How AI will impact business in the next decade
By Tania Rahman | 7 minute read | November 14, 2017
– AI already impacts many aspects of our daily lives at work and at home
– Over the next decade, AI enterprise software revenue will grow from $644 million to nearly $39 billion
– Here are the ways that we predict AI will impact business over the next decade including vehicular object detection, predictive maintenance and intelligent recruitment.
Artificial intelligence already impacts many aspects of our daily lives at work, at home and as we move about. Over the next decade, analyst firm Tractica predicts that annual Global AI enterprise software revenue will grow from $644 million in 2016 to nearly $39 billion by 2025. Services-related revenue should reach almost $150 billion. They report that there are 6 artificial intelligence segments which will account for a significant percentage of these revenues:
1. Machine learning
2. Natural Language Processing and Understanding
3. Computer vision
4. Machine reasoning
5. Strong AI
6. Deep learning
These functional areas are applicable to many use cases, industries, and generate benefits to both businesses and individuals. Here are the top use cases which will reap financial rewards for AI technology product and service companies, and a broad spectrum of benefits for everyone else.
Machine and vehicular object detection, identification and avoidance
Self-driving cars and other autonomous vehicles are consistently called the “next revolution” in transportation, technology and, some say, in civilization in general. Some predict that, along with the growth of the electric vehicle segment, it could bring an end (or the beginning of the end) to car ownership as we know it as soon as 2030.
Just as with cloud and computing as a service, it will be interesting to see how consumers and businesses can get the transportation value of a vehicle without the maintenance, storage, upgrades and depreciation costs of vehicle ownership. Would they would be willing to either rent out their vehicle on a site like Turo, or rent someone else’s car for a daily rate? Sharing economy leaders Lyft, Uber and new entrants can easily leverage autonomous cars to facilitate getting a vehicle from one car rental customer to another.
Autonomous forklifts, drones and other robot warehouse workers are already retrieving boxes for shipment for thriving e-commerce companies. Vehicles are now equipped with sensors to calculate distance and routes to their destination and spatial room between vehicles, and to identify potential hazards like pedestrians, poor road conditions and other vehicles. AI-enabled machines and vehicles don’t cause accidents while texting, they don’t fall asleep at the wheel, and they don’t need lunch breaks. A vehicle which can prioritize driving into the ditch or a tree instead of a person can save lives and reduce insurance costs.
Advances in IoT, geospatial applications and artificial intelligence have aligned to make autonomous vehicles a reality. Autonomous vehicles like Olli aren’t science fiction, they’re reality.
Visual recognition, classification and tagging
In industries like law enforcement, media and entertainment, AI provides organizations with the ability to process large volumes of photographs and NSO images, and prepare them for discovery and reuse.
Algorithmic financial trading strategy performance management
Financial services data is fast-moving, is highly regulated and Exchange Traded Fund (ETF) data requires a high level of security. Unlike human traders that rely on intuition, AI-driven algorithms like Watson’s Equbot can analyze a 10-year history of stocks and real estate holdings in fractions of seconds, as opposed to hours or days.
Watson can help investors make data-driven decisions on when to buy, hold and sell equities. It can also help regulators identify rogue traders who are making fraudulent securities transactions. Watson Financial Services reduces the risk of misconduct, while addressing the multitude of regulatory requirements trading firms must adhere to on an ongoing basis.
Localization and mapping
It goes without saying that self-driving cars need up-to-the minute details on the roads and road conditions that it is driving on. It also needs to maintain Simultaneous Localization and Mapping so the autonomous vehicle doesn’t “SLAM” into other cars and trucks on the roadway. AI helps to guide vehicles to their intended destination, relative to other vehicles, buildings and obstructions.
For autonomous vehicles, robots, drones and cargo-carrying transportation governed by AI, geospatial applications can play a role in tracking trends in location-related business data. It could track the locations of customers that buy a certain manufacturer’s product, or identifying regions with an ideal demographic for propensity to buy high-end electronics, for example. Companies can use this data to improve their marketing, customer service and product value.
For manufacturers with high-value machinery, airlines with large fleets of planes or car rental chains with many vehicles, protecting the value of their assets is critical. AI can help these companies (and others) to keep track of when wearable parts were last replaced when servicing needs to be completed next, and how long equipment or vehicles are in service.
Predictive maintenance reduces the frequency of equipment failures, as preventative action can be taken to refurbish or tune assets on a scheduled basis. Maintenance can take place relative to manufacturing or service requirements. For example, if certain equipment is required for a specific product run, it can be serviced outside of those parameters. Or, when a ship is scheduled to be in port for a specific time period, it can be serviced such that it won’t impact service level agreements.
AI can determine the possible outcome if maintenance doesn’t occur, and make accurate determination when equipment or vehicles should be taken out of a service rotation based on failure patterns or age. Sensors can monitor asset performance and transmit the data back to a cognitive analytics hub through IoT.
Prevention against cyber security threats
Government organizations, commercial enterprises and freelance white hat security experts try valiantly to keep ahead of the latest spyware, botnets, DDoS attack patterns, and other threats in cyberspace. Yet hackers are constantly seeking new vulnerabilities to exploit and encryption defenses to topple.
Cognitive Security systems scour the vast amounts of threat intelligence available on the internet, and help companies and public sector organizations to re-mediate their network and service perimeters before hackers can prey upon them. Human security analysts do their best to keep pace with the latest threats, but in many cases they are overwhelmed by Zero Day viruses and other emerging threats. Cognitive security is a way for companies to gain leverage through deep learning and strong AI.
Converting paperwork into digital data
Cognitive capture leverages AI and Machine Learning to expedite the process of “training” their systems to recognize key metadata (like employee numbers, invoice numbers, or loan numbers) and digitize records more effectively. It also liberates companies from scanning application and service vendor lock-ins.
Cognitive capture leverages innovative cloud, machine learning, and open source architecture to convert unstructured data into powerful insights through analytics. It also helps companies meet regulatory requirements without the burden of storing paper records, and increases the speed and accuracy of information discovery. Instead of just extracting the text, images and signatures from documents, cognitive capture learns the context of documents. It can then trigger workflows accordingly, to either file documents away in a repository, or send it to a case management system, accounts receivable or other application for immediate attention.
Intelligent recruitment and HR systems
Traditional applicant tracking systems can be great at filtering out unqualified online job applications, yet they can sometimes eliminate qualified candidates in the process, should a resume not be optimized based on the right keyword phrases. AI makes finding the right candidate a more intelligent, data-driven process. By going beyond the basic words on a resume to determine job fit, it adds context based on reasoning and human inputs.
AI also enhances traditional HR information systems by recommending career paths for certain employees and the best way to coach, motivate and engage employees based on their personality, mindset and other characteristics. Making better hires is a good start: however, retaining employees and ensuring they are mentored and challenged to do their best is part science, part art-form and part algorithm.
These are some of the leading market sectors which are generating revenue for developers of AI software, and service providers in this space. There are other segments like public safety, customer service and more which are growing, and other use cases will continue to emerge.
If you are looking for ways to create efficiencies, disrupt your industry and innovate by leveraging the power of cognitive computing and AI, discover the Watson products and services that best meet your business needs.
Find out how Watson can help you build a more intelligent, AI-driven enterprise.