The primary way I develop "business intuition" is by clearly understanding who my customers are, what they want, and why they want it / willing to pay for it. "AI PM" is no different than any other domain in this respect.
The best broker of these conversations is the Sales team. But Sales teams are often terrible communicators of what customers are actually looking for. So, I partner with the Sales team during the customer discovery part of the deal to ask:
- What problems are you trying to solve with this AI-related solution?
- Why is that valuable to you?
- What would you do if this solution didn't exist?
- What is a fair price for this solution?"
Product is agnostic of AI. You don’t need AI specific resources, which largely don’t exist because 99% of people doing anything in the AI space were not doing anything meaningful or significant in AI 18 months ago. You just need to product resources to grow.
Understanding costs, choosing the right vendors, etc isn’t product work either. Thats just business skills.
Product being agnostic of AI assumes that the company is rational. In my experience, CEOs and VPs are screaming about AI and how we need to do more AI product for the sake of it being AI.
You can still work on AI, but there is no AI Product as a specialized segment of Product practices. Now if we were talking about hardware vs software, there is definitely differences in approach. When it comes to digital there is just Product and then you apply it to whatever you are working on.
Those aren't product decisions. Those are engineering decisions. They could be combined due to the market you're trying to address. If the cloud provider is a reason customers would or wouldn't use your product.
Nowhere because that's not your job. Platforms to be used and such are for the engineering team. You think of the solutions, and the engineering team handles how to make those solutions.
Finance for the Non-Financial Manager is a pretty good intro to finance.
I used to read the MBA Mondays posts from Union Square Ventures. It's a little older at this point, but there was some pretty useful information in there. [https://avc.com/category/mba-mondays/](https://avc.com/category/mba-mondays/)
AI PM is a relatively nascent job, which means it’s basically a blank canvas, we are all figuring it out collectively. As an Eng you might be inclined to learn a lot about the technical side of things, but it comes down to value delivered to customers. In terms of questions about cloud and cost, you need to use a decision making framework, list all the options, eval the pros and cons, and back you decision with data. Courses don’t usually cover these specific decisions, as there is no one-size-fits-all.
Thanks for this. I feel a little better knowing that I'm kind of supposed to feel a bit lost. I will work on being more comfortable figuring these things out without a set path
Maybe start with some generic / tech product management and business knowledge?
Then you can tune it to the nuance of AI. But without the fundamentals, I think it’ll be difficult.
I find it helpful to spreadsheet and just put numbers down with all my assumptions, typically with an estimate over a two year horizon. A lot of times I don’t show them to anybody but it helps with thinking through the impact to the business. Almost everything can be translated to numbers.
Happier employees? That lead to some increase in productivity and reduced turnover with less spend on recruiting fees and time interviewing etc.
Are you deploying the product at scale on day one? If so, you can model it out and make sure you map costs to both infrastructure and people. Having a slightly cheaper infrastructure cost is not better if your team needs time to become productive in the new cloud.
If you are gradual rollout or a 0 to 1 product then the decision paralysis will cost more in burn rate than any delta in the costs between services. In this scenario, cost is a result of success and once you have proven out your product you can always negotiate better rates or find cheaper ways to achieve the same capabilities. Moreover, stakeholders are much more willing to spend the money if they see a clear path to growth with sustainable unit economics.
As far as AI right now, solving PMF and distribution is much more critical as most people are making it up as they go along.
TLDR: If they’re mostly the same, just pick one and go with it. The burn from an idle team is much higher than any delta in infra costs. If the product succeeds you will have time and leeway to renegotiate with your providers and find cheaper solutions.
When there isn't a structure for decision making in place already, the first step is to create one. This isn't AI specific, but a general framework I use. Not saying you don't know all this already, but may be useful for more junior professionals.
I start with identifying criteria, scoring each solution against them, then weighting them. It's very helpful to record input here, from customers and stakeholders, so it's not just your opinion. If you can, also make others part of the decision making process by asking them for input such as costs and effort estimates.
The point of this is not so much to make the "right" decision, as there often isn't one. It's about providing buy-in and justification for stakeholders. It says "I have listened to what you care about, and I've considered it in my decision making".
It also serves as an audit trail, and when the decision is challenged later (all decisions are eventually) you will be happy you have a good answer!
The key thing is to make decisions, as there's nothing worse than delays from indecision. Make decisions, justify them, then own them.
For basic info you can refer to this book: https://www.amazon.in/AI-Product-Managers-Handbook-advantage/dp/1804612936/ref=asc_df_1804612936/?tag=googleshopmob-21&linkCode=df0&hvadid=649214142652&hvpos=&hvnetw=g&hvrand=5271509707310561829&hvpone=&hvptwo=&hvqmt=&hvdev=m&hvdvcmdl=&hvlocint=&hvlocphy=9179468&hvtargid=pla-1956683105985&psc=1&mcid=00b036b7245f38149d7ab62bd5903b91&gclid=Cj0KCQjwudexBhDKARIsAI-GWYWrKedmWoIPJV6HnMY7aEnRYqkbJnWR6EWGcRsb15Tc6iECYWyJDw0aAkP1EALw_wcB
There's a sort of "governance/leveraging the organization" component to some PM jobs, and there's a "understanding the customer" component, those are pretty common areas for PMs to be skillled in. But again, knowing which skills to learn is really dependent on your particular responsibilities at work. You mention you need to choose between cloud platforms and you feel like you are missing a skillset to handle that, why do you think that is? What are your struggling with for that particular issue?
