Product and Risk Management

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Academic science mindset, is business product mindset

• Scientific inquiry maps to product management. Central Role of Product Design Managers: Deciding what to do next.

Scientific Method	Lean Product Development	General Product Development
Observe	Build	Where do we want to go (Vision)
Hypothesise	Measure	Where are we now (Data/Analysis)
Test	Learn	Where should we go next (Strategy)

Risk: Politics, Law, Privacy

• When I started my formal postgraduate machine learning training risks were couched in biases, now this is Safety and alignment, with the UK positioning itself as a global leader. This likely does have impact on your business goals.

Mitigate GenAI risks through product management. Maybe don’t just be grabby for AI.

• Question 1: Where Do We Want to Go?

  • Vision and Goal Setting: Defining a clear destination or North Star.

    • My northstar (as an example) is

    Equity of opportunity of access to AI, to support a fairer world.

  • **Where Are We Coming From?

• Product market analysis** : In your business this is more likely to be a function of your product methodolgy

  • I am coming from a position of understanding collaboration in groups, across technology, where some members of the group are likely to be AI.
  • Where Should We Go Next?

• Strategic Planning: Deciding the next steps based on vision and current status.

  • For me, this mean helping build B2B capabilities,
  • Education, and interweaving of people and AI through storytelling,
  • Distributed, global, AI enabled infrastructure,
  • Clearly communicating why,
  • Building communities to help.

• This won’t be you and your company, but this is a great time to work out these checklists.

  • SWOT Analysis: Evaluating strengths, weaknesses, opportunities, and threats.
  • I am trying to build this bit of the business journey through these presentations

  

Everyone’s pivoting to generative AI.

https://twitter.com/petergyang/status/1667539634577092609

• Link to Tweet
• But my alarm bells go off when I see:
  • ? A crowded landscape
  • ? FOMO driven decision making
  • ? Sky high valuations for an early space
• If you took the word “AI” out, is the product still solving a customer problem?
• AI is a solution, not a problem. Ask yourself:
  • What is the pain point?
  • How many users share this pain?
  • Is the pain big enough to take action?
  • Is the pain underserved by non-AI tools?
  • How accurate does the solution need to be?
• Plot the problem on a fluency vs. accuracy grid.
  • Gen AI today is great for high fluency + low accuracy problems (e.g., productivity).
  • It’s not great for solutions that need high accuracy (e.g., financial decisions).
• How fast will incumbents move?
  • Incumbents like Microsoft, Google, and Adobe have moved incredibly fast on AI.
  • Startups that overlap with core incumbent use cases might struggle.
• AI presentation startups need to be MUCH better than AI in Powerpoint to thrive.
  • Is there a moat? Examples moats include:
• Access to proprietary data and models
  • Exclusive contracts with large customers
  • Great product even without AI
  • Exceptional talent in the selected field
  • Business models that incumbents avoid
  • And of course…speed of execution.
• Is it overvalued?
  • If an AI product already has $100M+ valuation, you should think:
  • Can it continue to grow and (more importantly) retain users?
  • In a crowded space like AI copywriting and productivity
• that could get hard.
• To recap, here are 5 questions to ask to evaluate AI products and companies:
  • 1. Without “AI”, is it still solving a problem?
    2. How accurate does the solution need to be?
    3. How fast will incumbents move?
    4. Is there a moat?
    5. Is it overvalued? 7/ I hope these questions also help builders who are thinking of creating new AI products.