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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 E-Discovery 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.

      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.

      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

      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

      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.

      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

      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 processes that are quite amenable to AI. Professional services touch many of the industries discussed in this chapter, 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

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