Machine learning (ML) is transforming the way businesses operate and creating value for organizations. With more than two-thirds of businesses that have fully embraced artificial intelligence (AI) saying that it has improved customer experience, decision-making, and productivity, it’s clear that ML has become a vital part of business transformation. However, selecting the right ML use case can be a daunting task for many organizations.
To make the most out of ML, businesses must consider several factors. First and foremost, businesses must identify a use case that provides optimal business value and speed. Proof of concepts created by a siloed data scientist may not generate much enthusiasm for ML within an organization. Instead, it’s better to demonstrate how ML can address practical issues currently faced by the organization. Additionally, businesses should choose a use case that can be accomplished in 6-8 months to maintain momentum.
Secondly, businesses must select a use case that is rich in data. A good business use case without data can lead to frustrated data scientists. Lastly, businesses must evaluate whether their business problem requires ML for success and whether it will result in better outcomes than their traditional approach. These outcomes may be realized as cost reduction, increased employee productivity, or an improved customer experience.
To ensure that businesses satisfy these criteria, technical experts and domain experts should work hand in hand on their ML project. Technical experts can conduct feasibility assessments, while domain experts can ensure that the solution is solving a real business problem and will have a real impact.
In conclusion, selecting the right ML use case is critical for businesses seeking to transform their operations. It’s essential to balance optimal business value and speed, choose a use case that is rich in data, and evaluate whether the business problem requires ML for success. Collaboration between technical and domain experts is also crucial to the success of ML projects. By considering these factors, businesses can make the most out of ML and leverage its capabilities to drive value and growth.
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