Machine learning (ML) is a subset of artificial intelligence (AI), which is defined as an ability of a machine to use historical data and algorithms to imitate how humans learn, gradually increasing its accuracy. Relying on machine learning services, companies across every industry deploy ML-based solutions to improve productivity, decision-making, product and service innovation, customer journey, and more.
These days, it’s not an exaggeration to say that each of us encounters machine learning multiple times each day – mobile banking, top news pop-ups, recommended content in social networks, Uber’s commute estimations, chatbots – the list goes on. And even though saying that machine learning entirely changes the way we live may sound like a cliché, we’re going to say and prove that once again with the most impressive and recent statistics. Here, we’ve compiled a list of stats and facts about ML and AI market share, use cases, adoption across industries and business functions, ongoing investments, talents, and more.
The global machine learning market is steadily growing: in 2021, it was valued at $15.44 billion, and owing to the increasing adoption of technological advancements, it is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8%. (Fortune Business Insights).
Plan to implement in the next three years
Data source deloitte.com—Automation with intelligence. 2022
Although today’s use cases for machine learning are becoming more varied, customer-centric applications remain the most common. According to Statista, 57% of respondents state customer experience represents the top ML and AI use cases.
Being an extension, not a replacement for human capabilities, machine learning enables companies to automate complex processes, improve the quality, effectiveness and creativity of employee decisions with rich analytics and pattern prediction capabilities, and uncover gaps and opportunities in the market to introduce new products and services, hyper personalize customer experience, and much more. (Accenture)
As ML and AI initiatives are becoming more widespread, companies are getting more value out of their investments, as you can see from the following numbers and facts:
Data source: pwc.com—Sizing the prize. What’s the real value of AI for your business and how can you capitalise?
With such inspiring benefits, no wonder enterprises are increasing their investments in ML and AI initiatives. For example, solely in the United States the spending on artificial intelligence will grow up to $120 billion by 2025, representing a compound annual growth rate (CAGR) of 26.0% in 2021-2025. (IDC)
While AI and ML are becoming mainstream, the advances in AI and ML are being slowed by the shortage of employees with required skills. According to Statista, 82% of organizations need machine learning skills and only 12% of enterprises state the supply of ML skills is at an adequate level.
This shortage has the potential to hold back digital innovation and economic growth. According to the IBM Global AI Adoption Index in 2022 research, 34% of organizations consider insufficient AI skills, expertise or knowledge as the top reason blocking successful AI adoption. (IBM)
Convinced that with an effective AI team in place they would leverage AI to its full potential, companies are now allocating their budgets to:
Struggling to find specialists with adequate AI and ML knowledge, organizations put focus on such hard skills as:
Data source: snaplogic.com—AI Skills — 93% of Organizations Committed to AI but Skills Shortage Poses Considerable Challenge
As for soft skills needed for these tech roles, 37% of the respondents of the IBM’s survey Addressing the AI Skills Gap in Europe consider problem-solving to be the most critical soft skill and 23% of tech recruiters struggle with finding applicants with this aptitude. (IBM)
Itransition’s ML team builds and implements custom ML-powered solutions as well as helps you optimize the existing ML workloads and scale ML across the enterprise.
Based on the findings of IDC research Thrive in the Digital Era with AI Lifecycle Synergies, 50% of the AI initiatives fail and only one-tenth of AI and ML PoCs reach production deployments. IDC quotes the lack of data science skilled personnel, the cost of AI solutions, data management issues (data quality, quantity, and access), and issues with the ML algorithm explainability and selection as the main reasons for AI initiatives’ failure. (IDC)
According to some sources (Statista, IBM), the inability to scale AI and ML projects up tops the challenges that are hindering AI from reaching its potential.
Even though 85% of IT professionals agree that consumers are more likely to choose a company that’s transparent about how its AI models are built, managed and used, the IBM Global AI Adoption Index 2022 research reveals that a majority of organizations haven’t taken key steps to ensure their AI is trustworthy and responsible:
Other facts and statistics:
As seen from the statistics above, each company regardless of the industry has an endless number of ML adoption scenarios and high chances of success in case of following through on their initiative. If you want to advance your ML usage and achieve tangible economic gains, you need to take a holistic approach to AI and ML adoption. Rather than focusing on implementing scattered ML-powered solutions to address specific business needs, think of ML as an enabler of business transformation, enhanced decision-making, and modernized systems.
September 21, 2023
