In his How to solve the right problem with AI presentation at Future Product Days, Dave Crawford shared insights on how to effectively integrate AI into established products without falling into common traps. Here are my notes from his talk:

Many teams have been given the directive to "go add some AI" to their products. With AI as a technology, it's very easy to fall into the trap of having an AI hammer where every problem looks like an AI nail.
We need to focus on using AI where it's going to give the most value to users. It's not what we can do with AI, it's what makes sense to do with AI.


AI Interaction Patterns
People typically encounter AI through four main interaction types
Discovery AI: Helps people find, connect, and learn information, often taking the place of search
Analytical AI: Analyzes data to provide insights, such as detecting cancer from medical scans
Generative AI: Creates content like images, text, video, and more
Functional AI: Actually gets stuff done by performing actions directly or interacting with other services
AI interaction patterns exist on a context spectrum from high user burden to low user burden
Open Text-Box Chat: Users must provide all context (ChatGPT, Copilot) - high overhead for users
Sidecar Experience: Has some context about what's happening in the rest of the app, but still requires context switching
Embedded: Highly contextual AI that appears directly in the flow of user's work
Background: Agents that perform tasks autonomously without direct user interaction


Principles for AI Product Development

Think Simply: Make something that makes sense and provides clear value. Users need to know what to expect from your AI experience
Think Contextually: Can you make the experience more relevant for people using available context? Customize experiences within the user's workflow
Think Big: AI can do a lot, so start big and work backwards.
Mine, Reason, Infer: Make use of the information people give you.
Think Proactively: What kinds of things can you do for people before they ask?
Think Responsibly: Consider environmental and cost impacts of using AI.
We should focus on delivering value first over delightful experiences

Problems for AI to Solve

Boring tasks that users find tedious
Complex activities users currently offload to other services
Long-winded processes that take too much time
Frustrating experiences that cause user pain
Repetitive tasks that could be automated

Don't solve problems that are already well-solved with simpler solutions
Not all AI needs to be a chat interface. Sometimes traditional UI is better than AI
Users' tolerance and forgiveness of AI is really low. It takes around 8 months for a user to want to try an AI product again after a bad experience
We're now trying to find the right problems to solve rather than finding the right solutions to problems. Build things that solve real problems, not just showcase AI capabilities