Articles · 2022-12-13

AI Market: 2022 Status and Future Trends

AI Market: 2022 Status and Future Trends

AI has become one of the most exciting technology trends happening in the 21st century. The AI Market is booming, and the recent global pandemic has certainly had an impact on driving this growth, as it has both accelerated the already initiated digital transformation journey of many companies while also forced many other lagging companies to start that path.

There are still several roadblocks to the full implementation of AI models, since taking models to production still poses difficulties that not all available platforms are able to address, but AI (and machine learning) is only growing and will continue to do so in the coming years. In this article, we have gathered the most important statistics that paint a picture of the AI market and its projections in the near future. 

In this article we will address:

1.     Size of the AI market and growth projections.

2.     What industries are leveraging AI the most?

3.     Why and how is AI being adopted?

4.     How will it affect the job market?

1.     Size of the AI market and growth projections

- The global AI market is currently worth $136.6 billion (GrandViewResearch

The global AI market is counted in $136.6 billion (as of the latest 2022 data) and it's expected to grow by over 13x over the next decade.

The main reason for this drastic growth is the increase in practical use cases – from using AI for automating customer support operations to substituting manual labor inspection on production lines.

This market is set to grow by over 13x by 2030, and the exponential growth is driven by the continuous innovations' adoption among a variety of industries, and the development of platforms for data scientists and industry domain experts to build and run machine learning models that boost Artificial Intelligence (AI) not only for enterprises but also for SMB's businesses.  

The Global AI spending reached over $57 billion in 2021 (IDC). Artificial intelligence, machine learning, and augmented reality are shaping every organization. It is becoming more widely accessible as spending on AI technologies is increasing across many industries.  

Additionally, some estimations suggest that AI technology could generate $15.7 trillion in revenue by 2030 and boost the GDP of local economies by an additional 26% by 2030. (PwC)

45% of total economic gains by 2030 will come from product enhancements, thus stimulating consumer demand. This is because AI will drive greater product variety, with increased personalization, attractiveness, and affordability over time. 

- AI boosts industry profits by an average of 38% by 2035 (Accenture)

AI has the potential to boost rates of profitability by an average of 38% by 2035 and lead to an economic boost of US $14 trillion across 16 industries in 12 economies by 2035.

Regionally, China and North America are the regions that will see a bigger impact from AI on their GDPs:

Source: PwC analysis

2.    What industries are leveraging AI the most?

 The impact of AI on profits by industry

Industry verticals that are utilizing AI technology advantages vary – from Retail or other non-technical industries to Manufacturing and others. As per Accenture's prognosis, Manufacturing can generate an additional US$3.8 trillion in GVA by 2035, augmenting manual labour with AI and machinery: the manufacturing industry stands to gain $3.78 trillion from AI by 2035 (Accenture)

The use of AI in telecommunications is worth $2.5 billion and rising (Markets and Markets). The North American market in 2022 is obtaining the largest market size in AI in telecommunication – 36%, as vendors on the North American market have shown an increased investment, while the Asia Pacific (APAC) market is projected to grow at the highest CAGR (Compound annual growth rate).  

AI in telecommunication on the North American market is effectively used for various applications, such as network optimization, network security, customer diagnostics, and virtual assistance.  

A key use case is automating customer care operations, and AI Business predicts that 19 in every 20 customer interactions will be AI-assisted by 2025  across industries.

Tupl AI Care is a clear example of implementing a customer care operations automation solution that contributes to increased customer satisfaction rates while bringing down the cost of customer operations for the company. 

Most frequently applied machine learning and artificial intelligence use cases in 2020-2021

3.     Why and how is AI Being adopted?

AI is bringing competitive advantage to companies implementing AI-based solutions. Around 9 in 10 organizations back AI to give them a competitive edge over rivals (MIT Sloan Management)

And 83% of executives believe AI is a strategic priority for their businesses now, while 3 of 4 executives (75%)  say AI will allow them to move forward into new businesses, industries, and ventures (Forbes, PWC, Cisco).  

In fact, AI is already making a difference, as there’s a correlation between AI adoption and market position (PWC): companies adopting AI at a higher rate occupy market leader positions, while non-market leaders are still considering the solution, looking to scale or test PoCs with limited success.

These market leaders are looking at AI to increase productivity through automation, improve decision-making, improve customer experience, innovate in products and services, develop new data-driven business models, or even for recruitment and retention by improving employee experience and skills. 

However, the 2021 Gartner AI in Organizations Survey indicates that “most data and analytics leaders still struggle to move AI projects from prototype to production. The main challenge now is to clearly attribute and measure the contribution of AI to their business” (Gartner). 

Indeed, moving a model to production is still the biggest roadblock to AI adoption, since prototyping a model in a safe lab environment is very different from real production environments where multiple data sources need to be integrated, there are thousands of data points & KPIs that constantly evolve, and new problems and requirements are constantly arising. There are certain Important trends in ML to facilitate the adoption​ and success of AI, mainly no code machine learning, which allows a non-ML expert engineer to create ML models without writing a line of code​ and to build and deploy models with minimal skills​.

Undoubtedly, ML technology is key for enabling automation, but it’s only a small part of the overall working solution and there are operational aspects that need to be taken into consideration such as data quality, data processing, continuous operation, scalability, security, high availability, or maintainability​.

4.     How will AI affect the job market?

Almost 100 million people will be working in the AI space by 2025 while 85 million jobs will be displaced by automation and Artificial Intelligence (We Forum). 

With all these increasing projections related to the AI space, further manpower will be required. 

“Although 85 million jobs will be displaced by automation and Artificial Intelligence advances by 2025, in parallel, 97 million new roles will be created by 2025 to fill the work demands of the surging industry (The World Economic Forum).”

Below is data on employment shares and the proportion of jobs at high risk of automation by the early 2030s for all industry sectors:

Positions' replacement fears might be justified for workers in the transportation and storage (56.4%), manufacturing (46.4%), and wholesale & retail (44%) industries, but despite the misconception that automation and AI decrease job opportunities, it may prompt a massive acceleration in new positions covering evolving needs.

Conclusion

The future of AI is far from a passing trend as a growing number of companies are reorienting their plans to a more AI-driven automation strategy. But technology is only a part of the story, and there are many other operational aspects that contribute to the successful implementation of AI. Choosing a tool that is low-code and uses a white-box approach might be the key to taking prototypes to production successfully to enable the benefits and competitive advantages AI and ML can undoubtedly bring. 

Want to learn more about AI on practice and how telecom, manufacturing, agriculture, healthcare, and other industries are automating their manual operations? Review Tupl AI-powered solutions: https://www.tupl.com/solutions/  or request a demo

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