While deployment of artificial intelligence across business operations has become widespread, challenges with change management, talent retention and integrating data are hindering scaling, according to a new research report from MIT Technology Review Insights.
Data sharing is cited as one solution to significantly advancing AI’s impact, provided more clarity around privacy regulation and standards is developed. Two-thirds of survey respondents said they were willing to share internal data externally to help develop new AI-enabled efficiencies, products or value chains.
The report, "The Global AI Agenda: Promise, Reality, and the Future of Data Sharing,” was produced in partnership with Genesys and Philips.
“Manufacturers see their chief wins [with data sharing] in the forms of greater supply chain speed and visibility, more efficient production operations, and faster and more innovative product development,” notes its authors.
Respondents from consumer goods, retail, pharma and health care cited similar expected gains for the supply chain and product development.
The vast majority of respondents (87%) had begun deploying AI by 2019, but 51% struggled with modifying business processes to leverage the technology. Data challenges, such as integrating unstructured data and interfacing with open-data platforms, were cited by nearly half as a problem.
A lack of related talent and skills was also a common refrain of the report’s CIOs and CTOs, with 42% of respondents saying a shortage of internal data scientists and related experts is a major constraint on their use of AI.
Also among the findings was a breakdown of how AI is most being used in business operations:
60% of manufacturers and pharma companies are using AI to improve product quality.
47% of retail and consumer firms are using it in customer care.
Over 50% of energy firms are leveraging AI for monitoring and diagnostics.
58% of financial services providers are using it fraud detection
52% of tech firms are using AI to strengthen cybersecurity.
The report was developed through a global survey of over 1,000 senior executive across 11 different sectors, with the majority coming from manufacturing (15%), IT and telecommunications (14%), consumer goods and retail (13%), financial services (11%), and pharma and health care (10%).
Findings also incorporated interviews with experts holding responsibility for or a knowledge of AI. Geography was split, with 20% each in North America, Europe, Asia, Latin America, and the Middle East and Africa.