What are the challenges in implementing an ML feature in a product? who takes the decision to determine what type of ML feature would help the product and how to solve the problem related to it? How closely AI PM works with Data scientists?

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3 Answers

AI Product Management

Deepak Mukunthu

Salesforce Senior Director of Product, Agentforce AI Platform • 4y

While infusing ML into a product can be extremely powerful to light-up new scenarios for customers and to optimize current scenarios, it can take a lot of effort to get it right. It all starts with defining and prioritizing the right scenario/problem to focus on, working with data science and ML engineering teams to map the business problem to an ML problem, trying out to different approaches to identify the right one to go ahead with, experimenting and optimizing and then operationalizing the a ...Read More

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Rapha Danilo

Gong GM / Sr Director of Product • 3y

The first challenge is actually self-imposed by product teams. Ask yourself: are we implementing an ML feature mostly because of FOMO / not wanting to "fall behind", or because of a first-principled, customer-first assessment of need/opportunity? Assuming we've identified a real need/opportunity for ML in our product, we can ask what type of ML feature best solve the job to be done for our customers. IMO this is the PM's responsibility to own. It's important to involve and partner with R&D a ...Read More

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Savita Kini

Cisco Director of Product Management, Speech and Video AI • 3y

Implementing an ML feature vs a non ML feature is no different. The complexity is how the ML model is integrated into the existing product, business or user workflows, and how it might impact the overall end user experience. The goal is usually to simplify and not complicate things. The goal is to reduce buttons and not add buttons. The decision for ML features in a product is similar to the other features -- ML is just the technology about how - under the hood. It is not the end in itself. As ...Read More

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