Solve your most common business challenges with machine learning
Machine learning (ML) has moved beyond the hype to become a meaningful driver of value for many organizations. Over two-thirds of businesses that have fully embraced artificial intelligence (AI) say the technology has created a better customer experience, and more than half say it has improved decision-making, increased productivity, and allowed for innovation while achieving cost savings.
While it’s clear that ML is an essential part of business transformation, many organizations struggle to understand where to apply ML for the most impact. Selecting the right ML use case requires you to consider a number of factors.
First, you need to find a balance between optimal business value and speed. A proof of concept built by a siloed data scientist is not likely to generate much enthusiasm for ML in an organization. What is more apt to attract the needed commitment and funding is showing how ML can address the practical issues your organization currently faces. Furthermore, you’ll want to find something that can be accomplished in 6–8 months so that you won’t lose momentum. This is especially true if this is your first foray into ML.
Second, you’ll want to find a use case that is rich in data that you already have. A good business use case with no data will lead to frustrated data scientists.
Lastly, you’ll want to evaluate whether your business problem actually requires ML for success and whether ML will result in better outcomes than your traditional approach. These outcomes might be realized as cost reduction, increased employee productivity, or an improved experience for your customers.
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7 leading machine learning use cases