Most Businesses Bullish About Using Generative AI To Disrupt
- By Paul Mah
- March 06, 2024
Most businesses are seeking to disrupt their industries using generative AI, but only a small proportion believe they have what it takes to successfully leverage AI today.
This was one finding of a new global study conducted by MIT Technology Review Insights, commissioned by Telstra International.
The study found that a majority (78%) of surveyed businesses see generative AI as a competitive opportunity. Indeed, 65% of them say their businesses are actively considering new and innovative ways to use generative AI to unlock hidden opportunities from data.
Yet the study also found that less than 30% believe that their companies have the right level of technology and other attributes such as funding, culture, and skills to support its rapid adoption.
Generative AI will disrupt
Overall, six out of 10 respondents agree that generative AI technology will substantially disrupt their industry over the next five years. But despite expectations of change, few companies have gone beyond limited adoption or experimentation with generative AI in 2023.
The most common use case was automating non-essential tasks – which the report described as “a low-to-modest-gain, but minimal-risk usage of the technology”.
Companies have ambitious plans to increase AI adoption in 2024, however. According to the report, some areas include coding for IT firms, supply change management in logistics, and compliance in financial services.
The budget conundrum
Perhaps unsurprisingly, 56% of those interviewed cited IT investment budgets as a leading barrier. At a media briefing to discuss the findings from the study, Chris Levanes, who heads marketing for Telstra in South Asia noted that they might have a valid point.
“As you start to look at AI adoption, you've got to make sure you've got the right infrastructure in place. That you are capturing the right amount of data and analyzing that data.”
“You're adding that into your large learning models. You don't want to under-invest because you could risk having to spend money [again] and ultimately end up being disrupted versus being a disrupter.”
But a lot of work remains to be done.
“[Many organizations] are still sitting on static data, thinking about it from a structured and unstructured perspective. They've not even yet shifted gears into thinking about data in motion, thinking about data beyond their corporate borders, and about value creation, let alone getting into this concept of AI and this proliferation of the hyper-connected landscape,” said Levanes.
Setting the stage for AI
Laurence Liew, the director of AI Innovation – who was also at the briefing – observed that Singapore is still in the early stages of adopting generative AI. He noted that effective implementation of generative AI includes 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 own risks. Additionally, data quality, storage, and talent remain bottlenecks for effective deployment,” said Liew.
Ultimately, there is no question that businesses that can successfully leverage generative AI will come out ahead.
Geraldine Kor, the managing director of South Asia and head of Global Enterprise at Telstra International, noted: “As the world becomes increasingly digitized and human-to-machine interactions flourish, being able to process data to drive[ informed real-time or near real-time business decisions is paramo]()unt.”
“When implemented successfully, this proficiency will be a game-changer for most organizations, and will distinguish leaders from followers. However, building end-to-end capabilities to handle large datasets, accurately contextualize the data for business value and ensure the responsible and ethical application of AI is extremely challenging.”
The full report can be downloaded here (free registration).
Image credit: iStockphoto/wildpixel
Paul Mah
Paul Mah is the editor of DSAITrends, where he report on the latest developments in data science and AI. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose.