Artificial intelligence has entered various sectors in the last five years. With the enterprise adoption of machine learning and deep learning algorithms, many existing industries have seen widespread
Artificial intelligence, machine learning, and deep learning technologies have entered the mainstream; they are being adopted by enterprises all over the world. While these technologies certainly hold the potential to vastly improve the quality of operations in the corporate sector, they also stand to disrupt many existing markets.
AI can easily be extended, adapted, and applied to different business operations. When considering that AI is just a computer program, we can begin to see the potential scope of the technology. The reason that AI is being adopted on such a large scale is due to its capacity to bring intelligence to tasks that previously did not have it.
This, coupled with the technology’s ability to automate repetitive processes with intelligence, makes it a highly disruptive power in various sectors. Keeping this in mind, we explored some of the industries that are most likely to be impacted by the widespread adoption of AI technology. Let us see why companies are so eager to adopt artificial intelligence.
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Why AI Is a Disruptive Force
To look at the capabilities of AI, we must first look at what AI is. In simple terms, artificial intelligence is a computer program that can mimic certain aspects of human intelligence. Instead of simply following commands given to it, AI employs intelligent strategies and heuristics to bring a human-like intelligence to solving problems like any other computer program.
1. Improvement Through Machine Learning
Machine learning programs form a subset of AI that has the ability to learn from the data fed to them, even after being deployed. This gives a utility factor to companies adopting ML algorithms, as maintenance and upgrade costs are reduced. Moreover, the self-improving nature of ML allows solutions to dynamically develop according to the needs of the problems at hand.
The capabilities of AI range far and wide in an enterprise setting, but one of the biggest things to note is that clean data makes AI better. Hence, such algorithms are well-suited to businesses, where there is an established data workflow with a large volume of data. Today, data collection and data storage have become a norm, but sectors which have maintained long-standing data banks, such as finance, healthcare, and logistics, will stand to benefit the most from an AI solution.
While the capabilities of AI are varied and different from deployment to deployment, some characteristics exist across all kinds of AI. Primarily, they can utilize large amounts of data to iterate towards better solutions. This is a huge draw for companies that have collected large amounts of data. They can simply train an AI to solve a certain problem using this data and deploy a solution explicitly suited to their needs.
AI can be deployed and improved upon with minimal effort and can also be tailored to a company’s requirements using data. This close fit of solutions for the problem at hand is one of the biggest reasons companies are flocking to AI solutions. This, combined with the capability of machine learning algorithms to improve upon themselves with additional data, makes AI an easy buy for enterprises.
2. Reduced Expenses
Another unique selling proposition of artificial intelligence solutions is that they are not only orders of magnitude faster than human labor, but also considerably cheaper. This softens the blow for companies looking to explore AI as a solution, as the potential monetary gains are much higher than the initial investment.
Cloud computing and the vast variety of cloud service providers are also helping in AI adoption. Not only is the cloud deployment of AI cheaper than an on-premise solution, but it also comes with plug-and-play tools. The pricing is flexible, which further decreases the initial investment that companies have to make in order to try an AI solution.
3. Predictive Advantage
Predictive analytics, a branch of AI, is very useful across industries. Using machine learning algorithms and predictive models, a program can be trained to find the relationship between various variables. The program then uses this information to predict what the relationship between the variables will be like in the future.
For example, a predictive algorithm employed in a supply chain scenario will be trained using the data of the shipments. The quantity, supply, and demand of each item will be ingested by the algorithm, among other data. Then, the program can accurately predict the required quantity to be shipped by looking at past relationships between supply and demand.
As one can imagine, predictive analytics can optimize processes vastly, cutting down on warehousing costs and overheads. This is especially useful in retail, supply chain, and logistics markets. Predictive analytics is simply indicative of another useful characteristic of complex AI programs; pattern recognition.
By utilizing concepts from statistics and computer science, an ML program can be trained to recognize patterns. This not only includes patterns in the harvested data but also in areas, such as image and video recognition. This gives it considerable use in healthcare, defense, and customer service.
Let’s delve deeper into industries that are most likely to be disrupted by AI and ML solutions.
Industries That Will Get Disrupted by AI
Artificial intelligence has entered various industries over the past five years. With the enterprise adoption of machine learning and deep learning algorithms, many existing sectors have seen widespread disruption by the new technology. Let’s take a look at the ten industries that will get disrupted by AI the most.
