- Investments toward applied AI, which includes ML, computer vision and natural-language processing, grew 150% between 2018 and 2021, according to a report from the McKinsey Technology Council released last week. In 2018, businesses spent $66 billion on applied AI technologies, compared to $165 billion in 2021.
- The business functions with the highest rates of AI adoption are product development, service development, service operations, marketing and sales, the firm found. To compile the report, McKinsey collected data based on search engine queries, news publications, patents, research publications and investments.
- Businesses saw cost reduction and revenue growth as a result of adopting AI. However, companies still grapple with high up-front investments in talent and resources, cybersecurity and privacy concerns, increasing regulation, and compliance and ethical implications, according to the report.
Companies adopting AI expect to optimize their business processes. Across industries, AI has become more widely adopted and deployed. From Truist in financial services to Panera in the restaurant space, AI is helping companies execute strategies.
AI has become so critical to business operations and goals that it has influenced how companies shape leadership structures.
When Ashok Srivastava joined Intuit as SVP and chief data officer in 2017, his main task was to build an AI team. He said that he felt like the emphasis on AI was because the company’s future relied on the right use of AI.
“They gave me the title of chief data officer because... they knew that data was going to be essential for it,” Srivastava said.
Levi’s recently announced it is deploying AI throughout its fulfillment network to serve e-commerce orders better. Like Intuit, the company has data and AI together under Louis DiCesari, global head of data, analytics and AI.
Despite the popularity of AI, success is not guaranteed, and data sourcing is critical to benefit from AI. Earlier this month, a study found that enterprise leaders use over half of their AI budget on data sourcing and preparation stages.
Bad data can do more than break an AI model; it can reduce customer trust and revenue due to biases. Ethical concerns surrounding AI are not new, but as more companies begin planning and adopting AI projects the risk cannot be ignored.
More than one-third of tech leaders said their company was negatively impacted by bias in their AI algorithms, according to a study earlier this year.