A few years ago a company launched an intelligent recommendation system for its customer service agents to suggest solutions to customer problems.
The project was viewed as a high-profile cost-saver for the company.
It took nine months to figure out that the agents weren’t using it,…
And it took another six months to figure out why.
The recommendations the system was offering weren’t relevant, but the problem wasn’t in the machine learning algorithms.
Instead, the company had relied on training data based on technical descriptions of customer problems rather than how customers would describe them in their own words
According to a recent IDC survey, only about 30% of companies reported a 90% success rate for AI projects.
A key determinant of success of ML projects is whether data scientists work with stakeholders and SMEs to incorporate business context.
Keen on becoming a data scientist?
One of the key skills you can pick up is critical thinking.
- Objectively analyze questions, hypotheses, and results
- Understand what resources are critical to solve a problem
- Look at problems from differing views and perspectives
- Align on what an actionable outcome and success looks like to the end user
Source for case study here
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