AI’s adoption in the healthcare sector promises to bring a lot of benefits to adopters. Primarily, the healthcare sector as a whole has been geared towards collecting accurate and relevant data about patients and those who come into care. This makes AI a good fit for the data-rich world of healthcare. Secondarily, AI can find a variety of use-cases in the healthcare sector.
The introduction of AI can enable the widespread deployment of predictive healthcare. Using the power of predictive analytics, AI can help doctors make proactive moves towards ensuring their patients’ health. This is a much better approach to healthcare than the reactive approach taken today. With the rise of IoT-enabled embedded devices, doctors can remotely monitor the health of patients, and can also be informed in case a patient is in an emergency.
Apart from predictive healthcare, AI can also enable an easier analysis of scan results through image recognition. This has already been used to help doctors diagnose symptoms at a much higher rate, as AI can comb through multiple scans much faster than humans. Health chatbots are also being developed. These bots will allow doctors to collect preliminary data regarding the symptoms of the patient. industry conducts mission-critical operations.
2. Customer Service and Experience
AI has already begun to disrupt customer service. Natural language processing (NLP) algorithms have found their way into customer-facing helplines in the form of chatbots. These chatbots can collect information about a customer’s issues and enable customer support executives to work more efficiently. In certain cases, they are also able to resolve the customer’s issues on their own, only escalating to human executives if necessary.
Due to their ability to accurately understand what the customer is saying, sufficiently advanced NLP algorithms may replace customer support executives altogether. Instead of being a statically assigned algorithm with a set of predefined responses, the chatbot can dynamically adapt itself to any issue the customer is facing. Moreover, as the customer does not need to wait to get connected with a support executive, the waiting time is reduced, thereby improving customer experience.
Apart from chatbots and customer helplines, recommendation engines can also prove beneficial. Amazon is an excellent example of this. The website dynamically generates a distinct homepage for all of its customers based on their browsing habits. Netflix also utilizes recommendation engines to a great extent, thus enhancing customer experience by providing tailored recommendations for each user.
3. Banking, Financial Services, and Insurance (BFSI)
AI and the financial sector are a great fit for each other. Similar to healthcare, BFSI companies have been collecting, collating, and organizing data for many decades, making AI a natural addition to the field. The technology has been used to detect the chance of an individual conducting a fraudulent transaction.
Banking is a sector where paperwork and documentation are ever-present. AI can also automate processes that were previously done manually, such as paperwork and documentation. This will not only decrease the time required to solve issues but also enable banks to serve customers better.
Moreover, predictive analysis has also found great success in the BFSI sector. Banks can identify high-value customers using predictive analytics through data mining and parsing text online. They can also retain customers longer by providing additional services based on their spending and financial activities.
By looking at the customer’s credit history, an AI can accurately predict the likelihood of an individual defaulting on a loan. This streamlines the process of onboarding new customers while reducing the likelihood of non-payment.
AI in logistics holds the potential to drastically change operations. Predictive analytics can accurately predict the inventory required by a vendor and optimize routes to minimize overhead costs.
Ab InBev, the worldwide distributor for beverages like Budweiser and Corona, has used AI to optimize logistics to a great extent. Using predictive analytics, the organization was not only able to brew the optimal amount of each beverage, but also accurately predict the demand of a certain product. This allowed them to cut down the warehousing expenses and overhead costs significantly.
Shipping companies also stand to benefit greatly from implementing AI. Usually, document checks at customs stations hold up the shipping process. Today, it takes multiple working days for a ship to get clearance to ship all its goods. Image recognition algorithms and intelligent automation can help customs officials conduct checks more seamlessly by scanning the documents involved, transitioning it into a digital realm.
This data can then be used to accurately keep track of shipments while cutting down on time spent at ports. Due to the technology’s benefits, the worldwide shipping industry has also adopted AI, especially predictive analytics, to optimize supply chain economics.
Retail analytics is already seeing widespread adoption among retailers. Apart from optimizing the supply chain, retailers are also able to accurately predict how much to stock in their supermarkets. Moreover, by collecting data about the way that customers access the store, they are able to arrange the products according to customer preferences, thereby increasing the overall sales.
