Everyone is talking about AI, but where do you actually start?
The pressure on product leaders to "just add AI" can lead to costly mistakes and dead-end projects.
In a trilogy on AI for product leaders, we start with the most important thing: use cases. AI is not a magic solution; it's a new tool for solving existing customer needs.
Where should you start?
Start by mapping your current use cases. What value do you deliver to customers today? AI doesn't change these needs, but it can change how you solve them. A company's use cases typically fall into two categories:
Automation: Helping users complete tasks faster (e.g., time savings).
Quality and Decision-Making: Ensuring quality or providing the user with data to make better decisions.
The Journey to AI
AI implementation is a maturity journey. Start simple and iterate. A global retailer began with a simple analysis of where products sold best and ended up with an advanced solution for hyperlocal marketing based on store data. Product methods like validation and user feedback are still essential.
It is crucial to analyze existing use cases rather than starting "AI initiatives" without a clear purpose. This ensures faster results and better anchoring in the organization.
Make room for experiments
In addition to focusing on existing needs, it's important to give teams the freedom to experiment. The "crazy" ideas can turn out to be the biggest products of the future. AI development is unpredictable and requires short feedback loops.
This was an overview of how to get started with AI by focusing on use cases. In the next episode, we will discuss "build vs. buy" and the implementation itself.