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.
AI thrives on data and excels at automating routine tasks, so those industries with a wealth of digitized data and manual processes are poised to reap large rewards from implementing AI. For these industries, AI can enhance the things you want to increase, such as quality, adaptability, and operational performance, and mitigate the things you want to reduce, such as expense and risk.
This article provides a bite-sized overview of industries that can derive specific benefits from implementing AI.Healthcare, a prime target for AI
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.
© metamorworks/Shutterstock.com- 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.
- 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 back-end 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 and AI
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.
Ai in the Energy sector
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.
AI can capture data across all rigs and other operations and production systems to apply predictive models that can quickly identify potential problems, order the required parts, and schedule the work when physical maintenance is required.
Banking and investments and AI
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 and procedures. This helps ensure real-time automated transaction monitoring to ensure proper compliance with established rules and regulations.
Intelligent recommendations: AI can mine not just a consumer’s past online activity, credit score, and demographic profile, but also behavior patterns of similar customers, retail partners’ purchase histories — even the unstructured data of a customer’s social media posts or comments they’ve made in customer support chats, to deliver highly-targeted offers.
AI in the insurance industry
Some in the industry think that factors unique to insurance — size, sales channel, product mix, and geography — are the fundamental cost drivers for insurers. However, a McKinsey survey notes that these factors account for just 19 percent of the differences in unit costs among property and casualty insurers and 46 percent among life insurers. The majority of costs are dependent on common business challenges, such as complexity, operating model, IT architecture, and performance management. AI can play a significant role in mitigating these costs.Claims processing: Using NLP and ML, AI can process claims much faster than a human and then flag anomalies for manual review.
Fraud detection: The FBI estimates the annual cost of insurance fraud at more than $40 billion per year, adding $400 to $700 per year for the average U.S. family in the form of increased premiums. Using predictive analytics, AI can quickly process reams of documents and transactions to detect the subtle telltale markers that flag potential fraud or erratic account movements that could be the early signs of dementia.
Customer experience: Insurance carriers can use AI chatbots to improve the overall customer experience. Chatbots use natural language patterns to communicate with consumers. They can answer questions, resolve complaints, and review claims.
Retail and AI
The global economy continues to apply pressure to margins, but AI gives retailers many ways to push back.Reduced customer churn: MBNA America found that a 5-percent reduction in customer churn can equate to a 125-percent increase in profitability. Predictive analytics can identify customers likely to leave as well as predicting the remedial actions most likely to be effective, such as targeted marketing and personalized promotions and incentives.
Improved customer experience: A 2014 McKinsey study notes that companies that improve their customer journey can see revenues increase by as much as 15 percent and lower costs by up to 20 percent. AI provides a deeper and contextual understanding of the customer as they interact with your brand. In particular, natural-language processing and predictive analytics provide a granular understanding of your customer regarding their product preferences, communication preferences, and which marketing campaigns are likely to resonate with each customer.
Optimized and flexible pricing: Predictive analytics enable a company to implement an optimized pricing strategy, pricing products according to a range of variables, such as channel, location, or time of year. The system creates highly accurate predictive models that study competitor prices, inventory levels, historic pricing patterns, and customer demand to ensure that pricing is correct for each situation, achieving up to 30 percent improvement in operating profit and increasing return on investment (ROI) up to 800 percent.
Personalized and targeted marketing: A 2016 Salesforce report found that 63 percent of millennials and 58 percent of Generation-X customers gladly share their data in return for personalized offers and discounts. Retailers are uniquely positioned to collect a range of data on individual customers, including preferences, buying history, and shopping patterns. Predictive analytics help personalize marketing and engagement strategies. A 2017 Segment study found that 49 percent of shoppers made impulse buys after receiving a personalized recommendation and 44 percent become repeat buyers after personalized experiences.
Improved inventory management: The days of overstocking inventory are quickly diminishing as retailers realize that optimized stock equals more profit. Predictive analytics gives retailers a better understanding of customer behavior to highlight areas of high demand, quickly identify sales trends, and optimize delivery so the right inventory goes to the right location. The results are streamlined supply chains, reduced storage costs, and expanded margins.
