I'm excited to bring you this conversation with Ali Nahvi. Ali is a Sr. Technical Product Manager for AI and Analytics at Salesforce. During this conversation, he shared his thoughts on championing AI initiatives as a product manager, translating business needs into AI problem statements, and how to positioning yourself for success.

Links

Ali On LinkedIn

Transcript

[00:00:00 ] Ali Nahvi: to get there, to build that success, success story. You need to fail. And failure is part of the process and sometimes it's not easy for people to see that,

[00:00:09 ] Himakara Pieris: I'm, Himakara Pieris. You're listening to smart products. A show where we, recognize, celebrate, and learn from industry leaders who are solving real world problems. Using AI.

[00:00:19 ] Himakara Pieris: I'm excited to bring you this conversation with Ali Navi. Ali is a senior technical product manager for AI and analytics at Salesforce. During this conversation, he shared his thoughts on championing air initiatives. As a product manager, translating business needs into air problem statements. And how to position yourself for success. 

[00:00:37 ] Himakara Pieris: Check the show notes for links. Enjoy the show. 

[00:00:42 ]

[00:00:43 ] Himakara Pieris: Ali, welcome to Smart Products. 

[00:00:47 ] Ali Nahvi: Thank you so much Ima, for having me 

[00:00:49 ] Himakara Pieris: to start things off could you share a bit about your background and how you got into AI product management? I.

[00:00:58 ] Ali Nahvi: I'm an accidental [00:01:00 ] product manager. I started my career journey with business intelligence and I guess it was around 2012 or 13. It was the first time I've heard the board data science. Before that we simply called it math. And I love the idea. I decided to move from BI to ai and that was the major figure for me to come to us do a PhD.

[00:01:27 ] Ali Nahvi: And I did my PhD in application of ai m ml in the context of project management. And after that I started as a data science consultant. In a consulting company and yeah. And, and, and one day out of blue my roommate from grad school called me at the time who was working at Amazon and he told me that, Hey, I mean, we have this thing in product manager and I think you should, should become one of them.

[00:01:56 ] Ali Nahvi: I did some research and very quickly I [00:02:00 ] also. I've got the same impression that, well, this can be an ideal job for me. I love helping people. I love solving business problems. I love ai. And I also love business development and communication and being around people.

[00:02:16 ] Ali Nahvi: So I thought, well, that might not be a bad idea. So I joined iron Mountain in my first for like manager role. And then I joined another company after a while Cengage, which was mainly focused around online education. And recently I've joined Salesforce as a senior technical product manager for AI analytics.

[00:02:45 ] Himakara Pieris: What is the primary difference you see going from, bi to data science, to ai as a product product manager? Do you need a different skillset? Are those, BI skills transferable across all these verticals?[00:03:00 ]

[00:03:00 ] Ali Nahvi: Yeah, business intelligence definitely still helping me a lot. 

[00:03:04 ] Ali Nahvi: And from data science perspective, I'm one of those PMs who thinks that PMs should be technical and have the ability to have that super technical discussions with the teams especially in data sciences space. In data science, in AI ward, understanding the problem, understanding business requirements is, in my opinion, is solving half of the problem.

[00:03:31 ] Ali Nahvi: If you get there, if you can really digest the problem statement and have the ability to transfer that into a data science language then you are a really good PM and, and to do that for me, Having that technical background around data science have been extremely helpful. 

[00:03:51 ] Himakara Pieris: What would be a hypothetical example for translating a business requirement into data science or machine learning language?[00:04:00 ]

[00:04:00 ] Ali Nahvi: Let's say I'm assigned to work with a stakeholder in sales or marketing. And I sit with them, set up a call and say, Hey, what's your pain point?

[00:04:12 ] Ali Nahvi: And they say, okay, I wanna increase sales and productivity. And so I would say, okay so can you explain what you're doing on a day-to-day basis? And they, they explain, this whole sales process that they go through from lead generation to sales calls to closing deals, and I might be able to find some opportunities there.

[00:04:36 ] Ali Nahvi: To use AI to help them to do a better job. For example, the lead generation piece. Maybe you don't need to call all the customers, all, all the leads coming to your way. Maybe you can optimize that. Okay? But then you need to build a bridge. Between that really weight business problem into a very solid, robust data science problem.[00:05:00 ]

[00:05:00 ] Ali Nahvi: The business requirement doesn't give you anything like dependent variable, independent variable, the data structure, anything like that. So as a product manager, it's my job to help the team to kind of define that problem. And another thing that I believe that, that, that's why I think data, data science, product managers should be technical, the feature engineering.

[00:05:22 ] Ali Nahvi: That's extremely delicate thing to do in my opinion. It's, it's something that where you tie business with science and you really need to have good understanding about how data scientists would do feature engineering. And at the same time, you really need to have a robust understanding of how business operates to, in incorporate all the important features in your feature engineering and make sure you capture all the important elements. 

[00:05:51 ] Himakara Pieris: You talked about, doing these customer interviews or user interviews looking for opportunities, these might be data, sort of [00:06:00 ] curation opportunities or recommendation opportunities or clustering opportunities or, or what have you, that sort of.

[00:06:09 ] Himakara Pieris: Buried in, in the story that they're saying. 

[00:06:11 ] Himakara Pieris: You identify that and then you transform it from there to a, a problem statement that machine learning and DataScience folks can understand. Right. Could you talk me through the full workflow that you're using? So what are the key steps? So sounds like you're always starting with a use interview.

[00:06:28 ] Himakara Pieris: How does the rest of the process look like? 

[00:06:31 ] Ali Nahvi: Let's go back to that sales problem again. For example, on the late generation, they say that, okay, we generate 2000 leads per day, but we can only call. 500 of them. So the, the lead optimization problem that I mentioned before that would pop up or on the sales calls, they say that we have limited number of sales mentors who can help salespeople.

[00:06:54 ] Ali Nahvi: So maybe we can leverage AI to listen to some of the recorded calls and provide some [00:07:00 ] insights. So these are all hypotheses that could come up and I will write them down, all of them as potential initiatives. And then I would ask these questions from my stakeholders all the time. Let's say we are six months from now, a year from now, let's say we are done with this and we build this model that is a crystal.

[00:07:20 ] Ali Nahvi: Al can tell you this lady's gonna make it, this lady's not gonna make it. How would you use it in your day-to-day, how it's going to change your workflow? Okay. And, and based on that, I, I try to basically come up with an estimate, ideally a dollar value around the, the potential added value that initiative can have.

[00:07:44 ] Ali Nahvi: And then I would work with my team engineering managers, data science managers, try to understand visibility, data accessibility, data availability, and level of effort. , and based on that, I create a diagram [00:08:00 ] in, in one axis we have value. In the other we have level of f effort. And when you build something like that, it, it, it would immediately pop up and, and the, the, the high highest priority initiatives would, would show themselves to you.

[00:08:19 ] Himakara Pieris: Sounds like you're identifying opportunities and then solutions, and then you are going through an exercise of validating these solutions. Right? And then it moves to the implementation part. I want to go through and discuss how if it is different from a traditional software development process.

[00:08:41 ] Ali Nahvi: Absolutely. There are major differences between data science and software engineering and lots of intersections. So intersections are obvious. They both need coding. They both need infrastructure.

[00:08:54 ] Ali Nahvi: They both need data. But there is a, a delicate [00:09:00 ] difference between them that. It's, it's, it's kind of hidden in the name of data science as well. It's science, it's not engineering. So element of uncertainty is there. All of these initiatives that we came up with, they are just hypothesis. We have a hypothesis that based on the current data, based on the current evidence, we might be ab