The adoption of artificial intelligence is in its prime in 2022. Thus, the market is projected to grow from $387.45 billion in 2022 to over $1,394 billion by 2029. World-leading companies and local brands all leverage AI to introduce more automation, intelligence, and added business profits to everyday processes.
However, it can be challenging to adopt AI and its offshoots for unaware business owners. Today, we’ll go over the top ways and applications that can drive value to your company.
The phenomenon of machine learning and AI is still shrouded in myths despite their proliferation. With that said, let’s debunk some of the most popular misconceptions linked to these technologies.
Although both can be used interchangeably, it’s machine learning algorithms that power the majority of business solutions. Artificial intelligence is an umbrella term that includes machine learning, deep learning, and neural networks.
Therefore, AI is mostly a theoretical layer behind computer intelligence while ML targets specific tasks by learning from data and making predictions. It means that artificial intelligence is an indispensable part of machine learning, while machine learning isn’t necessarily AI.
The main benefit of machine learning is automation. In particular, intelligent algorithms take over daily, mundane or data-laden tasks so that your team can focus on critical business processes. The automation craze has taken over with 56% of companies adopting AI in at least one function.
However, not all business tasks are fit for automation. Thus, if your business case doesn’t involve huge amounts of data or is undefined, ML adoption might not come to fruition. Therefore, not all projects are meant for computer intelligence.
All innovations come at a price and machine learning is no exception. Hardware, data governance, model development, and deployment inherently require an upfront investment. The latter depends on the size, complexity, and data state within your company. Employee training and maintenance are also among core expenditure items.
However, cost decreases from adopting AI tend to balance and pay off the initial investment. Thus, AI adopters report cost reductions of around 20% in business operations, according to Statista.
Before diving into the most popular machine learning applications, let’s go over the fundamentals of ML adoption. As we’ve pointed out, the main benefit of machine learning is automation. The latter is based on miscellaneous data that powers the ML algorithms. After that, the smart system extracts valuable insights out of the data and visualizes it for further reporting and analysis. This flow is the foundation of any machine learning application.
Predictive analytics is the process of using data mining techniques to identify patterns and trends in historical data in order to predict future events. It can be used to identify customer behavior, optimize marketing efforts, or determine the risk of future credit card fraud. By analyzing past behaviors, businesses can make more accurate predictions about what customers are likely to do in the future and create a more personalized customer experience.
The goal of predictive analytics is to help businesses and organizations make proactive decisions, rather than reactive ones, based on likely outcomes.
Real-world use cases of predictive analytics include:
Natural language processing (NLP) is a field of computer science and linguistics that deals with the interactions between computers and human languages. It involves programming computers to understand human language as it is spoken and to produce results that are natural-sounding to humans.
NLP has been used for a variety of tasks such as automatically understanding customer queries in call centers, translating documents from one language to another, and helping robots communicate with people.
Business-wise, Natural Language Processing algorithms apply to the following fields:
Computer vision is the ability of computers to interpret and understand digital images. This process involves identifying and extracting information from the pixels in an image, which can then be used for tasks such as object detection and recognition, localization, and 3D reconstruction.
The development of computer vision has led to some applications in both consumer and industrial settings, such as facial recognition software, self-driving cars, and augmented reality.
Among the most disruptive applications of computer vision are the following:
This technology relies on biometrics to map facial features from visual stimuli. It then compares face landmarks with the databases and produces a prediction. Facial recognition is gaining momentum each year with businesses adopting it at manufacturing sites, entrance systems, and others.
The facial recognition market was assessed at roughly $5 billion in 2021. By 2028, it will equate to over $12 billion. The pandemic has also contributed to the widespread adoption of facial recognition software with hands-free systems being adopted in public places and offices.
Moreover, facial recognition fuels the following use cases:
In 2022, machine learning adoption is gaining traction as a growing number of businesses shift to automation. Thanks to their capabilities of processing troves of data, ML algorithms can become a competitive asset for any data-laden business. From computer vision to predictive maintenance, ML applications span all business processes and stages.
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