AI
=
NLP (Natural Language Processing = Speech to Text and vice-versa)
+
ML (Machine Learning = capability to learn from data and predict)
+
DL (Deep Learning = vision, image processing, etc.)
1.
Whether your product should be Web-first or Mobile-first or Bot-first or AI-first?
It is usually a decision you have to take based on your goals (for e.g. adoption, retention, CR, revenue, etc.)
2.
Which all capabilities - NLP or/& ML or/& DL - do we need to build in our product?
For example:
Do we need NLP (api.ai, Amazon Lex, Pullstring, Lex etc. for building assistants like Alexa, Google ALLO etc.)?
Do we need ML (for eg. finding the right job for the right person, predicting patient’s diseases, predict house prices etc.)?
Do we need DL (for e.g. applying vision capabilities to cars to make self-driving cars or to slowly introduce smart features into cars)?
3.
Never suddenly introduce or remove AI one day and change it another day - it can lead to bad user experiences. So, plan AI, decide AI, develop AI, deploy AI, see user's feedback on AI, & then change/remove AI. Ideally, you should be able to retract AI features that aren’t working properly before everyone uses your apps.
4.
How much AI?
How much of your App will be AI-driven and how much will be manual or usual software?
5.
Decide whether introduction of AI will increase the number of steps or decrease the number of steps for a user?
Sometimes the value of what we are providing is very high, so even if we are increasing the number of steps, it’s ok.
6.
Think about the barriers that exist for users to use it (like: privacy, not personal, personalization for a certain demographic, cultures, etc)
7.
Think about hiring a person from humanities background - to build for people.
8.
You are always launching something new in AI (because the field is new), the incremental launches need to be cohesive.
9.
Always keep a “roll-back procedure” ready and a letter from the business' spokes-person also, in case there are potential disruptions because of the new experiments.
10.
You'll need a context for your AI features/products and it’s the hardest things for your software application to learn.
Spend a little bit of time writing down contexts and also write plans and flows to deal with each of them.
11.
What if your Machine learning engineer leaves in the middle of your startup? Can you afford to hire another one? Most universities in India have now started incorporating ML and AI in their Computer Science courses. Be sure that the demand of Engineers will be met!
12.
How should you price your AI applications?
You can pick various kinds of models. May be you will not charge for the software at all and only charge for the AI part of it or may be a combination of both or may be operate a completely different business model (such as the value of data as business model).
13.
How are you going to communicate toyour users about your new AI based feature(s)?
You don’t necessarily have to say it (Google and Amazon have used this for years. Amazon slowly introduced Alexa as a harmless addon in the beginning. It didn’t make a big deal about it, although Alexa might be Amazon’s biggest strategy in the coming years.)
14.
What are the key KPIs based on which you will evaluate the success of your AI?
How will you track the same?
What are the industry benchmarks?
Do we need DL (for e.g. applying vision capabilities to cars to make self-driving cars or to slowly introduce smart features into cars)?
3.
Never suddenly introduce or remove AI one day and change it another day - it can lead to bad user experiences. So, plan AI, decide AI, develop AI, deploy AI, see user's feedback on AI, & then change/remove AI. Ideally, you should be able to retract AI features that aren’t working properly before everyone uses your apps.
4.
How much AI?
How much of your App will be AI-driven and how much will be manual or usual software?
5.
Decide whether introduction of AI will increase the number of steps or decrease the number of steps for a user?
Sometimes the value of what we are providing is very high, so even if we are increasing the number of steps, it’s ok.
6.
Think about the barriers that exist for users to use it (like: privacy, not personal, personalization for a certain demographic, cultures, etc)
7.
Think about hiring a person from humanities background - to build for people.
8.
You are always launching something new in AI (because the field is new), the incremental launches need to be cohesive.
9.
Always keep a “roll-back procedure” ready and a letter from the business' spokes-person also, in case there are potential disruptions because of the new experiments.
10.
You'll need a context for your AI features/products and it’s the hardest things for your software application to learn.
Spend a little bit of time writing down contexts and also write plans and flows to deal with each of them.
11.
What if your Machine learning engineer leaves in the middle of your startup? Can you afford to hire another one? Most universities in India have now started incorporating ML and AI in their Computer Science courses. Be sure that the demand of Engineers will be met!
12.
How should you price your AI applications?
You can pick various kinds of models. May be you will not charge for the software at all and only charge for the AI part of it or may be a combination of both or may be operate a completely different business model (such as the value of data as business model).
13.
How are you going to communicate toyour users about your new AI based feature(s)?
You don’t necessarily have to say it (Google and Amazon have used this for years. Amazon slowly introduced Alexa as a harmless addon in the beginning. It didn’t make a big deal about it, although Alexa might be Amazon’s biggest strategy in the coming years.)
14.
What are the key KPIs based on which you will evaluate the success of your AI?
How will you track the same?
What are the industry benchmarks?
Source:
Chatbotsmagazine.com/product-management-for-ai-startups-d738aebb8430
