The adoption of generative artificial intelligence (GenAI) is one of the top three business priorities for Thai executives, according to a study commissioned by Salesforce, a global customer relations management company.
"Despite the volatile macroeconomic environment, we've got the right ingredients to push AI adoption, with national investments in AI infrastructure through the Thailand National AI Strategy and strong interest from Thai businesses in embracing the technology," Siddharth Rastogi, vice-president of Salesforce Asean, told the Bangkok Post.
The company's research found corporate executives in Thailand are bullish about integrating GenAI into their operations, he said.
Among the various priorities they juggle, a significant majority of respondents (84%) indicated GenAI is a top business priority and will be critical to their success over the next three years, according to an online survey of 225 leaders from large businesses across Thailand carried out from July 22-24.
Mr Rastogi said there is great potential for an autonomous AI future in Thailand.
"In our research, executives expressed confidence in trusting AI to handle tasks autonomously and 100% of them stated they would delegate at least one of the surveyed tasks to AI alone [without human oversight] in the next three years," he said.
Thitirat Tongtavorn, country leader of Salesforce Thailand, said 58% of respondents said their organisation had a clear and defined GenAI strategy, while a further 38% said they had started work on a GenAI strategy for their business.
Three motivations driving GenAI adoption were customer expectations for faster/more personalised experiences (44%), employee demand to introduce GenAI tools (44%), and bringing innovative customer and/or employee experiences to market (41%).
However, 96% of the executives believed there were still barriers to the adoption of GenAI in their business.
Among the barriers were concerns that GenAI produces inaccurate outputs (29%), a lack of skill-building or training opportunities (29%), the use of incomplete customer/company data to train AI models (28%) and a lack of data privacy and security (28%).