Retail is also set to be disrupted by AI in the form of self-shop stores. Amazon has already demonstrated a proof-of-concept for completely autonomous shopping. Amazon Go has already opened several stores all over the United States. It utilizes machine learning, deep learning, image recognition, and smart automation to allow customers to walk in and walk out with the products of their choice.
Apart from brick-and-mortar establishments, Amazon has cemented its leadership role in online marketplaces through retail analytics. By analyzing the customer browsing patterns and their purchases on the site, Amazon is able to accurately predict similar products, thus maximizing sales.
AI in cybersecurity can work with vast databases that most cybersecurity companies maintain to check for virus attacks. The technology is also being adopted by antivirus companies to provide a proactive method of combating cyberattacks.
Due to a large amount of existing data on the kinds of cyber-attacks, malware, and attack vectors, AI can be trained to exhibit reasoning. This will allow companies to employ set-it-and-forget-it AI solutions that will continually monitor the network for any suspicious activity. If an out-of-place activity is detected, the algorithm can immediately patch the hole in security or notify human handlers of the problem. This reduces the time required to solve the problem, thus minimizing risk and loss of information.
In addition to this, long-term cyberattacks on high-profile targets, such as multinational enterprises, can also be detected sooner by AI solutions. AI actively monitors the networks for malicious activities, thus allowing a company to detect an attack a lot sooner. This is integral in reducing damage and protecting the company from financial and data losses.
Autonomous driving is considered as one of the most revolutionary uses of AI in the real world. Self-driving cars have already made their way into the mainstream due to companies like Tesla, and even Uber is looking into deploying autonomous vehicles. Giants like Google are also creating self-driving technology.
Apart from this, autonomous driving can also be used for goods transportation. Self-driving trucks will enable quicker deliveries and more efficient spending, as they will not require rest stops and will cost lesser than human drivers. An example of this is Tesla’s Semi automobile. This truck has safety features that are made possible by AI algorithms. These image processing algorithms can determine if a collision is imminent based on the speed of the vehicle and the perceived depth of other vehicles on the road.
Soon, this technology will advance enough to allow humans to take the position of a supervisor, who will only be required to monitor the AI. Driving will become autonomous in such circumstances, thereby reducing the strain on human drivers and cutting down the expenses for companies.
The marketing industry will benefit from AI in two main ways. The first is more personalized messaging, and the second is better targeting. Other smaller benefits, such as intelligent automation and AI-based tools, have already begun surfacing and are being adopted.
AI marketing solutions can also determine the most effective messaging for a company based on customer preferences. For example, if a customer orders a pair of shoes, the algorithm sends out a notification to the customer for similar products, thereby increasing the likelihood of the customer buying another product.
AI will enable marketing departments to reach customers more easily, as targeted advertising using neural networks becomes more widespread. Services like Google and Facebook ads have already started using AI technology for better targeting. Recommendation engines can also be used for personalized advertisements on a user-to-user basis.
Even though the advancement of autonomous weapons has been regulated heavily, this sector is sure to develop with the amount of capital being poured into it. Ethical consequences of creating autonomous weapons have also been considered, but AI-powered weapons are said to be indicative of the next arms race.
Apart from autonomous weapons, image recognition and video recognition may be used for surveillance of the general population. By building upon existing databases with biometric and facial scans, a citizen can be identified using facial recognition algorithms in surveillance networks. This increases the general security of the nation while reducing human intervention.
Ethical discussions about the use of this technology have also emerged, as it can be misused to enforce an authoritarian style of rule. Such technology has already seen deployment in China, where widespread facial recognition algorithms are being used to create a social credit system. Citizens are graded, based on their actions, which are logged using AI-based cameras.
AI will also lead to several lifestyle changes, such as smart homes and integrated living. Devices such as Google Home and Amazon Alexa have become popular all over the world, and chatbots might see more representation in the coming years across industries.
Such devices have already seen widespread use among the general populace. Along with the rise of the internet of things, predictive algorithms can enable an automated way of living for adopters. For example, a fridge can use image recognition algorithms to detect if it is running low on vegetables. It can then place an order at a nearby grocery store and have the groceries delivered to the user’s doorstep through a robot.
This method of living will extend into an everyday household undertaking. Moreover, the overall disruption brought about by AI will fundamentally change life as we know it.
