The drug development lifecycle is long and fraught with heavy risk -- it takes a staggering 10 – 15 years on average, with ultimately only 12 percent of drugs in clinical trials gaining approval by the U.S. Food and Drug Administration (FDA) [1]. To put this in perspective, 22.7 percent of all global research and development spending in 2017 was in the healthcare industry, second only to 23.1 percent spent in the computing and electronics industry, yet the product lifecycle is longer, and costs are much higher [2]. For example, the original iPhone took two and a half years to develop from concept to launch, and an estimated $150 million spent in research and development [3]. In contrast, the average cost of new drug and biologics is $2.87 billion when factoring in the post-approval research and development costs, according to figures released in May 2016 by The Tufts Center for the Study of Drug development (CSDD) [4]. For pharmaceutical companies that have launched more than four drugs, the median cost is closer to a staggering $5.3 billion according to analysis by industry expert Matthew Herper of Forbes [5].

Incorporating AI and machine learning technologies into everyday workflows isn't as easy as the testimonials would have you believe. That's the top-level finding from a survey of 750 business decision makers conducted by Algorithmia, which found that while machine learning maturity in the enterprise is generally increasing, the majority of companies (50%) spend between 8 and 90 days deploying a single machine learning model (with 18% taking longer than 90 days). Most peg the blame on failure to scale (33%), followed by model reproducibility challenges (32%) and lack of executive buy-in (26%). "The findings of our 2020 [State of Enterprise Machine Learning] study are consistent with what we're hearing from customers," said Algorithmia CEO Diego Oppenheimer. "Companies are growing their investments in machine learning, and machine learning operationalization is maturing across all industries, but significant room for growth and improvement remains. The model deployment lifecycle needs to continue to be more efficient and seamless for ML teams. Nevertheless, companies with established ML deployment lifecycles are benefiting from measurable results, including cost reductions, fraud detection, and customer satisfaction. We expect these trends to continue as ML technologies and processes arrive to market and are adopted."

Incorporating AI and machine learning technologies into everyday workflows isn't as easy as the testimonials would have you believe. That's the top-level finding from a survey of 750 business decision makers conducted by Algorithmia, which found that while machine learning maturity in the enterprise is generally increasing, the majority of companies (50%) spend between 8 and 90 days deploying a single machine learning model (with 18% taking longer than 90 days). Most peg the blame on failure to scale (33%), followed by model reproducibility challenges (32%) and lack of executive buy-in (26%). "The findings of our 2020 [State of Enterprise Machine Learning] study are consistent with what we're hearing from customers," said Algorithmia CEO Diego Oppenheimer. "Companies are growing their investments in machine learning, and machine learning operationalization is maturing across all industries, but significant room for growth and improvement remains. The model deployment lifecycle needs to continue to be more efficient and seamless for ML teams. Nevertheless, companies with established ML deployment lifecycles are benefiting from measurable results, including cost reductions, fraud detection, and customer satisfaction. We expect these trends to continue as ML technologies and processes arrive to market and are adopted."

The drug development lifecycle is long and fraught with heavy risk -- it takes a staggering 10 – 15 years on average, with ultimately only 12 percent of drugs in clinical trials gaining approval by the U.S. Food and Drug Administration (FDA) [1]. To put this in perspective, 22.7 percent of all global research and development spending in 2017 was in the healthcare industry, second only to 23.1 percent spent in the computing and electronics industry, yet the product lifecycle and cost are much higher [2]. For example, the original iPhone took two and a half years to develop from concept to launch, and an estimated $150 million spent in research and development [3]. In contrast, the average cost of new drug and biologics is $2.87 billion when factoring in the post-approval research and development costs, according to figures released in May 2016 by The Tufts Center for the Study of Drug development (CSDD) [4]. For pharmaceutical companies that have launched more than four drugs, the median cost is closer to a staggering $5.3 billion according to analysis by industry expert Matthew Herper of Forbes [5].