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How Much Is The AI Industry Worth? A Quick Glance At The New AI Economy

Currently, the Artificial Intelligence market is predicted to become the next trillion-dollar industry by 2030. Some of the most common technologies that are contributing to the growth of the Artificial Intelligence (AI) market are Machine learning (MI), Natural Language Processing (NLP), Predictive Analysis, Robotics, and Image Recognition.

With the increase in automation of various tasks, AI could eliminate around millions of jobs in the next ten years. It is predicted that every one person out of five will use AI technology to complete their work.

What is AI?

AI is the intelligence that is demonstrated by machines therefore,  AI is also known as Machine intelligence.

AI is a system that can interpret any external data, learn from such data, and can use the learning to complete tasks and goals.

The major goal of AI is to implement the intelligence of humans in machines by developing systems that can think, understand, learn and behave in a manner similar to humans.

Use of AI in various sectors

AI is being used in various industries and offers a lot of promise for the future. Some of the most popular applications of AI in present times include:

  • Various games are using AI technology to make games more realistic and engaging. Games like poker, chess, tic-tac, etc are now using AI.
  • NLP is used to interact with a machine such as a computer in a natural language spoken by humans. The systems that can comprehend speech and language and then talk to a person in the same language are also widely gaining popularity. The system is also capable of understanding accents, noise in the background, slang words, etc. Its application can be seen in virtual assistants like Alexa and Siri.
  • The vision systems that are capable of interpreting and comprehending visual inputs or photographs on the computer screen are also being used for various practical requirements. Doctors are using this technology to diagnose patients while crime investigators are using it for face recognition of criminals.
  • Software that can recognize handwriting with the help of AI and then can write in editable text has been used in solving many cases by law enforcement agencies.
  • Robots have been incorporated with processors, memory, and sensors that exhibit intelligence. They are also able to learn from mistakes and can adapt to environmental needs.

Market size and analysis

Technologies such as ML, NLP, Predictive Analysis, and image recognition have a large share in the AI market.

At present, North America has the largest share of revenue generated from the AI market.

This is due to the fact that companies in North America are adopting AI in large numbers. After North America, Europe has the second largest share of revenue generated from the AI market.


In the next 5-7 years, the Asia Pacific region is predicted to be the most lucrative market for Artificial Intelligence.

This is due to the fact that this region is emerging as a hub for startups working on innovative ideas.

Many startups here are already providing AI-based products and several other startups are working on the development of new products and services related to AI such as chatbots and virtual assistants.


Many countries such as the U.S., China, Germany, India, and Japan have seen growth in the AI market because of the rising trend of automation in many industries.

Many new companies in these regions are coming up with innovative service offerings across various industries such as BFSI, healthcare, gaming, agriculture and many more.

Growth depends on sectors and regions

North America already has the largest share of the AI market and the market is predicted to grow considerably in the coming years.

The segments that contributed to the largest share of the AI market are the government and defense sectors and they would continue to be a dominant market for AI for the next few years.

With a large number of startups and tech giants such as Amazon, Google, Intel, Microsoft, Apple, IBM, etc, the AI market will continue to grow at a rapid pace in the U.S.

The market for AI in the North American region would undergo consolidation in the upcoming years. This consolidation would be driven by the increasing adaptability of AI and AI-based products across new industries.

The increasing trend of using AI for video surveillance, video analysis, and cybersecurity solutions, will continue to boost the AI market in the U.S. in the coming years.

Governments across the world will also be using AI for improving their core functions across various domains such as defense, finance, technology, transportation etc.

This translates into increased spending by government departments for the next few years, giving a tremendous boost to the growth prospects of the AI industry.

Guest contribution by Ethan Scott – he started his career in the publishing industry at a very young age. It was 2014 until Ethan realized that he needed to explore the terrains of writing and seek his passion for it. He worked, partnered, and contributed to 20+ websites and blogs and constantly thrived by working on them.  

Read next: AI Economy: How Do You Make Money With Machine Learning?

Connected Concepts

DevOps

devops-engineering
DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.

DevSecOps

devsecops
DevSecOps is a set of disciplines combining development, security, and operations. It is a philosophy that helps software development businesses deliver innovative products quickly without sacrificing security. This allows potential security issues to be identified during the development process – and not after the product has been released in line with the emergence of continuous software development practices.

Continuous Intelligence

continuous-intelligence-business-model
The business intelligence models have transitioned to continuous intelligence, where dynamic technology infrastructure is coupled with continuous deployment and delivery to provide continuous intelligence. In short, the software offered in the cloud will integrate with the company’s data, leveraging on AI/ML to provide answers in real-time to current issues the organization might be experiencing.

Continuous Integration

continuous-integrationcontinuous-deployment
Continuous Integration/Continuous Deployment (CI/CD) introduces automation into the stages of app development to frequently deliver to customers. CI/CD introduces continuous automation and monitoring throughout the app lifecycle, from testing to delivery and then deployment.

MLOps

mlops
Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.

RevOps

revops
RevOps – short for Revenue Operations – is a framework that aims to maximize the revenue potential of an organization. RevOps seeks to align these departments by giving them access to the same data and tools. With shared information, each then understands their role in the sales funnel and can work collaboratively to increase revenue.

AIOps

aiops
AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.

Ad-Ops

ad-ops
Ad Ops – also known as Digital Ad Operations – refers to systems and processes that support digital advertisements’ delivery and management. The concept describes any process that helps a marketing team manage, run, or optimize ad campaigns, making them an integrating part of the business operations.

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