Over the past five decades, AI has evolved from offering lightweight consumer applications or add-ons to supplementing and performing mission-critical and even life-or-death functions. The next sections introduce some of the ways AI is changing the landscape for the enterprise.
Benefits of AI for Your Enterprise
In addition, when an algorithm produces results with high accuracy and predictability, mundane processes and routine decisions can be automated, thus reducing the need for human intervention in the paper chase of the typical enterprise and freeing workers to focus on tasks that increase revenue and customer satisfaction.
This section provides a bite-sized overview of industries that can derive specific benefits from implementing AI. Later chapters explore use cases for each in depth.
Healthcare
It’s hard to find an industry more bogged down in data than healthcare. With the advent of the electronic health record, doctors often spend more time on paperwork and computers than with their patients.
In a 2016 American Medical Association study, doctors spent 27 percent of their time on “direct clinical face time with patients” and 49 percent at their desk and on the computer. Even worse, while in the examination room, only 53 percent of that time was spent interacting with the patient and 37 percent was spent on the computer.
A 2017 American College of Healthcare study found that doctors spend the same amount of time focused on the computer as they do on the patients.
A 2017 Summer Student Research and Clinical Assistantship study found that during an 11-hour workday, doctors spent 6 of those hours entering data into the electronic health records system.
The good news is that AI is changing that equation. Healthcare is a data-rich environment, which makes it a prime target for AI:
Natural-language processing can extract targeted information from unstructured text such as faxes and clinical notes to improve end-to-end workflow, from content ingestion to classification, routing documents to the appropriate backend systems, spotting exceptions, validating edge cases, and creating action items.
Data mining can accelerate medical diagnosis. In a 2017 American Academy of Neurology study, AI diagnosed a glioblastoma tumor specimen with actionable recommendations within 10 minutes, while human analysis took an estimated 160 hours of person-time.
Artificial neural networks can successfully triage X-rays. In a 2019 Radiology Journal study, the team trained an artificial neural network model with 470,300 adult chest X-rays and then tested the model with 15,887 chest X-rays. The model was highly accurate, and the average reporting delay was reduced from 11.2 to 2.7 days for critical imaging findings and from 7.6 to 4.1 days for urgent imaging findings compared with historical data.
Speech analytics can identify, from how someone speaks, a traumatic brain injury, depression, post-traumatic stress disorder (PTSD), or even heart disease.
Manufacturing
If any system is ripe for transferring the tedious work to intelligent agents, it’s a system of thousands of moving parts that must be monitored and maintained to optimize performance. By combining remote sensors and the Internet of Things with AI to adjust performance and workflows within the plant or across plants, the system can optimize labor cost and liberate the workforce from the tedious job of monitoring instruments to add value where human judgment is required.
AI can also drive down costs using sensor data to automatically restock parts instead of referring to inventory logs and by recommending predictive maintenance as opposed to reactive maintenance, periodic maintenance, or preventative maintenance, extending the life of assets and reducing maintenance and total cost of ownership. McKinsey estimated cost savings and reductions could range from 5 to 12 percent from operations optimization, 10 to 40 percent from predictive maintenance, and 20 to 50 percent from inventory optimization.
Energy
In the energy sector, downtime and outages have serious implications. One study estimated that more than 90 percent of U.S. refinery shutdowns were unplanned. A McKinsey’s survey found that, due to unplanned downtime and maintenance, rigs in the North Sea were running at 82 percent of capacity, well below the target of 95 percent, because, although they had an abundance of data from 30,000 sensors, they were using only 1 percent of it to make immediate yes-or-no decisions regarding individual rigs.
In December 2017, a hairline crack in the North Sea Forties pipeline halted production that cost Ineos an estimated £20 million per day. In contrast, Shell Oil used predictive maintenance and early detection to avoid two malfunctions, saving an estimated $2 million in maintenance costs and downtime.
Banking and investments
The finance sector is blessed, or cursed, with both a super-abundance of paperwork and a surplus of regulation. I say “blessed” because the structured nature of the data and tightly defined rules create the perfect environment for an AI intervention.
Credit worthiness: AI can process customer data, such as credit history, social media pages, and other unstructured data, and make recommendations regarding loan applications.
Fraud prevention: AI can monitor transactions to detect anomalies and flag them for review.
Risk avoidance and mitigation: AI can review financial histories and the market to assess investment risks that can then be addressed and resolved.
Regulatory compliance: AI can be used to develop a framework to help ensure that regulatory requirements and rules are met and followed. Through machine learning, these systems can be programmed with regulations and rules to serve as a watchdog to help spot transactions that fail to adhere to set regulatory practices
