#Technologyadoption

A new study has uncovered a surprising trend: employees who openly acknowledge using artificial intelligence (AI) at work are often trusted less by colleagues and clients than those who keep silent. This finding, featured in a recent report on The Conversation, challenges widely held assumptions that transparency around technology use breeds confidence and goodwill in professional settings.

This insight is particularly significant for Thai professionals and organizations as businesses across the kingdom increasingly embrace AI-powered solutions—from customer service chatbots and automated translation tools to advanced data analytics. In many workplaces, staff are encouraged to adopt AI as part of digital transformation agendas and the nation’s push toward Thailand 4.0, where technology and innovation drive economic growth. Yet, according to the latest findings, revealing reliance on such tools could undermine interpersonal trust, an essential element in many Thai office cultures that value harmony, respect, and personal relationships.

A new study finds that employees who openly acknowledge using artificial intelligence at work are often trusted less by colleagues and clients than those who do not mention it. The research challenges the assumption that transparency about technology use automatically builds confidence.

This insight is timely for Thai professionals as businesses across the country increasingly deploy AI-powered solutions—from customer service chatbots to automated translation and data analytics. As Thai organizations pursue digital transformation and national initiatives like Thailand 4.0, the findings raise questions about how transparency around AI affects workplace relationships and teamwork.

A new framework released in early May by a leading fintech group product manager is making waves throughout the global business and technology communities, urging organizations to reconsider the automatic use of large language models (LLMs) for every artificial intelligence (AI) application. The article, recently published by VentureBeat, cautions that LLMs—despite their popularity—are not always the best fit for all customer needs and often prove costly and imprecise compared to other machine learning (ML) or rules-based solutions (VentureBeat).

A new framework released in early May by a leading fintech product manager is sparking debate in global business and tech circles. It urges organizations to pause before automatically adopting large language models (LLMs) for every AI need. The piece, summarized by VentureBeat, notes that while LLMs are popular, they are not always the best fit, and can be costly and less precise than alternative machine learning (ML) or rules-based solutions.