Over the past 50 years, the computing industry has undergone three significant changes: the mainframe period; the shift to PC-server computing; and the emergence of the cloud, mobile, and the Internet. The fourth transition that has already occurred is data-centric computing, which will be driven mainly by the growing ubiquity of artificial intelligence (AI).
According to a study by market research firm Grand View Research, the global AI market was valued at $136.6 billion in 2022 and is projected to reach $1,811.8 billion by 2030, expanding at a CAGR of 38.1% over the forecast period.
In this article, we’ll look at seven different industries that are being transformed by AI currently. But first, let’s understand what artificial intelligence is.
Understanding Artificial Intelligence (AI)
The overuse of the term “artificial intelligence” is becoming annoying. It is used interchangeably with any application powered by cutting-edge technology, hindering its true meaning and objective. Therefore, defining AI and its applications for industrial companies is important.
AI refers to the ability of computer systems or computer-controlled robots to perform problem-solving and decision-making tasks typically associated with human intelligence. These tasks include:
- Recognizing images and speech
- Translating languages
- Providing recommendations
- Making decisions
- And more
In simple terms, a machine’s ability to carry out cognitive processes typically associated with human minds, including sensing, thinking, learning, interacting with the environment, and problem-solving, is known as artificial intelligence (AI). Some examples of AI technologies include machine learning (ML), robotics, computer vision (CV), natural language processing (NLP), virtual assistants, and autonomous vehicles (AVs).
How Companies Use AI
One industrial sector in which AI is adding value is enhancing knowledge workers’ capabilities, particularly engineers. Such applications primarily make use of AI’s predictive skills. Companies are discovering new ways to reframe conventional business problems so that AI can leverage ML algorithms to evaluate data and experiences, identify patterns, and make predictions accordingly.
Put simply, companies use AI in three significant ways:
- Developing more intelligent products
- Providing smarter services
- Improving internal business processes and operations
A 2022 survey by consulting firm McKinsey & Company indicated that while the adoption of AI globally is 2.5x higher today than in 2017, it has leveled off in the past few years.
According to the study, 20% of respondents said they had adopted AI in at least one business area in 2017. Today, that number is 50%, though it reached an all-time high of 58% in 2019.
Additionally, the average amount of AI capabilities that businesses leverage has doubled from 1.9 in 2018 to 3.8 in 2022. Examples include computer vision (CV) and natural language processing (NLP). While NLP has advanced from the middle of the pack in 2018 to the top of the list just behind computer vision, robotic process automation and computer vision has continued to be the most frequently deployed among these capabilities each year.
AI is transforming and revolutionizing businesses across the world, irrespective of their shapes or sizes, across all industries. This includes providing opportunities for developing countries to thrive or for drug discovery. Professionals across all industry sectors must know how AI functions, regardless of their field.
The first step to influencing AI strategy within your organization and being at the forefront of the future is learning about how AI is transforming businesses worldwide and how you can capitalize on these changes.
That being said, let’s jump right in!
AI Use Cases & Applications Across Different Industries
AI applications range from consumer-based solutions (like chatbots) to highly complex industrial use cases, such as financial services or warehouse automation, to improve the interoperability of vehicles using IoT.
Let’s look at some significant AI use cases and applications reforming major industries.
The contribution of tech giants like Facebook, Microsoft, IBM, Google, and Apple in the healthcare sector holds great importance for the industry.
AI is currently being leveraged for a variety of healthcare services, including medical imaging, medication management, drug development, robotic surgery, and data mining for pattern recognition and, subsequently, more precise diagnosis and treatment of medical disorders.
For instance, IBM Watson can evaluate a patient’s medical record to find potential treatments. The AI tool derives the meaning and context of a set of structured and unstructured data that may be important for choosing a treatment plan. In other words, IBM Watson functions like an actual physician.
Similarly, a biopharmaceutical company called NuMedii is disrupting drug discovery through big data and AI leveraging its Artificial Intelligence for Drug Discovery (AIDD) platform. The company uses the platform to discover novel mechanisms, cell types, targets and biomarkers, which will be influential in identifying and developing precision therapies for diseases.
While many of us frequently take the availability of the Internet and communications for granted, the telecommunications sector depends on several highly complex processes and constant modifications. These needs are met by AI in many ways, including:
- Network Optimization
Networks must respond to abnormalities and adapt to changing traffic to sustain seamless operations. Currently, 63.5% of telecommunication companies and providers leverage AI to monitor and enhance their networks and offer the best performance for their end users.
- Predictive Maintenance
The hardware used in telecommunications networks is widely distributed. Moreover, issues within this infrastructure could impact the overall network. Predictive algorithms powered by AI allow telecommunications companies to predict when problems are most likely to occur.
- Retail & E-commerce
AI is improving experiences for both customers and organizations, particularly in the retail and e-commerce sectors. Technology such as chatbots, which are AI-based tools designed to simulate human-like exchanges online, are only the tip of the iceberg. Through tailored shopping experiences, robotic warehouse pickers, facial recognition payment systems, and anti-fraud technologies, AI is revolutionizing the e-commerce and retail industries.
- AI Personalization
Artificial intelligence is now being leveraged by brands to tailor their services, personalize customer experiences, and boost sales. A better, more successful online future is anticipated by many e-commerce organizations, thanks to the quick adoption of AI-powered personalization.
AI-driven personalization customizes a brand’s marketing messages, content, products, and services using machine learning, deep learning, NLP, etc. With the help of this technology, organizations engage with customers in new ways that lead to more lucrative customer journeys.
For example, after offering personalized recommendations that appeared after a consumer added one item to their cart, a shoe merchant from London saw an 8.6% rise in the add-to-cart rate.
