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Which data and labels are relevant to your audience? Which patterns can we expect to find in it?
Which critical aspects of your audience does your team not see? What harm can not seeing these aspects cause?
AI can’t do everything by itself, it still needs that human touch, so what does that touch look like for your audience?
How do we help the audience trust the system? How do we make it feel safe for them?
Developing great AI is hard. You need to have the right data, analyze the data in the right way, and, with a good dose of luck, extract something meaningful and practical from it.
So why not make it yourself easier, and prioritize value delivery by understanding your audience? This directs the development process towards the essential elements from the outset. This clarity enables better goal-setting, streamlines decision-making, and prevents wasteful resource allocation.