A global study by MIT Technology Review Insights (MITTR) has found that while most businesses are seeking to disrupt their industries using generative artificial intelligence (AI), 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.

Telstra International said in a statement on Monday that those with the most experience of 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.

The study included the following key findings:

1. Executives expect generative AI to disrupt industries across economies.
Overall, six out of ten respondents agree that generative AI technology will substantially disrupt their industry over the next five years. Despite inevitable variations, respondents that foresee disruption exceed those that do not across every industry.

2. Majority do not see AI disruption as a risk and instead hope to be disruptors.
Rather than being concerned, 78 percent of respondents see generative AI as a competitive opportunity. Just 8 percent regard it as a threat.
Most hope to become disruptors with 65 percent say their businesses are actively considering new and innovative ways to use generative AI to unlock hidden opportunities from data.

3. Despite expectations of change, few companies went beyond experimentation with, or limited adoption of, generative AI in 2023.
Although most (76 percent) companies surveyed had worked with generative AI in some way in 2023, few (9 percent) 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.

4. 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 end-2024.
Meanwhile, respondents plan to increase use of generative AI in specific fields relevant to their individual industries. These areas include coding for IT firms, supply change management in logistics, and compliance in financial services.

5. Companies need to address IT deficiencies or risk falling short of their generative AI ambitions.
Fewer than 30 percent 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 percent) say their available hardware is, at best, modestly conducive to rapid adoption.

6. 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 percent of respondents cite their regulatory, compliance, and data privacy environment as a leading barrier to rapid AI adoption.
Budgets: 56 percent 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.

Commenting on the state of generative AI in Singapore, Laurence Liew, Director of AI Innovation, 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,” he added.

He also said companies face a dilemma in accessing the necessary hardware today.

According to him, choices include outright purchase and pay-as-you-go outsourcing, both of which carry their own risks. Additionally, data quality, storage and talent remain bottlenecks for effective deployment.

“At AI Singapore, we try to address the issues of AI talent with programs such as the AI Apprenticeship Program (AIAP) and the LLM Application Developer Program (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,” he added.

The report for the study was produced in partnership with Telstra International, a global arm of leading telecommunications and technology company Telstra.

MITTR polled 300 business leaders across Asia-Pacific, the Americas, and Europe on how their organizations 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, noted this global study sheds light on companies’ readiness to tackle the challenges to effective adoption of generative AI.

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