SINGAPORE - Media OutReach Newswire - 10 June 2026 - OOm Institute, a Singapore-based AI and digital skills training provider, is calling for greater AI fluency as businesses adopt Generative AI and concerns grow over declining critical thinking and verification skills.
The rapid adoption of AI tools without sufficient verification, contextual understanding, or critical oversight is contributing to a growing "Human Critical Thinking Gap".
Recent research from Professors Rick Dakan and Joseph Feller also corroborates this; only 8.7% of participants consistently verified high-stakes AI-generated claims before accepting them.
Beyond the Prompt: The Human-Centric Shift
While prompt engineering remains a foundational skill for today's workforce, OOm Institute suggests the discipline must evolve beyond simple input mechanics.
"We are entering a false competence trap," says Ian Cheow, CEO at OOm Institute. "People are learning how to prompt, but they aren't learning how to make the right decisions. If you cannot spot when an AI's logic fails, you aren't using a tool, you are delegating your intelligence."
The Warning: If You Let AI Think for You, You "De-skill"
Professionals who rely heavily on AI without developing critical evaluation skills risk "de-skilling", where domain expertise erodes over time due to over-dependence on automated systems.
The concept of AI Fluency is built around three core capabilities:
- Decision to Correct: The ability to determine when AI-generated output is sufficient and when it poses a risk that requires human revision or rejection.
- Contextual Sovereignty: Ensuring human oversight remains central so AI outputs align with Singapore's cultural, commercial, and ethical contexts.
- Critical Inquiry: Moving beyond prompting to actively question assumptions, logic, accuracy, and completeness in AI-generated responses.
Real-World Practice vs Theory
As AI tools evolve rapidly, practitioners argue that effective AI capability cannot rely solely on static classroom theory. Real-world usage often involves changing workflows, unpredictable outputs, and context-specific decision-making that require continuous practical application and human judgment.
Maintaining AI fluency increasingly requires learning from practitioners actively applying these tools in commercial environments.
"At OOm Institute, our focus is on building practical decision-making in AI usage," Mr Cheow added. "Our goal is to help professionals use AI with stronger critical thinking, clearer accountability, and practical business understanding."
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OOm Institute
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As AI cuts production costs and multiplies the output of small agency teams, a new model is emerging in Singapore: revenue-sharing, where the agency earns only when the client sells.
SINGAPORE – Media OutReach Newswire – 14 September 2026 – For decades, the marketing agency business model has rested on one assumption: producing campaigns is expensive, so clients must pay fixed monthly retainers regardless of results. Artificial intelligence is dismantling that assumption from two directions at once — cutting what it costs an agency to operate, while multiplying what a small team can produce.
The savings start in places clients never see. Administrative work that once consumed billable hours — invoicing and quotations, client reporting, campaign documentation, scheduling and follow-ups — can now run largely on AI systems, stripping overhead out of every account an agency manages.
The bigger shift is in production itself. In video, AI now handles the foundation of the edit — assembling rough cuts, syncing footage, generating captions and preparing ad-ready variations — before a human editor applies the judgment, pacing and finishing that machines can't. What once took an editor days takes hours. The same applies to design and ad creatives. The result: a small team's production capacity multiplies while its cost per campaign falls.
That double effect — lower cost, higher capacity — changes what an agency can afford to offer. When servicing an account no longer carries heavy fixed costs, those economics can be passed on to the customer: instead of a large retainer that pays for activity, the agency can charge a minimal base fee and earn its real income as a share of the sales its campaigns generate.
Singapore agency touchmkt has built its business on this model, charging SME clients a low base retainer that roughly covers production, then earning 5–9% of the revenue attributed to its campaigns — tracked through conversion tools that tie each sale back to the ads that produced it.
"The economics simply didn't work before," said Charlie Tan, founder of touchmkt. "If every account carries thousands of dollars in production and admin costs, you have to charge for it whether the campaigns perform or not. When AI takes out the cost and multiplies the output, you can afford to be paid on outcomes. For the first time, the agency's goals and the client's goals are actually aligned — we only do well when they sell."
The model does demand discipline: rigorous attribution, and the agency absorbing the risk of campaigns that underperform. But for small businesses long wary of paying retainers for activity rather than results, the direction is clear — AI hasn't just changed how marketing is made; it's beginning to change how it's paid for.
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Touchmarket LLP
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