{
  "id": 12211750,
  "title": "Trust is the real AI advantage",
  "url": "https://urgent.news/2026/10/05/trust-is-the-real-ai-advantage",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-05T19:08:00.000Z",
  "source": {
    "name": "Straits Times Business",
    "slug": "straits-times-business",
    "url": "https://www.straitstimes.com/business/trust-is-the-real-ai-advantage"
  },
  "original_language": "en",
  "account": "The prevailing belief is that AI success hinges on speed. However, as models become more accessible, competitive edge will arise from responsible implementation and trust. Trust matters more than AI intelligence itself. Many organizations began AI adoption with experimentation, building proofs of concept and gaining confidence. Yet, this created a false sense of ease in real-world deployment. Financial services, in particular, demand higher standards for security, explainability, accountability, and trustworthiness before scaling. Trust and responsible AI might seem contradictory, as governance is often viewed as stifling innovation. But in reality, trust enables innovation by fostering adoption. Without trust, employees, customers, and regulators all impose more controls, making a promising pilot just another initiative without real change. Responsible AI involves designing systems with accountability, transparency, human oversight, privacy, security, and fairness from the ground up. It means establishing ownership, testing procedures, data handling, exception management, and human intervention paths. Though initial trust-building may appear slower, it avoids later adoption hurdles, risk management, and retroactive controls. This isn't about balancing speed against trust; it's about integrating trust to achieve sustainable speed. In Asia, AI adoption is visible but not unconditional. A Visa study found that while 74% of consumers use AI for product discovery, 32% are hesitant to share personal information. Similarly, UOB's consumer sentiment study revealed 79% of individuals utilize AI for financial matters weekly, but only 62% trust AI to act in their best financial interests. Consumers increasingly expect AI to positively impact their financial relationships more than in other sectors. Building trust in AI agents relies on transparency, human validation, and explainability. Consumers will welcome speed and convenience from AI, but only if they have control, clarity, and accountability. Organizations must also reconsider AI investment measurement. While return on investment is crucial, it can distract from innovation if every benefit must be immediately observable in productivity metrics. Trust, resilience, explainability, employee confidence, and regulatory readiness might be harder to quantify but are pivotal in determining whether an AI capability transitions from a pilot to enterprise-wide scaling. Organizations should incorporate these qualitative factors into business cases. Measuring success beyond cost savings and revenue growth is necessary. UOB's strategy focuses on putting people first and leveraging technology to augment judgment, not replace responsibility. AI should enhance decision-making, not replace it. At UOB, AI readiness begins with real-world workflows, clear pain points, and daily user involvement. This approach, seen in UOB FinLab initiatives like AI Ready and Elevate, emphasizes starting with a problem, not a model. Internally, UOB prioritizes AI safety and scalability through enterprise data foundations, governance, stewardship, secure platforms, and employee training, including Microsoft Copilot for about 30,000 employees. While technology is vital, people's understanding of AI's capabilities and limits are equally important. A forthcoming UOB Asean Insights report, \"Trusted by Design: From AI Experimentation to Scaled Adoption,\" underscores that the gap between successful pilots and scaled adoption lies not in the model but in the operating model around it: workflows, governance, people readiness, leadership sponsorship, and ecosystem partnerships. These elements transform AI from a demonstration into a lasting capability. The AI landscape is in a new phase, shifting from experimentation to scaled deployment.",
  "summary": "Why responsible artificial intelligence should be treated as an enabler, not a brake.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}