Legal system and AI
AI is tackling the mountain of paper that characterizes most legal proceedings by providing better and smarter insights from organizational data to detect compliance risks, predict case outcomes, analyze sentiment, identify useful documents, and gather business intelligence to make better-informed decisions. Through automation and the use of predictive analytics, these technologies have significantly helped reduce the time and costs associated with discovery.A 2018 test pitted 20 lawyers with decades of experience against an AI agent three years into development and trained on tens of thousands of contracts. The task? Spot legal issues in five NDAs. The lawyers lost to the AI agent on time (average 92 minutes as opposed to 26 seconds) and accuracy (average of 85 percent as opposed to 94 percent).
In one case, a discovery team of three attorneys on a class-action lawsuit had 1.3 million documents to review. They used eDiscovery to code 97.7 percent of the 1.3 million documents as non-responsive, leaving fewer than 30,000 documents for the three-attorney team to review.
AI can aggregate and analyze data across a law department’s cases for budget predictability, outside counsel and vendor spend analysis, risk analysis, and case trends to facilitate real-time decision making and reporting. AI can perform document on-boarding and reviews based on continuous active learning to prioritize the most important documents for human review — lowering the total cost of review by up to 80 percent.
AI and human resources
Another bastion of paperwork, the HR department is a good candidate for streamlining processes using AI. In the 2018 “Littler Annual Employer Survey” of employers, the top three uses for AI were recruiting and hiring (49 percent), HR strategy and employee management (31 percent), and analyzing company policies and practices (24 percent).As the average job opening attracts 250 resumes, the most immediate gains in efficiency are possible in recruiting and hiring. Scanning resumes into an applicant tracking system can reduce the time to screen from 15 minutes per resume to 1 minute. Natural-language processing and intent analysis go beyond keyword searches to find qualified candidates whose wording doesn’t exactly match the job posting. Virtual assistants interact with candidates to schedule meetings, an otherwise time-consuming and tedious task. By automating these and similar tasks, HR personnel have more time to focus on strategic tasks that require an interpersonal approach.
AI impacts on supply chain
Globalization increases volatility in demand, lead times, costs, and regulatory hurdles, just to name a few factors. The announcement of a new trade tariff or a sudden flare-up of civil unrest can force quick adjustments and decisions. AI and data visualization techniques can accelerate the transition from reactive operations to predictive supply chain management and automated replenishment. It starts with recovering the value locked up in structured and unstructured data to convert a data swamp into a data lake to provide pervasive visibility of the current state of all assets across the entire organization and beyond to partners, customers, competitors, and even the impact of the weather on operations and fulfillment. It ends with streamlined processes, improved customer satisfaction, reduced costs, and an increased bottom line.Transportation and travel and AI
Transportation issues have become the many-headed hydra of the twenty-first century, threatening the lifestyle and sustainability of metropolitan life. Addressing traffic is one of the defining challenges of worldwide urban life for this century.Congestion: The cost of congestion in the U.S. reached $305 billion in 2017. AI can process the complex dataset of traffic monitoring to suggest intelligent traffic light models and use real-time tracking and scheduling to mitigate traffic, both on the road and for public transport systems.
Maintenance: A single downed truck can cost a fleet up to $760 per day. A grounded plane can cost more than $17,000 a day. Using machine learning and digital twins, you can assess the performance of a vehicle, plane, or train in real-time and trigger notifications or alerts when repairs or preventive maintenance are needed. The system uses automation to order parts and schedule maintenance.
Public safety: AI can track real-time crime data to increase public safety and direct law enforcement to developing situations.
Freight transport: Predictive analytics can assist in forecasting volume to optimize routes and inventory.
Telecom industry and AI
With the turn of the millennium and the advent of ubiquitous communications, the era of customer loyalty for a communications provider has passed. Customers churn faster than carriers can roll over minutes. As the network continues to evolve, customer quality-of-experience expectations increasingly dictate consumer behavior.Customer support: AI-powered chatbots are helping many telecoms improve the customer experience while saving support costs. Nokia improved resolution rates by 20 to 40 percent. Vodafone improved customer satisfaction by 68 percent with its chatbot, TOBi.
