Gamma · AI product · Senior · 45 to 60 minutes

Should Gamma Rebuild Around AI Now?

Gamma is a software product for creating and sharing polished content in a format that sits between a presentation, a document, and a webpage. Answer this product management case and get it graded line by line against a weighted rubric.

Product strategy
AI product judgment
Prioritization
Metrics design
Risk management

The situation

Gamma has found user interest with a content creation product that sits between a presentation, a document, and a webpage. Now generative AI is changing how people expect to create, edit, and share content. The co founder and CPO asks you, a senior PM, for a recommendation on whether Gamma should make AI the center of the product strategy or treat it as a set of supporting features. Assume Gamma can fund one major product bet over the next year, but not several in parallel. Moving too slowly could make the product feel dated, but moving too aggressively could confuse current users, weaken the core format, and create quality and trust issues. You need to advise on the strategic direction, what user problem to prioritize, and how to sequence the work so the company does not spread itself too thin while the technology and competitive landscape are moving fast.

What to produce

A product strategy memo of about two pages.

Your answer must

State a clear recommendation on whether AI should be a company level product bet now, and define the main user problem or job to be done it serves.
Compare at least three strategic paths, including a lighter assistive AI path and a deeper AI native path, with tradeoffs on differentiation, speed, risk, and focus.
Propose a phased plan for the near term and the following year, including what to build first, what to delay, and what learning each phase should produce.
Define success metrics with both leading indicators and outcome measures, and explain how you would distinguish durable value from short term novelty.
Identify the biggest risks and second order effects on current users, product quality, trust, positioning, and team focus, with mitigations or decision points.

Constraints

Work only from the information in this prompt, do not assume undisclosed traction, partnerships, or proprietary model advantages.
Your recommendation must be viable for an existing live product with current users, not a blank slate startup.
The plan should fit one major company bet over the next year and acknowledge that model quality, cost, and user expectations are changing quickly.

Format

Use headings for recommendation, user problem, options, sequencing, metrics, and risks.

How Gamma actually played it

Gamma's co-founder and CPO talks through the product's new format between presentation, document and webpage and how the company moved to an AI-native product used by over 100 million people. Rich detail on an AI pivot from a product lead.

A new format can give early propulsion
Rebuilding around AI needs a clear product bet

Built from How Gamma Pulled Off Their AI Pivot, with Jon Noronha by First Round Review.