Talking about the industries that will get disrupted by AI the most, Michael Beckley, CTO and founder, Appian, says, “Stories about AI Disruption used to center around fintech startups or truck drivers getting replaced by autonomous vehicles. That all changed on September 14, 2019, when a swarm of 19 AI-powered drones crippled Saudi Arabia’s oil production and disrupted the national security industry forever.
Multi-billion dollar air defenses, Patriot missiles, satellites, and fifth-generation fighters are virtually powerless to stop inexpensive, easy to produce, drones and missiles powered by today’s readily available, commercial-grade AI technology. We are just beginning to process the consequences for global security, let alone the disruption to the defense industrial base. Nothing less than a revolution in speed and agility in procuring and fielding new technology and doctrine is required.”
Talking about the ones that will be the last to be disrupted by AI, he says, “Highly regulated industries are proving especially resilient to AI disruption. You need to be able to explain to financial regulators why you turned someone down for a mortgage, and that is difficult or even impossible with today’s deep learning technology.
While we work on inventing explainable AI, financial services firms are using AI and RPA with Low-Code platforms to augment human decisions rather than replace them. AI-powered image recognition, document classification, entity extraction, and translation services can make humans far more efficient without fundamentally disrupting the way humans process claims and make decisions.”
Jeff Denworth, VP products and co-founder, VAST Data, says, “I think the transportation and logistics industry is going to be severely disrupted as the machines that warehouse and move goods through our economy become smart enough to make their human operators obsolete.
The AI driving a truck from the port of Long Beach to Chicago doesn’t have to stop for legally required meal and sleep breaks, doesn’t drive off the road because it didn’t get a good night’s sleep, and doesn’t need health insurance or a pension.
Unfortunately, this will create real disruption in people’s lives as all the drivers have to make the transition from the king of the open road to finding new professions in mid-life.
He further adds, “The industries that will be the most resistant to AI are those based on some combination of personal service and creativity. As AI becomes “smart” enough to take over mundane tasks, people will start valuing the human touch.
Sure, you can order your drinks from a tablet, and they can be delivered by a simple robot, but a human bartender does more than just mixing gin with tonic. People will still want to talk to their bartender and barber, and even more importantly, when things go wrong, they want to talk to a human being who can both listen and do something about the problem.
If we’re lucky, as automation and AI take over ordinary tasks, some company in a service industry like an airline or hotel chain will realize that granting their humans more authority to deal with the failures of AI will lead to greater customer loyalty and therefore profits rather than having the AIs treat humans like more AIs.”
- AI automates decision making. When a human gets incoming data – such as a report – they make a handful of decisions. Per minute? Per hour? Per day? AI can process a lot more incoming data while producing a lot more decisions much faster. From a disruption perspective, this means industries with a large workforce spending over 80% of their time making medium complexity decisions.
- AI is only as meaningful as the data available to feed the machine. From a disruption perspective, this means industries that access to a wide variety and volume of data, or where innovative ways to quickly and economically collect data can exist.
Media has already been disrupted by AI as their advertising based business models were hijacked by platforms with AI-driven bidding markets and audience targeting. The reason third party platform players have been able to do this is because they found a scalable, economic way to collect the data that powers their AI (e.g., web searches or social network interactions). Banks and insurance are clear candidates for similar disruption.
Talking about the industries that will be the last to be disrupted by AI, he says, “At first glance it would seem that healthcare has a lot of opportunity for disruption (e.g., scan analysis, patient monitoring, etc.) but its legacy and regulatory environments create a barrier on the required data which will slow down the disruption.
Industries that strongly depend on physical activity such as mining or steel would be very hard to disrupt as well. Sure, AI can optimize where you have to mine or how to better process steel, but the optimization would have to be very significant (10x) to make disruption possible.
If I can only give one answer to the question who will be the last to be disrupted by AI, it would be Strategic Consulting. AI has a long way to go before it can reach the levels of complex decision making and creativity required.”
Closing Thoughts for Techies
While artificial intelligence is one of the most revolutionary technologies of the 21st century, its effects on existing markets are yet to be seen. We are at the beginning of the adoption curve for AI and its accompanying technologies, and the long-term benefits will soon be witnessed.
With that being said, the current capabilities of AI make it clear that we are nowhere near discovering its full potential or impact on society. From lifestyle changes to behind-the-scenes improvements in normal societal institutions, AI is changing the way every industry conducts mission-critical operations.
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