(2024-05-01) Gilad What Does Generative AI Mean For Product Development?

Itamar Gilad: What Does Generative AI Mean For Product Development?

Ways to use Generative AI

1. Generating Entire Products

Poof! out comes a fully designed and coded feature or product.

I would hazard a guess that for most real-world dev projects, full-automation is years away,

2. Co-Piloting Development

Coding/code-reviewing/debugging

Creating dev artifacts: specs, designs, test plans, product content, interview scripts, experiment plans…

Processing and analyzing data: cleaning, summarizing, reporting, finding insights…

Ideating: generating ideas, proposing goals, suggesting approaches

Acting as a knowledge-base: product data, frameworks, processes, templates, book summaries…

Testing code: both test development and running the tests

This is what gets practitioners most excited

3. Powering Your Product

The Risks of Generative AI in Product Development

Missing Context

I wrote a whole post on this one ((2024-04-26) Gilad On GenML Artifacts And Product Management). The bottom line is that although the bot can produce a compelling user story, OKR, or business model, these are not necessarily the right ones for you. The model is missing a lot of context about your specific product, users, market, and company

the bot will ultimately output a generic artifact (a Not-Thinking Tool)

humans suffer from context problems too.

Even smart and capable people need help thinking broadly and deeply.

This may be a big opportunity for AI.

Equipped with the right model, this system could help us detect patterns and uncover insights, opportunities, and threats.

I’m sure someone somewhere is working on such a product right now, although this too may turn out to be another tough problem, partly because of the next issue.

Hallucinations

A major downside of the current generation of ML models is their tendency to confidently spew out nonsense

New Costs and Risks

If you embed Generative AI into your software, things get even less deterministic

Another cost is that of using a 3rd-party Generative AI API.

As Marty Cagan and Marily Nika point out, Gen-AI introduces all four classes of product risk: feasibility, usability, value, and business viability. (2024-04-24 CaganNikaAiProductManagement)

Accelerating Feature Factories and Junk Features (feature factory)

This one worries me the most. It’s no secret that many company leaders measure the success of their product orgs by their output

Gen-AI-Utopia or Techno-Hell?

we may use Gen-AI like some companies use Agile (dark agile): “optimize” engineering, design, and product work for maximum throughput creating a “production machine” that is increasingly devoid of deep thinking and judgement.