🔐 TPM Microguide: Lessons On Running Effective AI Projects + Project Plan Template

A short guide to help you take the first few steps toward leading and launching your AI initiative, project or application as TPM.

Hi, I am Aadil and I write this newsletter on the art of doing technical program management.

You are reading a “TPM Microguide” designed for my paid subscribers. These are concise, actionable guidebooks designed to help TPMs level up in specific areas of their craft — whether it’s mastering technical concepts, building program structures, or learning how Apple builds software. If you want full access, please consider becoming a paid subscriber.

My aim is to publish a new guide every month.

If you have Feedback and/or Suggestions to improve this guide, please leave a comment. Only paid subscribers who have access to the guide can leave comments.

OR reach out to me for any questions - aadilmaan at gmail.com.

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Purpose of This Microguide

This microguide is designed to help TPMs who are either leading or unexpectedly tasked with running AI initiatives at their company. Drawing from my own experience and research, my goal is to give you a head start so you’re not stuck staring at a blank page wondering where to begin. I have also included a project plan template for you to use as a starter.

How to Use This Microguide

Every section is written in a modular way so feel free to copy, remix, or adapt anything here including the proposed project plan template.

How I Write These Microguide

All microguides are built on a combination of in-depth research and my 15+ years of experience leading complex programs in the tech industry. I take the time to deep dive into the core pillars of each topic, aligning research with real-world insights from the industry where ever possible.

Rather than focusing solely on academic theory, I reframe the research to highlight the most practical and relevant aspects for Technical Program Managers (TPMs). The result is content rooted in reality, offering empirical insights that help TPMs navigate challenges with a practical, execution-focused approach.

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What Shipping AI Products Has Taught Me

Over the past four years, I’ve had the chance to help ship multiple AI-powered user experiences, software and hardware. AI projects come with a lot of hype, a lot of unknowns, and even more pressure.

The best thing you can do as a TPM? Ground the chaos in clarity.

How? Focus on milestones and goal posts.

This guide shares lessons I’ve learned leading AI initiatives so you don’t have to learn them the hard way. Whether you’re starting from scratch or joining midstream, use this as a blueprint to avoid the common pitfalls and lead with confidence.

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Lessons Learned Building AI Experiences

Beta and Pilot Testing Is Critical

Modern AI applications are messy. They have both deterministic logic (rules, buttons, flows) and non-deterministic behaviors (model outputs, hallucinations, strange edge cases). Traditional QA won’t catch everything.

Invest in robust pilot programs and beta groups; this will be your ideal early warning feedback loop. These test groups help uncover:

• UX friction you won’t see in regression testing

• Model outputs that feel “off” but are hard to quantify

• Gaps between what the system can do and what users think it should do

Treat beta testing as a first-class part of your program, not a checkbox before launch.

Ramp Up Slowly and Steadily

Just because your AI feature can go live doesn’t mean it should go live for everyone at once. Use ramp rollouts and progressive deployment strategies.

Start small, maybe release to 1–5% of real users. Monitor your goal metrics. Expand only if things are working as expected. Rollbacks are painful, but small ones are survivable. Full population failures? Not so much.

AI features especially ones with generative components tend to behave differently in the wild. Ramping slowly is your insurance policy and just sound product decision making.

Embed Privacy and Legal From Day One

Don’t wait to bring privacy, compliance, and legal teams into the conversation. Large Language Models (LLMs) and AI apps are inherently data-hungry, and much of that data may be sensitive or user-generated. Waiting until the end to run a privacy review often results in launch delays or painful re-architecture.

Build early alignment on:

• Data collection practices

• Data retention and deletion

• Model inputs and outputs

• Regional compliance needs (GDPR, CCPA, HIPAA, etc.)

Privacy isn’t just about risk avoidance but rather it’s about building trustworthy products. Your brand value depends on it.