Recent Examples of AI Use Cases in Retail & E-commerce
eBay uses AI to provide user recommendations and advice, accelerate shipping and delivery, lower prices, build trust between buyers and sellers, and more. Find It On eBay, Image Search, and eBay ShopBot (a personal shopping assistant on Facebook Messenger) are recent examples of AI-powered features.
In 2019, the online marketplace claimed that it could identify 40% of online credit card theft with “high precision” with the help of AI.
3PM uses AI to safeguard its clients’ and their customers against online fraud on marketplace websites. It leverages machine learning algorithms, which can frequently distinguish between fakes and the actual product while becoming more intelligent and, consequently, more effective over time.
ReconBob, an AI product by 3PM, uses Google Cloud Platform services to identify inconsistencies in seller evaluations on Amazon, eBay, and Walmart.com websites.
From virtual reality mirrors and face recognition payments to interactive in-store smartphone games, online retailer Alibaba leverages AI for everything. The company even developed an AI copywriting tool with deep learning models and NLP to churn out as many as 20,000 lines of content per second.
- Banking & Financial Services
To say that machine learning (ML) and artificial intelligence (AI) are transformational technologies is an understatement. In a recent poll involving IT and line-of-business executives, Deloitte found that 86% of financial services AI adopters believe AI would be “extremely” or “critically important” to the success of their company over the next two years.
Although the banking industry has always been heavily dependent on technology and data, new data-enabled AI technology has the potential to accelerate innovation more quickly than ever before. AI may help increase differentiation, manage risk and regulatory requirements, boost efficiency, support a growth agenda, and improve customer experience.
The following are some other examples of artificial intelligence in the banking & financial services industry:
- Fraud Detection
The frequency of financial fraud attempts, whether on a large scale or through routine crimes (such as credit card skimming), is constantly increasing and negatively impacts companies and people.
Business Insider claims that financial institutions like J.P. Morgan Chase use specialized AI algorithms to identify transactions that don’t fit standard patterns and flag them for further inspection.
- Algorithmic Trading
The shouting of traders on the stock market floor is a thing of the past. Today, algorithms manage most trading transactions, reacting and making decisions much faster than humans ever could. Additionally, it is predicted that the algorithmic trading market will be valued at $19 billion annually by 2024.
- Generative AI for Consumer-Based Solutions
The emergence of generative AI has the potential to alter the business landscape fundamentally. This technology can potentially disrupt industries and how businesses operate because it enables the generation of original content by learning from existing data.
Generative AI can boost efficiency and productivity, cut costs, and provide new growth prospects by making it possible to automate numerous jobs that humans previously performed.
AI technology is expanding into domains previously reserved for humans, thanks to products like ChatGPT and GitHub Copilot, as well as the underlying AI models that drive such systems (Stable Diffusion, DALLE 2, GPT-3, to name a few).
By leveraging generative AI, computers may now display creativity. Using the extracted data and interactions they’ve had with users, they can produce original content. They can write computer code, produce blogs, create sketches of package designs, or even theorize on the cause of a production error.
Artificial intelligence (AI) has several potential real-world applications in the automobile sector — from self-driving cars, driver assistance systems, and traffic prediction to increasing safety and decreasing traffic congestion.
It can power autonomous vehicles, helping them make intelligent decisions, navigate traffic, and avoid obstacles. AI can improve driver assistance systems like adaptive cruise control, lane departure warning, and automatic emergency braking. These systems monitor and accommodate traffic conditions using cameras, radar, and other sensors, making driving safer and more efficient.
Jointly developed by Aptiv and the Hyundai Motor Group, Motional pushes the potential of self-driving cars closer to reality by integrating each company’s technical expertise with real-world experience.
To prioritize safety, the company’s autonomous driving technology uses three sensor types: LiDAR, radar, and cameras. This has allowed the company to produce the world’s first robotaxi pilot as well as an operational, commercial robotaxi service that has offered over 100,000 self-driven rides with a track record of zero at-fault incidents.
Since 2018, Motional has collaborated with key ridesharing companies Lyft, Via, and Cox Automotive to expand the availability of autonomous transportation around the globe.
- Logistics & Supply Chain Management
Retailers worldwide suffer losses of around $1.1 trillion annually due to poor execution in the logistics & supply chain industry. Factors involving leftovers and low supply levels can be avoided.
Restocking in the retail supply chain can be done using AI, which determines the demand for a specific product by factoring in historical sales, location, weather, trends, promotions, and other factors.
With the help of BlueYonder, Morrisons has improved the situation with stock forecasting and replenishment in 491 stores. As a result, the number of in-store shelf gaps was reduced by up to 30%.
The Future of Artificial Intelligence
What future applications might artificial intelligence have? Although it is difficult to predict how technology will advance, most experts believe those “commonsense” jobs will become even simpler for computers to process.
Russell Glenister, CEO and Founder of Curation Zone, said, “AI is starting to make what was once thought to be impossible possible, like driverless cars.”
“Fast GPUs and access to training data are essential factors that make driverless cars possible. Huge volumes of accurate data are needed to train driverless automobiles, and completing the training quickly is equally important. Although the processors were too slow five years ago, the invention of GPUs made everything possible,” he said.
Glenister added that “GPUs would only get faster, thereby advancing the use cases of AI tools across the board.”
Applications of big data, machine learning, and other technologies have significant consequences across industries, as the range and depth of AI applications demonstrate. While some of these applications are still in their infancy, they will only be the tip of the iceberg as this technology develops.
Therefore, organizations should consider taking additional steps to increase their internal capacity for AI research and application.