How organizations in the region are approaching GenAI – from transformation to ethical implementation – and beyond.
Generative AI (GenAI) is rapidly transforming Southeast Asia’s technological landscape, disrupting norms, automating processes, and surpassing conventional innovation. It is revolutionizing traditional industries and nurturing new, innovative startups.
According to Gartner, by 2026, over 80% of enterprises will have incorporated GenAI application programming interfaces (APIs) or models, and/or implemented GenAI-enabled applications in operational settings, a significant increase from the less than 5% recorded in 2023.
Nonetheless, the swift growth also presents challenges and considerations, encompassing ethical implications, market readiness, and infrastructure support. Besides the opportunities it opens up, GenAI also brings threats to businesses, include the potential for deepfakes, copyright infringements, and other malicious applications aimed at organizations.
CybersecAsia discussed with Jihad Dannawi, APAC General Manager, DataStax, the developments in GenAI in Southeast Asia.
Jihad Dannawi (JD): A key driving factor has been the accessibility to this new technology. ChatGPT, Bard, Dall-E and Copilot, for instance, can be easily accessed by anyone – whether in a professional setting or otherwise. These new generative AI tools have the ability to create and augment content on demand and in real time, providing users with a variety of use cases and problem-solving opportunities.
The new focus is on enabling enterprises to leverage this powerful technology. Their focus hinges on using tools that opens up enterprise data, in a secure way, for generative AI applications to use, learn and evolve from.
How are businesses in Southeast Asia leveraging GenAI to enhance customer experiences while safeguarding against the risks of potential threats?
JD: We’re seeing businesses in Southeast Asia leverage GenAI to enhance creativity, unlock efficiencies by slashing time spent on mundane tasks, and create customer interactions based on personal preferences, on-demand.
However, the surge in content like deepfakes across the world is proof that these technologies are also being exploited for malicious purposes, including misinformation campaigns and privacy violations.
Establishing ethical guidelines and robust detection mechanisms is, therefore, crucial to abuse prevention. In addition, large language models (LLMS) can also be harnessed for cybersecurity protection and for thwarting bad actors, but this rests on handling large-scale similarity queries and query processing efficiently within a database and eliminating the need to transfer large amounts of data.
In what ways do the infrastructural challenges in Southeast Asia influence the adoption and implementation of GenAI technologies by businesses, and what steps are being taken to address these challenges?
JD: Infrastructural challenges it more difficult to:
- Manage overall running and variable costs at scale
- Have complete oversight and understanding of LLM performance
- Improve efficiency and productivity while managing costs
These outcomes can be avoided via tools that simplify cloud-native application development. For instance, many businesses in Southeast Asia would benefit from Apache Cassandra, the open-source NoSQL database behind some of the world’s most impactful applications. But the operational overhead is a turn-off. That can be overcome, however, through multi-cloud NoSQL database that offers a fully managed service, with no infrastructure to manage, like DataStax Astra DB.
For those companies looking to build a GenAI application, identifying the right tools and infrastructure components can be a daunting task. The market is evolving quickly and there are so many new tools available on the market, with more popping up daily. It can be hard to identify which tools are needed for a GenAI project. Organizations need a battle-tested, trusted, reliable product that can help teams build an application without the hassle of configuration and implementation – like RAGStack.
How do government-business collaborations in Southeast Asia contribute to shaping regulatory frameworks for the responsible deployment of GenAI, and what effects do these regulations have on fostering innovation within the region’s technological landscape?
JD: Public-private partnerships are crucial to establishing robust guardrails that further the responsible generative AI project. In Singapore, the GenAI Sandbox for SMEs is one such example of the government stepping in to help local businesses understand and mitigate the risks associated with generative AI.
Meanwhile, DataStax’s recent partnership with Nanyang Polytechnic is also aimed at equipping future talents to immerse themselves in the fundamentals of big data management and, by extension, foster a deep understanding of responsible AI use.
DataStax also believes in building capabilities with other local businesses to prepare them to meaningfully contribute to their nation’s responsible use of generative AI. For example, the company’s recent collaboration with Cambodia’s PTC Computer is enabling Cambodian enterprises build scalable and smart applications that are future-ready and meet evolving business needs. That will equip them to set the agenda more incisively as regulatory frameworks are updated in the coming years.
Looking ahead, what are the key trends or advancements in GenAI that are expected to shape the future of Southeast Asia’s technological landscape?
JD: Firstly, we expect to see more conversations about deeper regulatory GenAI oversight. The pressure isn’t just coming from governments, but if current economic conditions prevail, then concerns about job displacement among workers will likely further demands to rein in generative AI.
We’re also likely to see companies seek out partners that provide holistic and robust support in their AI journey. AI companies that have the acumen and clout to build effective government relationships will position themselves to come out on top in this area.
It could well be that one to three new, big players emerge and serve this need. OpenAI is expected to be the giant, but there’s room for other players to create turnkey, compliant AI solutions. We also expect that SMEs may have more of an edge over bigger counterparts, as their agility may prove to be an advantage in terms of responsible experimentation, innovation, and deployment.
Like elsewhere, Southeast Asia is also grappling with so-called ‘dark LLMs’ that are powered by open-source and uncensored LLMs. They can even come from bad actor states, and can potentially be used for everything from financial fraud and organized crime to bioweapons and terrorism.
Finally, we might be on the cusp of the ‘Instagram moment’ for GenAI apps, where the wheat is separated from the chaff. We’re seeing early signs of this with Alpha Ori disrupting and enabling the multi-billion-dollar international shipping business, with PhysicsWallah using GenAI for EdTech for millions of students, and Skypoint automating healthcare, saving physicians and medical providers up to 10 hours each a week.
