one is we are trying to match potential customers with potential cars. So it's like a propensity to buy a model. Second is predictive service. Basically what we're trying to do is that when you go to a car dealership and, and sometimes you do not know what services. additional services that you need.

[00:02:56 ] Ankit Raheja: And, and, you know, you are a busy professional, [00:03:00 ] you have so many other things to worry about. So we want these car dealership employees to be able to recommend you additional services that you may have not even thought about. So that's the second use case. Last but not the least. We are also exploring benchmarking use cases where something like dealers like you, for example, you have one dealership group and you don't know whether, how are you doing?

[00:03:24 ] Ankit Raheja: Like, are you doing well? You need to back up on few of the things. So, so that's where the benchmarking comes in. So these are the current use cases. And as you know chatbots are becoming more and more prevalent now. So, yeah, but right now just want to focus on the current use cases and the use cases that I've worked on previously.

[00:03:47 ] Himakara Pieris: Great. And before this you had an interesting use case with chatbots at Cisco as 

[00:03:54 ] Ankit Raheja: well. Absolutely. Yeah. I can definitely talk to you a little bit about the [00:04:00 ] chatbot at Cisco. The, let me tell you some... context around the issue. Basically Cisco has lot of switching products, router products, basically all B2B products.

[00:04:17 ] Ankit Raheja: And some of them as you can imagine will become defective and, and you want to return those products. However, Cisco identified that a lot of these products do not need to be returned. Some of them are avoidable returns. So technically we were trying to solve an avoidable returns problems. This existing way to solve that was that these customers would reach out to the technical assistance center engineers.

[00:04:55 ] Ankit Raheja: who are technical customer service engineers, if [00:05:00 ] in, in more layman terms, and they troubleshoot these problems from them and then decide whether this product should be returned or not. We realize. AI could be a really big help to these technical assistant center engineers because you can basically have a lot of skill.

[00:05:25 ] Ankit Raheja: Number two it's like an intern. AI is like an intern, which is trying to learn new, new things. So as it learns more and more, it will get, become better and it will become a lot more helpful for them. And sometimes these technical assistance engineers are not available, that's where this chatbot can come in.

[00:05:43 ] Ankit Raheja: So, multiple use cases, why we thought AI made sense, and, and we really had great impact by leveraging AI for this use cases. 

[00:05:56 ] Himakara Pieris: So Cisco and CDK, these are very large companies [00:06:00 ] with a ton of use cases. 

[00:06:02 ] Ankit Raheja: How did you decide 

[00:06:04 ] Himakara Pieris: the use cases and when to use AI, when to not use AI and what kind of framework do you use for that?

[00:06:12 ] Ankit Raheja: Absolutely. I'll have a spicy take on this. The first rule of AI is not to use AI in the first place when you're in the discovery stage. You should be able to understand how. A human can do this work better for example, I'll give you two examples, autonomous driving car, what could happen right now, instead of autonomous driving car, what's happening, you're the one who are driving, so you're the one looking around, hey, here's the signal, here's this pedestrian, here's this road, so you should be able to do that first.

[00:06:51 ] Ankit Raheja: Another thing for chatbot, right? So we had this technical assistance engineers who were doing it. So, so this is a very, [00:07:00 ] the framework is pretty simple and universal. AI is only one of the ways that may solve this customer's problem while ensuring its need to drive business value. We have seen so many times right now, as you've seen with the chat GPT hype, more and more products are coming out, but the time will tell how many of them will really be retained.

[00:07:25 ] Ankit Raheja: Right now there's big hype, but eventually retention is the key. So to think about this, I have a very simple framework and this is overused a lot, but there's a bit nuance to it. The number one is user value. Are you providing real value to customers? Why should these customers hire your solution? Are you helping them with their jobs to be done?

[00:07:52 ] Ankit Raheja: So that's the first thing. That's the first constraint that you'll look at. Number two, which is very important. You may not even get [00:08:00 ] funding if you don't have a good answer for it. That's your business goals. Just because your c e O said, Hey, I see the chatbot chat gpt is doing really well. You need to really start from the vision.

[00:08:12 ] Ankit Raheja: Go to the strategy, goes to the goals and come with your KPIs. And what are your KPIs? Do you want to acquire more users? Number two, you want to retain more users. Number three, you need to monetize these user more by upscale or cross sell. Or last but not the least you need to drive more word of mouth, net promoter score.

[00:08:33 ] Ankit Raheja: So that's the second thing, the business goals. The last constraint that we need to think about is the The technical component of it, like how comfortable are you? Okay. Using a predictive solution versus a deterministic solution. Sometimes, if you can imagine [00:09:00 ] there like you can make a machine go through and read one medical chart for cancer.

[00:09:10 ] Ankit Raheja: Would you give all the... Onus on the machine to make a call. I would not say that. So you still need to have a human in loop. However, in some cases like recommendation engine for Amazon, there are so many different permutation combination that can, can, can come with the long tail option. So that's where the the AI makes sense.

[00:09:33 ] Ankit Raheja: So it all depends from case to case basis. If you want me to go more into detail, I can definitely go more into detail about the AI use cases.

[00:09:41 ] Himakara Pieris: generally speaking, start with with a focus on customer value and then map it to your business goal and strategy and have clear KPIs. And make sure that your proposed solution could deliver on those KPIs. Absolutely. 

[00:09:59 ] Ankit Raheja: So, 

[00:09:59 ] Himakara Pieris: how [00:10:00 ] would you compare, let's say, more of a deterministic solution? So, if you have a, I'm sure at