A global study by MIT Technology Review Insights (MITTR) has found that while most businesses are seeking to disrupt their industries using generative AI (Artificial Intelligence), only a small proportion believe they have the right level of technology and other attributes such as funding, culture and skills to support its rapid adoption. The MITTR study was produced in partnership with Telstra International, a global arm of leading telecommunications and technology company Telstra.
Those with the most experience in rolling out generative AI have even less confidence in their IT, suggesting many businesses underestimate the requirements for its effective deployment. This implies their plans to be disruptors—rather than the disrupted—may well flounder over problems that many respondents appear not to appreciate fully.
Said study polled 300 business leaders across Asia-Pacific, the Americas, and Europe on how their organisations are implementing—or planning to implement—generative AI technologies, along with the barriers to effective deployment.
The respondents mostly manage information technology, data, and data engineering-related functions, and represent a broad spectrum of industries including financial services, banking, and insurance, consumer packaged goods and retail, manufacturing and automotive, technology and telecom, logistics, energy, oil, and gas, and media and communications.
Geraldine Kor, Managing Director of South Asia and Head of Global Enterprise at Telstra International, said the MITTR study sheds light on companies’ readiness to tackle the challenges to effective adoption of generative AI.
“As the world becomes increasingly digitised and human-to-machine interactions flourish, being able to process data to drive informed real-time or near real-time business decisions is paramount,” she said, commenting on the MTTR study. “When implemented successfully, this proficiency will be a game-changer for most organisations, and will distinguish leaders from followers. However, building end-to-end capabilities to handle large datasets, accurately contextualise the data for business value and ensure the responsible and ethical application of AI is extremely challenging.”
Some Compelling Findings from the MITTR Study
The MITTR Study included the following key findings:
- Executives expect generative AI to disrupt industries across economies. Overall, six out of 10 respondents agree that generative AI technology will substantially disrupt their industry over the next five years. Despite inevitable variations, respondents who foresee disruption exceed those who do not across every industry.
- Majority do not see AI disruption as a risk and instead hope to be disruptors. Rather than being concerned, 78% of respondents see generative AI as a competitive opportunity. Just 8% regard it as a threat. Most hope to become disruptors: 65% say their businesses are actively considering new and innovative ways to use generative AI to unlock hidden opportunities from data.
- Despite expectations of change, few companies went beyond experimentation with, or limited adoption of, generative AI in 2023. Although most (76%) companies surveyed had worked with generative AI in some way in 2023, few (9%) had adopted the technology widely. The rest who experimented had deployed it in only one or a few limited areas. Moreover, the most common use case was automating non-essential tasks—a low-to-modest-gain, but minimal-risk usage of the technology.
- Companies have ambitious plans to increase adoption in 2024. Respondents expect the number of functions or general purposes where they will seek to deploy generative AI to more than double in 2024. They expect to frequently apply the technology in customer experience, strategic analysis, and product innovation areas by the end of 2024. Meanwhile, respondents plan to increase the use of generative AI in specific fields relevant to their industries. These areas include coding for IT firms, supply change management in logistics, and compliance in financial services.
- Companies need to address IT deficiencies or risk falling short of their generative AI ambitions. Fewer than 30% of respondents rank IT attributes at their companies as conducive to rapid adoption of generative AI. Moreover, these results may be overly optimistic. Those with the most experience of rolling out generative AI have even less confidence in their IT. Many in this group (65%) say their available hardware is, at best, modestly conducive to rapid adoption.
- Other factors can also undermine the successful use of generative AI. Respondents, both in general and AI early adopters, also report non-IT impediments to the extensive use of generative AI.
- Risk: 77% of respondents cite their regulatory, compliance, and data privacy environment as a leading barrier to rapid AI adoption.
- Budgets: 56% list IT investment budgets as a leading barrier.
- Competitive environment: Early adopters of generative AI are more than twice as likely to see the competitive environment as an enabler of rapid adoption than as a barrier.
- Culture: Early adopters of generative AI are more likely to regard openness to innovation as an enabler of rapid adoption.
- Skills: The skills needed for significant AI projects are in short supply; but among the respondents, early adopters are more acutely aware of the shortage of available talent.
Expert Insights on AI
Commenting on the state of generative AI in Singapore, Laurence Liew, Director of AI Innovation at AI Singapore, said: “Singapore, like most countries, is still in the early stages of adopting generative AI, with the technology only recently becoming available in productivity suites suitable for a wider audience. The requirements for effective implementation of generative AI include access to real datasets, AI engineers, and computer infrastructure.”
“Companies face a dilemma in accessing the necessary hardware today. Choices include outright purchase and pay-as-you-go outsourcing, both of which carry their risks. Additionally, data quality, storage and talent remain bottlenecks for effective deployment,” he added.
“At AI Singapore, we try to address the issues of AI talent with programmes such as the AI Apprenticeship Programme (AIAP) and the LLM Application Developer Programme (LADP), both designed to help companies solve an immediate business problem in which AI could be used, and also build up a pipeline of AI Talents,” Liew concluded.
The MITTR study can be downloaded at the following link: https://www.telstra.com.sg/genAI.
Archive
- October 2024(44)
- September 2024(94)
- August 2024(100)
- July 2024(99)
- June 2024(126)
- May 2024(155)
- April 2024(123)
- March 2024(112)
- February 2024(109)
- January 2024(95)
- December 2023(56)
- November 2023(86)
- October 2023(97)
- September 2023(89)
- August 2023(101)
- July 2023(104)
- June 2023(113)
- May 2023(103)
- April 2023(93)
- March 2023(129)
- February 2023(77)
- January 2023(91)
- December 2022(90)
- November 2022(125)
- October 2022(117)
- September 2022(137)
- August 2022(119)
- July 2022(99)
- June 2022(128)
- May 2022(112)
- April 2022(108)
- March 2022(121)
- February 2022(93)
- January 2022(110)
- December 2021(92)
- November 2021(107)
- October 2021(101)
- September 2021(81)
- August 2021(74)
- July 2021(78)
- June 2021(92)
- May 2021(67)
- April 2021(79)
- March 2021(79)
- February 2021(58)
- January 2021(55)
- December 2020(56)
- November 2020(59)
- October 2020(78)
- September 2020(72)
- August 2020(64)
- July 2020(71)
- June 2020(74)
- May 2020(50)
- April 2020(71)
- March 2020(71)
- February 2020(58)
- January 2020(62)
- December 2019(57)
- November 2019(64)
- October 2019(25)
- September 2019(24)
- August 2019(14)
- July 2019(23)
- June 2019(54)
- May 2019(82)
- April 2019(76)
- March 2019(71)
- February 2019(67)
- January 2019(75)
- December 2018(44)
- November 2018(47)
- October 2018(74)
- September 2018(54)
- August 2018(61)
- July 2018(72)
- June 2018(62)
- May 2018(62)
- April 2018(73)
- March 2018(76)
- February 2018(8)
- January 2018(7)
- December 2017(6)
- November 2017(8)
- October 2017(3)
- September 2017(4)
- August 2017(4)
- July 2017(2)
- June 2017(5)
- May 2017(6)
- April 2017(11)
- March 2017(8)
- February 2017(16)
- January 2017(10)
- December 2016(12)
- November 2016(20)
- October 2016(7)
- September 2016(102)
- August 2016(168)
- July 2016(141)
- June 2016(149)
- May 2016(117)
- April 2016(59)
- March 2016(85)
- February 2016(153)
- December 2015(150)