Predictive and preventive maintenance: AI can process performance data at the equipment level to anticipate failure based on historical patterns and recommend tactical or strategic actions. For example, the system could alert a technician, who can use the AI-powered insights to proactively run diagnostics, perform root-cause analysis, and take action at any point in the link, from the set-top box all the way up the chain to the cell tower or network operations center. On the strategic level, these insights can inform network redesign to sustain better quality of experience and provide valuable data to inform development of new services to maintain a competitive edge.
Network optimization: AI can find patterns at the traffic level and notify the network operations center of anomalies so a potential issue can be corrected before it affects the quality of service and to assist in exploring alternatives for optimizing the existing network.
AI in the public sector
In 2017, United States agencies collectively received more than 818,000 freedom-of-information (FOI) requests and processed more than 823,000. In the second quarter of 2017, the U.K. Department for Exiting the EU was able to respond to only 17 percent of FOI requests, and the Department for International Trade faired only slightly better at 21 percent.AI can shorten the time to provide information by automating manual tasks and flagging requests that require special consideration, enabling government workers to focus on high-value tasks instead of tedium.
The U.S. Citizenship and Immigration Services respond to more than 8 million applications each year. In 2018, Emma, a virtual assistant on their website, responded to 11 million inquiries with a success rate of 90 percent.AI-assisted decision making: Many aspects of governance suffer from a surfeit of information. Separating the important from the mundane is a time-consuming and mind-numbing task for a human, but a simple and appropriate task for predictive analytics. AI can process and analyze enormous amounts and varieties of data to highlight patterns and reveal insights that facilitate efficient and effective decisions.
Internet of Things: As cities deploy devices such as traffic cameras, smart traffic lights, smart utility meters, and other sensors, AI can sift through the mountain of data they generate to streamline operations, optimize process control, and deliver better service.
Professional services benefit from AI
Professional services firms often focus on high-touch engagements that are essentially human-centric and thus may not seem to be good candidates for AI. However, much of the work they take on involves process that are quite amenable to AI. Professional services touch many of the industries discussed here, and just as technology matures and affects all industries, of necessity it affects how professional services firms engage their clients.The key takeaway is that AI won’t replace core professional expertise, but it will make professional services firms more efficient and thus increase the value proposition for their clients. However, professionals who do begin to embrace AI will replace those who don’t.
The applications span all industries:- Document intake, acceptance, digitization, maintenance, and management
- Auditing, fraud detection, and fraud prevention
- Risk analysis and mitigation
- Regulatory compliance management
- Claims processing
- Inventory management
- Resume processing and candidate evaluation
AI impacts marketing
The secret sauce in marketing is not a secret. The ingredients are well known and are used daily all over the world. What is new is the glut of data now available regarding every search, click, and comment your customers make. AI doesn’t reinvent marketing. It just simplifies the daunting task of incorporating everything your data tells you about customers so you can anticipate their next move and improve the experience.With AI, your marketing can accomplish these feats:
- Use everything you know about customers, including their order history, browsing path through the website, customer service interactions, and social media posts
- Target your candidates and customers down to the individual
- Personalize messages according to whatever metric you have tracked, even down to buyer personas
- Generate thousands of variations on a message
- Schedule messages to maximize engagement
- Train messages based on engagement feedback
- Customize the customer experience on your website
- Optimize customer engagement and reduce churn
- Optimize price, even down to the individual if you so choose
- Qualify leads automatically
- Produce more accurate sales forecasts
Media and entertainment and AI
AI obviously plays a big role in movies and video games through CGI, special effects, and gaming engines, but what can it do for the enterprise?Valuing and financing: AI can use predictive analytics to determine the potential value of a script and then identify likely prospects for investment.
Personalized content: AI can analyze user data to make intelligent recommendations for streaming media services.
Search optimization. AI can support intelligent search engines for visual content for applications within and outside of the media industry.
Film rating: AI can use predictive analytics to process historical rating information to suggest the proper rating for a film.
