5 AI PM Projects to Get Hired in 2026 | Ex-Amazon

Summary

The video argues that PM portfolios in 2026 should demonstrate product execution, not just analytical writing. Generic case studies, product teardowns, and lengthy slide decks are unlikely to distinguish candidates. A stronger portfolio shows that the candidate identified a real user problem, built and tested a solution, learned from failures, and connected the work to user or business outcomes.

Portfolio project checklist

Each project should show:

• A real user problem: Who has the problem, what they do today, and how an AI solution could help.
• The AI workflow: Inputs, processing, outputs, and where a human validates or intervenes.
• Evaluation: How the candidate judges whether the AI’s output is good.
• Failure modes: Where the system can break or hallucinate.
• Safety and fallbacks: What happens when the system fails and how users are protected.
• Business or user metrics: What the product saves, improves, earns, or enables.
• Evidence of iteration: What users tried, what failed, and what changed as a result.

The recommended portfolio package is a working demo, a Loom video, an evaluation scorecard, and real user feedback—not just a PPT or PDF.

Five suggested AI PM projects

1. AI interview coach

   • Let users upload a resume, add a job description, choose a target role, and practice a mock interview.
   • The AI can ask role-specific questions, follow up, score answers, provide feedback, and suggest improved responses. A voice-based version is a more advanced option.
   • Define evaluation dimensions such as relevance, structure, specificity, business impact, and clarity. Test for confident but incorrect feedback.

2. Voice-of-the-customer copilot

   • Combine messy customer feedback—such as app reviews, support requests, sales calls, interviews, or screenshots—into themes, pain points, supporting quotes, and roadmap recommendations.
   • Keep a traceable source for each finding so users can verify what the AI’s conclusions are based on.
   • For example, show that 12 of 50 reviews mention confusion during setup, with five users specifically saying they could not complete the first setup step.
   • A stronger version updates findings automatically each day. Test it with founders or PM peers to see whether it helps them prioritize.

3. Rebuild or improve a feature in an existing product

   • Choose a real workflow and build an AI-enabled alternative, then show a clear before-and-after.
   • Examples include natural-language food search—such as “something light under ₹250 that takes less than 20 minutes”—or shopping search for a cotton dress under ₹700 with strong reviews.
   • Possible formats include a live web prototype, an AI-connected Figma design, or a Chrome extension. The goal is to make the improvement clickable and testable.

4. Meeting-to-execution tool

   • Turn a rough meeting transcript into action items with owners and deadlines, open questions, and a ready-to-use follow-up email.
   • More advanced versions could capture meetings through a Chrome extension or local app and connect to Jira or documentation tools.
   • Evaluate whether the system catches execution gaps—for example, identifying that seven action items were discussed but only three have owners, or that a launch date was mentioned but never agreed upon.

5. Internal knowledge assistant

   • Build an assistant that answers questions from company materials such as support, HR, product, and sales documentation.
   • Start with 10–20 uploaded documents; a more advanced version connects to sources such as Google Drive, Slack, Jira, and Notion.
   • Evaluate answer quality and include safeguards for citations, freshness of information, and permissions. This project can help demonstrate how practical enterprise AI products are designed.

Execution playbook and recommendations

• Use AI-assisted development tools such as Claude, Cursor, Lovable, and Bolt to create prototypes. The aim is not to prove senior engineering ability, but to show product judgment and execution.
• Put projects in front of real users instead of leaving them as concepts. Suggested validation examples include testing an interview coach with five job applicants and asking two founders to pilot a customer-feedback tool.
• Record what users liked, where they were confused, which AI outputs they did not trust, and what was changed afterward.
• If possible, get a user to pay for the product; payment provides evidence that the solution offers practical value.
• The speaker recommends doing two projects well rather than producing many untested ideas, and suggests putting the voice-of-the-customer project prominently on the portfolio page.

Metrics and claims mentioned

• The presenter says he reviewed 1,000+ PM resumes and interviewed 100+ PM candidates over four years.
• The customer-feedback example uses 12 of 50 reviews and five users as illustrative evidence, not as results from a reported case study.
• A daily automated refresh is suggested for the customer-feedback tool.
• The speaker claims that doing two projects well could put a candidate ahead of 99% of competing PM applicants. This is his assertion, not a measured benchmark.
• No revenue, CAC, LTV, margin, or other commercial performance results are reported.

Presenter/source: Neel Sikdar (he introduces himself as “Neil” in the subtitles), described in the video as a product manager at an AI startup who managed a team of six PMs. The video title identifies him as ex-Amazon.