According to Nita, this approach will ensure that AI becomes an enterprise-level initiative and not just one driven by the IT team in isolation.
Jaksic also believes change should be driven from the top down, beginning with the CIO, who must be willing to lead by example.
“As a leader, you must demonstrate your own willingness to embrace the AI and participate in its integration. It inspires confidence and motivates others,” he says.
When dealing with board-level leaders or investors, Sun recommends beginning with use cases that have a clear return on investment. For example, Collectius operates in several markets across Asia Pacific, each with its own language that can make cross-cultural communication difficult. The company has started to use generative AI to help with translating business communications.
Now, with the help of generative AI, Thai colleagues can now more confidently write business correspondence in English, Sun says, noting that use cases such as this are helpful because there is a clear gain with minimal investment.
“It’s obvious. People can see the benefit itself,” he says. “But if you are looking to build an agent for your company, for your specific business context, that requires some effort and resources.”
This advice plays into Nita’s recommendation that CIOs must not forget to consider the overall data maturity of their organization in favor of focusing only on employee change management, which may be a case of losing sight of the forest for the trees.
He emphasizes that the company must have a strong foundation in data processes, stewardship, and governance. Apart from individual employee sentiments, gaps in these areas are a major reason that “the adoption of AI is sometimes not as successful as expected.”
To accelerate AI adoption no matter what stage of data maturity, Nita advises CIOs to take advantage of already established best practices for driving change within that particular organization. “Take them, learn from them, and advance further in the enterprise,” he says.
