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Nearly all software projects are premised on understanding user needs and requirements. In our experience with clients, we typically address this phase by working with prospective users to develop use cases. What are some common use cases? Well, it depends on the company, but we’ve seen use cases primarily fall into four categories for early-stage, toe-in-the-water AI compared to full-scale ML efforts:
Market and consumer intelligence
Sales and marketing
Pricing and optimization
Two challenges have plagued corporate America wholistically and the technology industry specifically: the presence of workforce inequities experienced by people of color and the lack of meaningful and sustainable actions addressing these inequities. Statistics representing the number of women of color, Black, and Latinx professionals working in the technology industry overall, not to mention those holding leadership positions, remain in the single digits.
For many decision makers in government and industry today, practicing robust risk management is still seen within their organizations’ social context as providing little if any positive upside, but instead possessing potentially large downside consequences for them personally and professionally. Many decision makers skeptically view rigorous risk analysis as akin to future blame analysis for something that might go wrong rather than a way to increase career or organization success.