1/ AI adoption has more than doubled since 2017
In 2017, 20% of businesses reported adopting AI in at least one business area.
Today, it stands at 50%.
2/ AI capabilities such as natural-language generation have doubled since 2018
Robotic process automation and computer vision are the most commonly deployed each year.
Natural-language had advanced from mid-pack in 2018 to the top of the list, behind computer vision.
3/ Business investment in AI has increased
In 2018, 40% of businesses using AI reported > 5% of their digital budgets went to AI.
Today, 52% report that level of investment.
Looking forward, 63% say they expect their business investment to increase over the next 3 years.
4/ Areas where businesses see value from AI has evolved
In 2018, manufacturing and risk where the largest business functions where value from AI was reported.
Today, the greatest revenue increases using AI are found in:
• Marketing and sales
• Product development
5/ Cost benefits from AI are are increasing
Businesses report the highest cost benefits from supply chain management.
25% of respondents reported that ≥ 5% of their EBIT was attributable to AI in 2021.
(EBIT = earnings before interest and taxes, an indicator of profitability)
6/ Concern: Risk mitigation
While AI use has increased, there has been no significant increase in mitigation of AI-related risks from 2019.
Top reported business risks:
• Regulatory compliance
• Personal privacy
7/ AI high-performers
'AI high performers' are businesses with ≥ 20% of EBIT from AI use.
They are 1.6x more likely to engage nontechnical employees in creating AI applications through low-code or no-code programs.
8/ Hiring AI talent remains difficult
Software engineers were hired most in the past year.
This is more often than data engineers and data scientists.
This represents a shift from experimenting with AI to actively embedding it in enterprise applications.
9/ Tech talent shortage
A majority of respondents had difficulty hiring for each AI-related role in the past year.
2022 was more difficult to acquire talent than past years.
Data scientists were the most difficult role to fill.
10/ AI data scientist and machine learning engineer demand
AI high performers are:
• 2x more likely to hire machine learning (ML) engineers and AI data scientists than other businesses
• 2x more likely to hire an AI product manager to oversee product development and adoption