AI technologies have advanced at a dizzying pace over the past two years, but workforces at many enterprises remain hesitant to adopt them.
A common assumption is that employees are reluctant to use AI-powered tools out of fear that these solutions will out-perform their own work, and in time, make them replaceable. While job displacement is certainly a concern, the reasons for this hesitancy are much more complex.
QiFang Sun, CIO at Collectius, a Singapore-based debt management company, frames the hesitancy on a continuum of two dimensions: willingness and skill.
While some employees may be willing to use AI, they may not have the necessary skill; others may have the skill, but not the willingness. For organizations, the goal, Sun says, is to find — or develop — employees that meet both criteria: They have the willingness and skill to use AI.
But doing so is challenging, given the starting point most IT leaders face in terms of typical employee beliefs around AI in the workplace.
“There are two extremes. One end is they think [that AI is] so powerful that it’s going to replace the humans — that they are afraid that they might lose a job,” Sun says. “And the other end is people view it as a black box. They don’t see the explainability and they don’t trust it.”
Bogdan Nita, CIO at Singapore-based World Vision International, has witnessed similar black-and-white thinking around AI among end-users and IT leaders alike.
“[Artificial intelligence is either] perceived as a magic wand: You just apply AI and suddenly your data — although it might be not accurate, consistent, reliable — suddenly becomes the opposite, or it’s something that is perceived as scary,” he says.
KEO International Consultants’ Damir Jaksic has navigated such AI reluctance firsthand. Based out of Dubai, the CIO says the planning, design, engineering, and project management consultancy’s architects, interior designers, and other professionals have expressed reservations about AI as a threat to their own execution.
“In our field of architecture and design, creativity is highly valued. Architects, designers, and interior designers feel that AI-powered tools would stifle their creative expression or undermine the uniqueness of their design,” he says.
The key to overcoming this issue, Jaksic says, is to educate employees on how AI works to help them understand it is complementary rather than supplementary to their role.
To this end, Jaksic tries to show KEO employees that AI can help them with higher-value work, which is essential to their industry: Although half the world’s population is expected to be living in urban areas by 2050, there is a shortage of qualified professionals to help design, architect, and build that real estate.
“AI is coming up at the right time in my view to help increase productivity,” he says.
Under Jaksic’s leadership, KEO has already found several use cases that aid efficiency without diminishing creativity. For example, concept renders, which once took dozens of staff hours to produce, can be done exponentially faster with the aid of an AI art generator, like Midjourney or Stable Diffusion — all while still under the skillful direction of an actual professional, he says.
Education is step 1
Forrester CIO Michael Kasparian is another proponent of taking an education-first approach to preparing employees to take advantage of AI in the workplace.
Although Kasparian says Forrester as an organization has been relatively open to using AI, employees have expressed a desire for proof and transparency in order to help explain the “why” behind AI recommendations, predictions, and outputs.
Kasparian uses the example of stakeholders wanting to understand the basis of a client churn recommendation: “Is it that they’re not active with us? Is it that they’re not engaged? And then ultimately: What can they do with it? I think where folks struggled initially was like, ‘You’re saying it’s low, but I think they’re doing great,’ so why, right? And so being able to have the proof behind it is important with any of the models,” he says.
Kasparian adds that employees at Forrester are also doing a lot of work around generative AI, such as content creation and transcript summarization. When encouraging employees to use generative AI, Kasparian emphasizes the efficiency that can be gained from the tools.
At the same time, Kasparian reminds these employees that the content is not reproducible. “If you ask the same question twice, you might get varying answers,” he says, adding that hallucinations can also be an issue.
“Our coaching to folks has been like, ‘Look, it’s a great way to create a draft email or summarize a transcript from a call, but you got to look at it. Don’t just take it at face value,’” he says.
Encouraging employees to use output from generative AI as a foundation for their own higher-level work can help drive greater productivity, Kasparian says. This message is central to how he and his team have pitched generative AI to employees.
“Use [generative AI] as a draft and then make it your own. And so you’re taking a lot of the initial manual work out of it, but then allowing the end user to ultimately then cater the message to their tone or wording,” he says. “It gets them 80% of the way there, and so I think folks have seen a lot of value in that.”
Like Kasparian, Collectius’ Sun emphasizes how AI can help with productivity on a day-to-day basis. He also takes a big-picture view by explaining how use of AI can add up to better work-life balance.
“If you want to convince the staff, the most important thing is to tell them that it’s going to bring you work-life balance,” he says. “[AI] can help you and save you from the mundane daily routine jobs and required business as usual.”
This kind of education can help facilitate AI adoption. Employees who better see into the black box that AI once was to them will be more likely to consider its output in any decision-making process. Similarly, employees who understand that AI workflows are still a human-assisted process will be more likely to embrace solutions that aid — but don’t obviate — their own labor.
Upskilling with intent
If willingness and skill are the two main dimensions that influence hesitancy toward AI, employees who question whether taking the time to learn the technology is worth the effort are at the intersection.
These employees often believe the AI learning curve is too steep to justify embarking on in the first place, he notes.
“People perceive that AI is something complex, probably because of all of these movies. They worry: Will they have time and effort to learn these new skills and to adapt to these new systems?” Jaksic says.
This challenge is not unique to AI, he adds. “We all prefer familiar ways of working, and we don’t like to disrupt our established day-to-day activities,” he says.
Perhaps the best inroads then is to show that learning enough about AI to use it productively does not require a monumental investment. To this end, Jaksic has structured a formal program at KEO for AI education in bite-size segments.
The program, known as Summer of Innovation, is organized around lunchtime sessions taught by senior leaders around high-level AI concepts. The goal of these sessions is to provide enough context that employees can comfortably experiment with AI on their own.
In addition to instruction, there is also recognition.
“One of the most important parts is recognizing and rewarding innovation [by] acknowledging employees who embrace AI. [This is to] incentivize others to follow suit,” says Jaksic, who advises doing so in a public ceremony.
World Vision International’s Nita has also implemented a formal program, called AI Academy. The goal of AI Academy is to teach employees about the basics of AI, including how it works, what jargon such as machine learning or natural language processing really mean, and most importantly, how they can use AI in their business context.
To ensure lessons land, Nita advises framing discussions to fit the perspective of the learners.
“For example, when we try to upskill our finance people, we always use models from the finance space,” he says. “It’s always very important to know your target audience and make sure you adjust the context to respond to that specific target audience.”
Campaigning for AI company-wide
In addition to employee education and upskilling, CIOs must rally support for AI across the enterprise. For Nita, this means support must be driven from the top down.
“Everything starts with the top. If you don’t have the right buy-in at the top-level organization — starting even with the board of directors, but then moving to the executive leadership team — below that level, nothing will ever happen,” he says.
To galvanize support from top-level leadership, Nita advises CIOs to look for proof points that can help build organizational momentum.
“Never ask for a 20 million [SGD] investment without proving the value that will be generated with that investment,” he says. “So start small with the quality experiments, pilot, [or] prototypes that will show value. And it’s not the value of technology — it’s the value that it delivers to your business.”
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.
