S P O N S O R P E R S P E C T I V E
Artificial intelligence (AI) and machine learning (ML) have the potential
to transform nearly every industry, but many organizations strule to
adopt and implement AI/ML at scale. Recent Gartner research shows
that only 53% of ML projects make it from prototype to production.
Chief information officers and IT leaders find it hard to scale AI/ML
projects because they lack the tools and talent to create and manage a
production-grade AI pipeline.
Data is often cited as the number one challenge. The other common
barriers we see today are business- and culture-related. For instance,
organizations often strule to identify the right use cases to start their
ML journey, which is often exacerbated by a shortage of skilled talent to
execute on an organization's ML ambitions. Business and technical leaders
play a critical role in addressing these challenges by driving a culture of
continuous learning and innovation; however, many lack the resources to
develop their own knowledge of ML and its use cases. According to "The
State of AI in 2021," McKinsey & Co.'s global survey, there are certain best
practices that differentiate AI/ML high performers from those that strule
to see the full value of AI/ML. On the technical front, the companies
seeing the biest bottom-line impact from AI adoption are more likely to
follow both core and advanced AI best practices, including ML operations,
move their AI work to the cloud, and spend on AI more efficiently.
In my role as leader of the Amazon Machine Learning Solutions Lab,
I head a global team that helps AWS customers identify and implement
their most important ML opportunities. I've been fortunate to work with
some of the most innovative organizations in the world, such as the
National Football League, Formula 1, Intel, and United Airlines, as they
transformed their businesses through ML. I've seen the challenges of
ML-led transformation firsthand and helped our customers overcome
them. That's why I'm very excited to present this research from Harvard
Business Review Analytic Services that not only uncovers some of the
common challenges to AI/ML implementation but also offers guidance to
overcome them.
Dr. Priya Ponnapalli
Senior Manager, Applied Science
Amazon Machine Learning
Solutions Lab
