According to a recent global report, most businesses agree that generative AI (gen AI) will have a substantial impact on their industry. However, despite the fanfare, the adoption rate of the technology remained abysmally low for businesses. While three quarters of the surveyed companies had experimented with gen AI, only a lowly 9 percent admitted to having adopted the technology.
Based on a survey conducted by MIT Technology Review Insights and telecom company Telstra, the report covered 300 C-suite executives, and vice presidents or directors, across Asia-Pacific, the Americas and Europe.
The report questions the participants on how their companies are using, or plan to use, gen AI technologies. In addition to gauging the interest in the technology, the report also highlights the barriers to the adoption of gen AI in businesses.
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“There is a misconception about how easy it is to run mature, enterprise-ready, generative AI,” said Stela Solar, inaugural director at Australia’s National Artificial Intelligence Centre, in the report.
Uncharted territory
Agreeing with Solar’s assessment, Brian Prince, founder & CEO at Top AI Tools, says investing in gen AI can be expensive. He argues that beyond the software itself, businesses will likely need to also invest in training, infrastructure upgrades and ongoing maintenance. This is especially true for small to medium enterprises, who may find it challenging to allocate sufficient budgets without guaranteed short-term returns.
“As the Telstra survey showed, skills, training and company culture are major impediments to IT adoption,” says Prince. “Integrating AI into existing workflows and systems can be complex.”
Speaking from his experience, Paolo Danese, CEO at Storya, says he has observed a tangible disconnect between the theoretical allure of gen AI and the practical realities faced by businesses.
“This divergence is not just about the technology itself but encompasses broader concerns such as the readiness of IT infrastructure, the steep learning curve for effective deployment, and the ethical considerations surrounding AI use,” says Danese.
Building on this, Sukhdeep Sohal, senior consultant at Glue Affinity Reply, says businesses’ apprehension towards gen AI can often be traced back to a series of strategic and operational disconnects. He believes the crux of the issue lies in the alignment, or lack thereof, between AI initiatives and overarching business strategies.
“When AI strategies are perceived as standalone experiments rather than integral parts of business growth, the disconnect breeds reluctance,” says Sohal. “Additionally, the delivery of AI foundations in piecemeal, rather than holistically, fails to instil confidence in the technology’s comprehensive benefits.”
Evolving ecosystem
The report highlighted the evolving regulatory, compliance and data privacy environment as a leading non-technological barrier to rapid gen AI adoption in business.
Danese agrees, saying businesses are navigating uncharted waters, and need to balance the push for digital transformation while adhering to strict privacy standards.
According to Prince, this is especially true in sectors like healthcare, finance and legal, where data sensitivity is a critical concern. “The burden of compliance becomes a significant deterrent,” he says.
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Peter John Alexander, chief business officer at MeetKai, highlights other non-technical factors businesses must take into consideration. He says companies likely and should fear potential data breaches, ethical misuse, high implementation and usage costs, unreliable output, and the risk of falling behind competitors.
“These apprehensions highlight the need for careful consideration of the benefits and risks of generative AI deployment, alongside proactive measures to address security, compliance, ethical and operational challenges,” says Alexander.
Using AI in business
According to the report, 78 percent of businesses look at gen AI as a competitive opportunity. Many expect their number of functions deploying gen AI to more than double in 2024.
In terms of actual use, 65 percent respondents say their businesses are “actively considering new and innovative ways to use gen AI to unlock hidden opportunities from our data”.
This doesn’t surprise Sohal. He says gen AI, when aligned with a well-articulated strategy, fits best where it can leverage the ‘predictive power in data streams’ identified through rigorous data audits. “This suggests that analysis, especially predictive analytics, is a prime area of application, as it directly amplifies strategic decision-making capabilities.”
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Alexander agrees, saying AI excels at rapidly analyzing vast amounts of data to surface patterns, making predictions, and uncovering actionable insights. This, he says, could include customer analytics to identify trends and segments, financial analysis and forecasting, operations optimization, and more. “By leveraging AI, businesses can make smarter, data-driven decisions much faster than manual analysis,” says Alexander.
According to the report, rather than being confined to specific sectors, gen AI is likely to transform specific functions across the economy, such as customer service. It also points to a 2023 National Bureau of Economic Research working paper that predicts gen AI customer service could raise productivity by 14 percent.
“Generative AI’s prowess in natural language understanding and generation positions it as a linchpin in customer service revolution, offering personalized, efficient and engaging user experiences,” says Danese.
To each his own
Alexander believes businesses must approach gen AI as a transformative business capability, not just a scattershot set of projects.
Read: Meta rolls out new generative AI features in UAE and Saudi
“Ultimately, the best fit for gen AI depends on each company’s unique needs, available data and strategic priorities,” says Alexander. “Companies should align AI initiatives with overarching business goals, start with focused experiments to prove value, and scale up successes.”
To ensure readiness and maximize benefits, Sohal suggests organizations should seek out use cases depending on their level of AI maturity. Additionally, he recommends businesses should also look at industry precedents to validate and inform their approach.
“This integrative strategy promises to yield tangible business value, elevating gen AI from a technological experiment to a cornerstone of enterprise capability,” says Sohal.
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