An International Data Corporation (IDC) forecast has revealed that the spending on Artificial Intelligence and Machine Learning will grow from $1 billion in 2017 to $57.6 billion by 2021. A prediction by Deloitte Global states that the number of machine learning pilots and implementations will double in the year 2020 from 2018.
Now for actual numbers:
- ML patents grew at a 34% CAGR between 2013 and 2017, the third-fastest growing category of all patents granted.
- 61% of organizations most frequently chose ML/AI as their company’s most significant data initiative for next year.
- 60% of organizations who have adopted ML say the technology has facilitated more extensive data analysis and insights.
- Companies that invested in AI, spent about 60% of that investment in ML.
Let’s look at some of the industries that have adopted machine learning and have witnessed a transformation that includes creating world-class services and products, first rate customer service, industry competitiveness, and never before ROI.
The one industry that has been constantly reinventing itself with its use of machine learning is manufacturing. The advent of Industry 4.0 has led the way to “smart manufacturing”, where in global manufacturing companies are transforming their operations by deploying smart technologies. According to a forecast by TrendForce, the size of global market for Smart Manufacturing Solutions will top US $320 billion by 2020, growing at a CAGR of 12.5% from 2017 to 2020.
Machine learning has revolutionized the manufacturing industry in more ways than one – from semiconductor manufacturing yields up to 30%, bringing down scrap rates, and optimizing fab operations, to cutting back supply chain prediction errors by half and making better product availability to curb lost sales by 65%. ML has also made automation of quality testing possible that has improved defect detection rates by up to 90%.
Reduction of energy costs and negative price variations, improvement in service levels, and enhancements in inventory and shop floor optimization are just some of the new found strengths that ML has made possible for the manufacturing industry. The good news is that the scale of reinvention is going higher even as you are reading this; and manufacturing companies are evolving with machine learning every single day.
Self-driven cars are the biggest opportunity on the anvil, in the context of the application of machine learning in transportation. We have gotten closer to putting these high tech autonomous vehicles on the road, which promise faster and safer journeys, by eliminating human error from the process of driving. Autonomous vehicles are set to become the backbone of decision-making – from routing, minimizing disruptions and coordinating emergency responses, to enabling better fuel economy and making transport-related predictions.
At the business front, logistics is going to see a tremendous disruption with autonomous trucks and railway cargo bursting with impending potential. A survey by Forbes Insights – Logistics, Supply Chain and Transportation 2023: Change at Breakneck Speed – found that 65% of respondents from the surveyed industries recognized tectonic shifts in logistics, supply chain and transportation processes. Just under 62% say their own companies are already undergoing profound transformation. One of the major disrupting factors was found to be machine learning.
Machine learning has started helping clinical decision-making – for instance, from diagnosing diabetic retinopathy in retinal images, to Google’s ML algorithm that helps identify cancerous tumors on mammograms, and Stanford’s deep learning algorithm to identify skin cancer.
While there may still be time before robot surgeons lead an entire surgery on their own, the phenomenon is not too far in the future. In fact, Da Vinci, a robotic laparoscopic surgery system, has performed around 600,000 procedures, with a human surgeon maneuvering it remotely from a console.
The advancements made in the healthcare sector due to machine learning is set to make surgeries less invasive, characterized by more dexterity and possible interventions. ML is also expected to make more accurate diagnosis, less invasive yet more effective treatments, and even allowing comparison of patient symptoms for projected treatments. The application of ML in healthcare will naturally improve the outcomes of treatment, for both common ailments and complicated diseases.
Retail has displayed one of the most obvious transformations, which is attributed to machine learning. ML has touched many aspects of the retail industry – sales and customer relationship, recommendations, manufacturing, logistics and delivery, and payments.
Recall the robot Pepper, developed by a Japanese telecom company, to serve as an in-store customer greeter and representative? Since then, the bot has been adopted by a bunch of companies that now claim sharp increase in customer interactions, foot traffic and revenues.
The impact of machine learning on online stores is increasingly evident by way of optimized analytics, marketing, product placement, and product stocking. However, ML is now moving to brick and mortar stores, Pepper being a strong example, and is gradually changing processes such as retail stocking and inventory, and behavioral tracking for marketing, product placement and even theft.
Financial services are perhaps the best suited industry for machine learning applications, given the high volume and need for accuracy in records. According to a McKinsey report, in Europe alone, more than a dozen banks have replaced older statistical-modeling approaches with machine-learning techniques and, in some cases, experienced 10% increase in sales of new products, 20% savings in capital expenditures, 20% gain in cash collections, and 20% reduction in churn.
ML is now an integral part of the financial ecosystem, helping with asset management, loan approval and risk assessment. However, there is so much more predicted for the finance industry, including data security and fraud detection, loan underwriting, algorithm trading, investment predictions and portfolio management, trade settlements, customer service, prevention of money laundering and more. In the field of insurance, ML is expected to help with determining the price of insurance, estimate losses, monitor fraud, and so on.
A few other industries
Of course there are other industries that are experiencing significant impact of machine learning, which is helping them create better revenue models, address customer needs better, cut back operational costs, prevent security breaches, enforce improved regulations, and more. Some of these industries include education, cybersecurity, telecommunications, food and beverage, among others.
With the global machine learning market expected to grow to $8.81 billion in 2022 from $1.41 billion in 2017, it is equipped to bring in a whole new set of technologies that will offer significant enhancements in data accumulation and its integration and analysis, powering up industries across the globe.
This is definitely a space to watch.
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