TL;DR
AI adoption remains stubbornly low despite high awareness. The reason? We’ve been training the wrong people. While 20% of technical specialists build AI tools, the 80% who hold critical domain knowledge and understand business processes are being left behind. AI bilingualism is a framework that empowers non-technical professionals to become AI overseers in their own domains. This isn’t about learning to code. It’s about the 80% learning to speak AI fluently enough to redesign workflows, govern implementation, and bridge the gap between what engineers build and what businesses actually need.
On November 11, 2025, industry leaders, educators, and SME representatives gathered at Holiday Inn Express & Suites Singapore Novena for something unprecedented: the launch of The AI Factory, touted as the world’s first AI-blended publication. But this wasn’t just another book release. It was a rallying cry for what speakers called “AI bilingualism”: the ability to speak both the language of business and the language of artificial intelligence.
This concept aligns closely with what I’ve been calling an “AI Officer”: the idea that everyone in an organization needs to oversee and govern AI implementation in their domain. While “AI Officer” captures the oversight responsibility, “AI bilingualist” may be the more approachable term for the masses. The core message remains the same: AI capability isn’t just for technical specialists anymore. It’s becoming an essential literacy for every professional who wants to remain relevant in the intelligence economy.
Beyond Competency: The Five-Element Framework
The event’s most compelling insight came from Kevin, one of the book’s authors, who challenged a fundamental assumption about AI education. “Just attending a course doesn’t make you capable,” he stated plainly, before breaking down what he calls the capability framework: a model that explains why 95% of AI projects fail to reach ROI, according to MIT research.
The framework distinguishes between competency and capability:
Competency (what most training provides):
1. Knowledge – Understanding AI concepts
2. Skills – Technical abilities
Capability (what organizations actually need):
3. Tools – Knowing which AI tools to use and how
4. Processes – Redesigning workflows and business processes around AI
5. Mindset – Cultivating digital transformation willingness
“The engineers are not familiar with the processes,” Kevin explained. “It’s the bilinguals that know the processes, the bilinguals that have the skills and the knowledge.” This gap between technical implementation and business process understanding is, according to the panel, why most AI initiatives stumble.
What Is an AI Bilingualist?
The term “AI bilingualism” dominated both panel discussions, with speakers from SMU Academy, upGrad, ASME (Association of Small & Medium Enterprises), and the labour movement converging on a shared vision: professionals who can fluently speak both their domain expertise and the language of AI.
As Jack Lim, Executive Director of SMU Academy, put it: “For us it’s not about turning everyone into data scientists or coders, but it’s about helping business managers and executives understand what AI can solve real problems, improving decision making and drive innovation.”
Ang Yuit, President of ASME, reinforced this with a practical perspective: “In the past you look at enterprise support and trade support. But now, that line starts to get blurred with manpower, training, skills. The SME space becomes a very ripe and very great place to develop the workforce because in the SME space, you get your hands on work done.”
Academia Meets Industry: A Uniquely Singaporean Model
Professor Jay Gonzalez from Golden Gate University (co-author of The AI Factory and architect of the world’s first DBA with specialization in Generative AI) praised Singapore’s ecosystem as a model of “academia in action.”
“You have SMU Academy that’s here and closely connected with government. And closely connected with the SMEs,” he observed. “The SMEs are the essence of the industry majority, right? But they’re small. And when you’re small, you need help. And that’s when you see government standing up and advocating for the small.”
This tripartite collaboration (academia, government, and industry) was on full display. Patrick Tay, an MP and labour leader, shared how the labour movement has been working company-by-company to build AI capabilities, impacting over 100,000 workers across 3,000 companies in the last three years.
“We don’t see this as job killing but really job creation, a new opportunity and a transformation agenda,” Patrick emphasized, introducing the concept of “just transition”: ensuring that as the economy transforms through AI, the change remains fair, responsible, and progressive for workers.
From Automation to Augmentation: The Mindset Shift
Celine Chew, a learning psychologist on the panel, offered crucial insight into the psychological barriers preventing AI adoption: “It’s moving from automation to augmentation. We are so used to using technology to automate things. But now that we’ve got AI and GenAI, it’s a mindset shift where it’s moving to how do we use this AI to augment our ability.”
She continued with a powerful analogy: “How many of you here have been babies? All of us, right? But we were never born being able to walk. We tried, we experimented, we get up, we fall down, we stand up again. It’s the same mindset with AI: if you fail, okay, move it away and then start again.”
The 80% Problem
Perhaps the most striking observation came from Kevin’s challenge to the audience: “It’s so sad that 80% is feeling that they’re going to lose their jobs. This 80% is going to be the AI bilingualists. Tell me which app is built by just a developer today that’s working? Why is AI failing? It’s because the person who knows the content refused to cooperate.”
He continued: “No AI is going to be working very well without your involvement because you hold the subject matter expertise. You don’t need to know how to program, but you still need to have the full capability framework.”
A Platform for Non-Technical Builders
The AI Factory comes with a companion platform (the Capabara Tool Builder) which recently won the Meta Llama Incubator programme, beating 25 startups. The platform allows non-technical professionals to design, test, and deploy custom AI agents without coding.
“When a non-tech guy develops his first app, it’s like you see that new sense of empowerment in them, smiling, gleaming all the way,” Kevin shared. “We convert all of them to take the next few courses because they want to learn how to do it for all the different departments: HR, finance, compliance, marketing, customer service.”
The Path Forward: Start Small, Start Soon
If there was a unified message from the diverse panel, it was this: Don’t wait.
Rohit from upGrad put it succinctly: “Start small, but start soon. Don’t delay that decision. When we start implementing initial projects, most organizations discover a hidden AI muscle that they never knew they had.”
Ang Yuit from ASME echoed: “Start early, start small.”
And Patrick Tay summarized the workforce readiness approach in three words: “Ready, relevant and resilient. Ready for the new skills, relevant for the new jobs, resilient for the new changes.”
The Human Element Remains Central
Throughout both panels, speakers returned consistently to one theme: AI is a tool for human augmentation, not replacement. The “human in the loop” isn’t just a safety feature, it’s the entire point.
As Dr. Sumitra, the moderator, synthesized: “AI is not just a technological shift, it’s a psychological shift. We really need to understand that AI is an augmenting tool, not a tool which is going to replace, and there is no fear about it.”
Conclusion
At Kerson.ai, we believe in making AI accessible to everyone, not just developers and data scientists. Like the vision articulated at The AI Factory launch, we’re committed to creating tools and education that empower the 80% of professionals who hold crucial domain expertise but may lack technical training.
The future belongs to the bilingualists: those who can bridge business wisdom with AI capability. And that future is being built right now, one small step, one experiment, one augmented decision at a time.
Want to become an AI bilingualist? Learn more about how Kerson.ai can help your organization build its own AI factory at kerson.ai.
The AI Factory is now available for purchase. The book comes with free access to the Capabara Tool Builder platform, enabling readers to immediately apply the frameworks and build their own AI solutions: no coding required.
