Generative AI (Gen AI) is often hailed as a transformative game-changer. While it is still in the early days, it has become abundantly clear that Gen AI will reshape industry structures, business operations, and human interactions.
While there has been significant concern about the challenges Gen AI might present, inaction poses a far greater risk. President Tharman Shanmugaratnam captured this sentiment succinctly when he exhorted policymakers at the recent Asia Tech X Summit to avoid seeking perfection, and to approach AI regulation as the art of the possible, the attainable, and the next best.
What lessons can companies draw in developing their own approaches to AI and Gen AI?
Governance as the bedrock of responsible innovation
As companies continue to experiment with the use of AI, it is imperative for business leaders to carefully consider governance frameworks to manage material risks around using AI, which would vary across industries and enterprises. Venturing into AI without considering its potential ramifications for customers, employees, and the broader community would be reckless.
Effective guardrails are crucial in helping companies use AI in an ethical and responsible manner. For instance, we employ the PURE framework to guide our AI use cases, ensuring they are purposeful, unsurprising, respectful, and explainable. Additionally, we have established a senior-level, cross-functional committee to deliberate on ambiguous cases, ensuring adherence to the highest legal and ethical standards.
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Companies should also actively engage in public-private collaborations dedicated to developing industry-specific AI governance frameworks. The Monetary Authority of Singapore (MAS) partnered with banks and financial institutions to introduce the Principles to Promote Fairness, Ethics, Accountability, and Transparency (FEAT Principles) in 2018, providing guidelines for the responsible use of AI and data analytics within the financial sector. Building on this foundation, MAS has worked with the industry on the Veritas Initiative to help financial institutions evaluate their AI initiatives against the FEAT Principles, and on Project MindForge, which aims to apply similar standards to Gen AI solutions.
The Singapore Government has also developed several resources to help smaller enterprises kickstart their AI governance journeys. The AI Verify toolkit, collaboratively developed by the public and private sectors, enables businesses to test and validate their AI products against international best practices and standards. Furthermore, the recently launched Model AI Governance Framework for Gen AI further outlines nine dimensions to address concerns around Gen AI while fostering innovation and offers practical suggestions for stakeholders to develop a trusted AI ecosystem.
Preparing for the future of work
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As companies adopt AI, it is equally important to ensure that employees are not left behind. AI will inevitably change the nature of work, including the types of jobs and skills that will be in demand.
Upskilling, now more than ever, must be an urgent priority for all organisations. Many economists and HR experts predict that AI will become a basic skillset for most jobs sooner rather than later, with every employee expected to have at least a basic understanding of how AI works. Additionally, Singapore has articulated a target of tripling its AI workforce to 15,000 over the next few years under the refreshed National AI Strategy.
While workers can take individual ownership of acquiring these new skills, such as by utilising their SkillsFuture Credits, companies also have a responsibility to prepare their people for this impending reality. Making AI training less daunting and more engaging is a perennial challenge, but we find that gamification can be an effective technique. In 2020, we partnered with Amazon Web Services (AWS) to run the DBS DeepRacer League, riding on the hype around F1 to help over 3,000 employees acquire AI skills in a fun, gamified manner.
But even as the workforce continues to develop and evolve, organisations must structure themselves to adapt more nimbly to changes. In a Gen AI-powered world, where employees at all levels truly have any information they need at their fingertips, traditional top-down management hierarchies will likely become less relevant.
We believe that horizontal ways of working, where employees are organised around shared outcomes instead of functional silos, are the way forward. How this looks on the ground would vary across industry and company, but one useful rubric is to focus on the customer. One possible modality is what we call at DBS, “Managing through Journeys”, with about 70% of the bank organised around specific customer journeys. Each journey is managed by a squad comprising people with the necessary skillsets, expertise and experience to work off unified data dashboards, share the same workflows and workbenches, and drive towards a common set of KPIs. By leveraging such horizontal ways of working, combined with technological capabilities including AI, companies will be in a better position to deliver differentiated customer outcomes in a more agile manner without sacrificing efficiency.
Being in the driving seat of change
When the wheels of change are in motion, the only way to move is forward. The same can be said for AI and Gen AI. Staying on the sidelines is not an option, but being in the driver’s seat affords us some control in determining that the end outcomes are positive. The key then lies in finding that sweet spot for approaching innovation responsibly.
Lim Him Chuan is the group head of Strategy, Transformation, Analytics and Research at DBS
