As companies across all industries realize the value of implementing a data-driven strategy, machine learning (ML) is emerging as a transformative force to be reckoned with. While implementing machine learning initiatives at the core of their digital transformation strategy, they are measuring the complexity of bringing them to fruition.
Navigating Complexity and Avoiding Project Pitfalls
Analysts agree that the failure rate of machine learning projects is around 80%. Indeed, Gartner predicted that by the end of 2022, approximately 85 percent of AI projects would have delivered erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them. IDC's research indicates that while AI/ML adoption is on the rise, cost, lack of expertise, and lack of life-cycle management tools are among the top three inhibitors to realizing AI and ML at scale.