Having general product management methods won't cut it in AI and data projects anymore. These projects are full of uncertainty, high complexity, and constant learning. It's high time we work smarter and adapt techniques designed specifically for AI and data-driven work.

In this edition, I’m sharing some practical methods to shift your mindset and help you deliver better outcomes in AI projects.

PMs Must Speak AI

We need to be clear about one fact - PMs don’t need to become data scientists, but must grasp fundamentals.

Let's start with basics:

• Data Analytics = What’s happening
• Data Science = How to build intelligent systems
• Know basics of ML: Models trained on data to find patterns and make predictions.
• Deep Learning is a more advanced ML technique inspired by the human brain.

ML vs Deep Learning – How to Decide

• ML works well with smaller datasets and requires human-picked features.
• DL needs large data, more hardware, but no manual feature selection.
• ML is faster, cheaper, easier to interpret.
• DL is great for image, speech, and complex pattern recognition.

When to Use AI in Business

Use AI when:

• Task is repetitive but complex to define
• Labeled data is available
• Human can do it, but AI can do it faster

AI use cases include:

• Enhancing current product features
• Fixing operational inefficiencies
• Generating business insights
• Creating new AI-first products

The PM's Tasks in AI Projects

• Define the right problems
• Ensure training data is available and clean
• Choose the right learning approach
• Work closely with engineers and data scientists
• Set success metrics: accuracy, precision, recall
• Validate with users

Prioritizing & Shipping AI Features

• Use AI Prioritization Matrix: Business impact vs Technical complexity
• Aim for Minimum Viable Data (MVD) before full-scale model

Delivery framework:

• Use Kanban, not Scrum
• Better for uncertainty and iterative delivery

AI is powerful — but without the right product strategy, it's just noise. If you're a PM, start speaking the language of AI. If you're in AI, bring in product thinking.

Keep learning. Keep growing.