Where to introduce AI?

AI should make the software exponentially better and in a positive impact for end-users.

The AI for Product Management Course explores AI’s place in product management—including how to leverage AI throughout the development life cycle, best practices for building AI-powered features, and why product managers should view AI as a strategic tool, not a threat.

What will be AI's place in building products?

1. Data analysis - Automatically collecting, analyzing and recognising patterns in data set. The decision making examples that would help PMs is product discovery, roadmap planning, and product-led growth strategies

2. Experimentation - When data analysis would be automated, product managers will have a lot more time for whats working and implement changes quickly. AI can suggest what to test for multivariate feature testing and even run tests automatically.

Before AI: Product managers spend a ton of time manually analyzing data to understand what's working. Running A/B or multivariate tests often requires collaboration with data scientists, engineers, and analysts.

After AI-Driven Data Analysis: AI removes the bottleneck of manual data crunching and opens the door to automated experimentation. Here's how:

• Automated Data Analysis AI monitors product metrics in real time, flagging trends or anomalies instantly—no more waiting for reports.

• Smart Multivariate Test Suggestions AI identifies multiple influencing variables and suggests test combinations (e.g. button color + CTA text + layout) for more impactful experiments.

• Automated Test Setup & Execution It handles test design, user segmentation, and ongoing optimization, running multivariate tests autonomously.

• Real-Time Insights & Actions AI delivers immediate results and recommends (or auto-applies) winning variants, accelerating decision-making and implementation.

Real-World Impact

• Speed: A test that used to take 4 weeks to plan, run, and analyze now takes days — or is always running in the background.

• Scale: You can test 10+ variables at once instead of one or two.

• Focus: Product managers can now focus on strategy, user empathy, and storytelling, not spreadsheets.

Visualization of Multivariate Testing Flow

plaintextCopyEdit           
            +------------------------+
            |  AI Observes Behavior |
            +----------+------------+
                       |
                       v
            +-----------------------------+
            |  AI Identifies Variables    |
            |  (CTA text, layout, etc.)   |
            +--------------+--------------+
                           |
                           v
            +-----------------------------+
            | Suggests Combinations for   |
            | Multivariate Testing        |
            +--------------+--------------+
                           |
                           v
            +-----------------------------+
            |  Test Setup & Execution     |
            |  (Traffic split, control)   |
            +--------------+--------------+
                           |
                           v
            +-----------------------------+
            |  Real-Time Results Analyzed |
            +--------------+--------------+
                           |
                           v
            +-----------------------------+
            | Best Performing Variant Auto|
            | Implemented or Suggested    |
            +-----------------------------+

1. Communication - Since PMs are always talking about ideas, priorities and plans with so many different stakeholders. What can be automated is user stories, persona description, product requirements documents, release notes and more. Information needed as prompt would be offered by you, while all other communication materials will be optimized by AI (Example - How I created user personas for AspireHer using GPT by giving it clear prompts)

Two areas AI will amplify but not replace:

1. Being customer-centric

2. Having a good-business sense