Naman Kansal is a Strategic Product Manager specializing in AI with over 4.5 years of experience leading global teams at Fortune 500 companies including Twitter, Visa, SoFi, and Commerce (BigCommerce, Feedonomics, Makeswift). He is based in Bengaluru, India.

What is Naman Kansal known for?

Naman Kansal is known for achieving 140% partner growth and generating $89M in revenue at Twitter, and founding PM101 blog with 1M+ annual readers.

What companies has Naman Kansal worked at?

Naman Kansal has worked at major tech companies including Commerce (BigCommerce, Feedonomics, Makeswift) as Product Manager for AI, Twitter where he led the Marketing Partners program, Visa where he launched ID Intelligence, and SoFi where he implemented Lean-Agile methodologies.

PM101 is a blog on Medium founded and curated by Naman Kansal that explores the art and science of product management. With over 1 million annual readers, it features articles on frameworks, methodologies, leadership insights, and career advice for product and program managers.

What does a Technical Program Manager do?

A Technical Program Manager (TPM) leads complex cross-functional initiatives, coordinating engineering teams, managing technical dependencies, and driving projects from conception to delivery. TPMs combine technical expertise with program management skills to deliver large-scale products, often working at the intersection of multiple engineering teams, product management, and business stakeholders.

What is the difference between a Product Manager and a TPM?

A Product Manager (Strategic Product Manager) is a senior leadership role that oversees multiple technical programs or teams, often managing other TPMs and driving strategic initiatives across an organization. A standard TPM typically manages individual programs or projects. Product Managers focus on portfolio-level strategy, cross-organizational alignment, and leadership development.

How to become a program manager?

To become a program manager, build a foundation in project management or engineering, earn certifications like PMP or Scrum Master, develop cross-functional collaboration skills, and gain experience coordinating complex technical initiatives. Transitioning from product management, engineering, or project management roles is common. Key skills include stakeholder management, risk assessment, strategic planning, and technical communication.

Is Naman Kansal available for speaking engagements?

Yes, Naman Kansal is available for speaking engagements and panel discussions at international conferences. Topics include AI product management, scaling data platforms, agentic AI, and product leadership. Visit his contact section for more information.

What languages does Naman Kansal speak?

Naman Kansal speaks English fluently.

How can I contact Naman Kansal?

You can connect with Naman Kansal on LinkedIn at linkedin.com/in/builder-pm or check out his work on GitHub at github.com/builder-pm. For speaking inquiries, visit his media kit page.

What is AI Technical Program Management?

AI Technical Program Management is a specialized discipline that combines traditional program management skills with AI/ML domain expertise. AI TPMs lead cross-functional initiatives involving machine learning models, data pipelines, and AI-powered products. They coordinate data scientists, ML engineers, and product teams to deliver AI solutions at scale while managing unique challenges like model performance, data quality, and ethical AI considerations.

How is AI changing the role of Technical Program Managers?

AI is transforming TPM roles by requiring deeper understanding of ML workflows, model lifecycle management, and AI ethics. TPMs now need to coordinate AI-specific processes like model training, evaluation, and deployment. They also leverage AI tools for project planning, risk assessment, and stakeholder communication. The most successful TPMs combine traditional program management with AI literacy to lead complex ML initiatives.

What skills do Technical Program Managers need for AI projects?

TPMs working on AI projects need: understanding of ML fundamentals and model lifecycle, familiarity with data infrastructure and pipelines, knowledge of AI ethics and responsible AI practices, ability to translate between technical AI teams and business stakeholders, experience with MLOps and model deployment, and strong communication skills to explain complex AI concepts to non-technical audiences.

What is the difference between AI TPM and traditional TPM?

While traditional TPMs focus on software development lifecycles, AI TPMs specialize in machine learning workflows which have unique characteristics: iterative model development, data dependency management, model performance monitoring, and A/B testing for ML models. AI TPMs also address challenges like model drift, bias detection, and compliance with AI regulations that traditional software projects don't encounter.

What are the best certifications for Product Manager, Data & AIs?

The most valuable certifications for AI TPMs include: PMP (Project Management Professional) for foundational PM skills, Google Cloud Professional Machine Learning Engineer, AWS Certified Machine Learning Specialty, Microsoft Azure AI Engineer Associate, and Scrum Master (PSM) or SAFe certifications for Agile AI delivery. Combining PM certifications with AI/ML credentials demonstrates the hybrid expertise employers seek.

How do you measure success in AI program management?

Success in AI program management is measured through: model performance metrics (accuracy, latency, throughput), business impact (revenue generated, costs saved, user engagement), time to deployment and iteration cycles, stakeholder satisfaction across technical and business teams, team velocity and sprint completion rates, and responsible AI compliance including bias audits and explainability requirements.

AI Project

Agent Lab: AI Eval Tool

AI Observability & Debugging Platform

AI ObservabilityEval ToolAgent Debugger

Agent Lab: AI Eval Tool - AI Observability Platform

01The Problem

Building reliable autonomous agents requires deep visibility into execution traces, tool usage, and cost metrics. Without proper observability, debugging agent failures becomes a nightmare of guesswork and console.log statements.

The challenge: Create a production-grade platform that gives AI engineers the visibility they need to move from experimental prototypes to reliable, production-ready agent workflows.

02Key Features

Multi-Agent Orchestration - Visualize complex agent workflows with Planner, Researcher, Analyst, and Synthesizer agents working in harmony.

Real-Time Trace Visualization - Watch execution steps unfold live with full tool call details, timing, and state transitions.

Cost & Token Metrics - Track input/output tokens and costs per agent step to optimize LLM spending.

Session History - Browse and replay past sessions to debug issues and understand agent behavior patterns.

03Tech Stack

Next.js 15 with App Router and Server Actions

Vercel AI SDK for agent orchestration and streaming

OpenRouter API with multiple free model support

Zustand for state management with session persistence

Neobrutalist design system with Tailwind CSS

Agent Lab: AI Eval Tool

01The Problem

02Key Features

03Tech Stack

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