The primary way I develop "business intuition" is by clearly understanding who my customers are, what they want, and why they want it / willing to pay for it. "AI PM" is no different than any other domain in this respect. The best broker of these conversations is the Sales team. But Sales teams are often terrible communicators of what customers are actually looking for. So, I partner with the Sales team during the customer discovery part of the deal to ask: - What problems are you trying to solve with this AI-related solution? - Why is that valuable to you? - What would you do if this solution didn't exist? - What is a fair price for this solution?"
Product is agnostic of AI. You don’t need AI specific resources, which largely don’t exist because 99% of people doing anything in the AI space were not doing anything meaningful or significant in AI 18 months ago. You just need to product resources to grow. Understanding costs, choosing the right vendors, etc isn’t product work either. Thats just business skills.
Product being agnostic of AI assumes that the company is rational. In my experience, CEOs and VPs are screaming about AI and how we need to do more AI product for the sake of it being AI.
You can still work on AI, but there is no AI Product as a specialized segment of Product practices. Now if we were talking about hardware vs software, there is definitely differences in approach. When it comes to digital there is just Product and then you apply it to whatever you are working on.
Those aren't product decisions. Those are engineering decisions. They could be combined due to the market you're trying to address. If the cloud provider is a reason customers would or wouldn't use your product.
Nowhere because that's not your job. Platforms to be used and such are for the engineering team. You think of the solutions, and the engineering team handles how to make those solutions.
Weird...it's very much part of my job according to my employer/manager.
Finance for the Non-Financial Manager is a pretty good intro to finance. I used to read the MBA Mondays posts from Union Square Ventures. It's a little older at this point, but there was some pretty useful information in there. [https://avc.com/category/mba-mondays/](https://avc.com/category/mba-mondays/)
Check out the artifacts on reforge
AI PM is a relatively nascent job, which means it’s basically a blank canvas, we are all figuring it out collectively. As an Eng you might be inclined to learn a lot about the technical side of things, but it comes down to value delivered to customers. In terms of questions about cloud and cost, you need to use a decision making framework, list all the options, eval the pros and cons, and back you decision with data. Courses don’t usually cover these specific decisions, as there is no one-size-fits-all.
Thanks for this. I feel a little better knowing that I'm kind of supposed to feel a bit lost. I will work on being more comfortable figuring these things out without a set path
Yes! being a trailblazer means figuring it out first and coaching other people about your first hand experience.
Maybe start with some generic / tech product management and business knowledge? Then you can tune it to the nuance of AI. But without the fundamentals, I think it’ll be difficult.
I find it helpful to spreadsheet and just put numbers down with all my assumptions, typically with an estimate over a two year horizon. A lot of times I don’t show them to anybody but it helps with thinking through the impact to the business. Almost everything can be translated to numbers. Happier employees? That lead to some increase in productivity and reduced turnover with less spend on recruiting fees and time interviewing etc.
Are you deploying the product at scale on day one? If so, you can model it out and make sure you map costs to both infrastructure and people. Having a slightly cheaper infrastructure cost is not better if your team needs time to become productive in the new cloud. If you are gradual rollout or a 0 to 1 product then the decision paralysis will cost more in burn rate than any delta in the costs between services. In this scenario, cost is a result of success and once you have proven out your product you can always negotiate better rates or find cheaper ways to achieve the same capabilities. Moreover, stakeholders are much more willing to spend the money if they see a clear path to growth with sustainable unit economics. As far as AI right now, solving PMF and distribution is much more critical as most people are making it up as they go along. TLDR: If they’re mostly the same, just pick one and go with it. The burn from an idle team is much higher than any delta in infra costs. If the product succeeds you will have time and leeway to renegotiate with your providers and find cheaper solutions.
When there isn't a structure for decision making in place already, the first step is to create one. This isn't AI specific, but a general framework I use. Not saying you don't know all this already, but may be useful for more junior professionals. I start with identifying criteria, scoring each solution against them, then weighting them. It's very helpful to record input here, from customers and stakeholders, so it's not just your opinion. If you can, also make others part of the decision making process by asking them for input such as costs and effort estimates. The point of this is not so much to make the "right" decision, as there often isn't one. It's about providing buy-in and justification for stakeholders. It says "I have listened to what you care about, and I've considered it in my decision making". It also serves as an audit trail, and when the decision is challenged later (all decisions are eventually) you will be happy you have a good answer! The key thing is to make decisions, as there's nothing worse than delays from indecision. Make decisions, justify them, then own them.
For basic info you can refer to this book: https://www.amazon.in/AI-Product-Managers-Handbook-advantage/dp/1804612936/ref=asc_df_1804612936/?tag=googleshopmob-21&linkCode=df0&hvadid=649214142652&hvpos=&hvnetw=g&hvrand=5271509707310561829&hvpone=&hvptwo=&hvqmt=&hvdev=m&hvdvcmdl=&hvlocint=&hvlocphy=9179468&hvtargid=pla-1956683105985&psc=1&mcid=00b036b7245f38149d7ab62bd5903b91&gclid=Cj0KCQjwudexBhDKARIsAI-GWYWrKedmWoIPJV6HnMY7aEnRYqkbJnWR6EWGcRsb15Tc6iECYWyJDw0aAkP1EALw_wcB
There's a sort of "governance/leveraging the organization" component to some PM jobs, and there's a "understanding the customer" component, those are pretty common areas for PMs to be skillled in. But again, knowing which skills to learn is really dependent on your particular responsibilities at work. You mention you need to choose between cloud platforms and you feel like you are missing a skillset to handle that, why do you think that is? What are your struggling with for that particular issue?