A recent SAS survey showed APAC organizations are facing GenAI challenges such as a lack of a clear GenAI strategy, insufficient data and inadequate tools. Intelligent CIO put questions to Deepak Ramanathan, Vice President, Global Technology Practice, SAS, that deep dive into the survey’s findings.
1. How do you foresee GenAI impacting the economic landscape of your industry within APAC region?
The integration of GenAI among organisations in APAC is poised to radically boost the economic landscape by enhancing productivity, driving substantial growth and fostering innovation across various industries. Businesses that adopt GenAI gain a competitive edge in markets.
According to IDC, Gen AI spending is forecast to reach $26 billion by 2027, with a 30% higher adoption rate in developing economies compared to developed ones. Integrating GenAI into governance, risk and compliance (GRC) systems is crucial, as failing to do so could lead to missed opportunities in automating tasks like policy creation and handling controls, potentially putting organisations at risk of non-compliance with evolving regulations. That being said, it is also important for organisations to proceed cautiously, adhering to privacy protocols.
2. With 94% of APAC organisations planning to invest in GenAI next year, but only 10% of organisations are fully using and implementing GenAI in APAC – how do you propose organisations should allocate their budget for GenAI initiatives?
To harness the full capabilities of GenAI, data infrastructure is crucial. A comprehensive strategy for GenAI includes customized business applications that provide value for customers while adhering to the principle of maintaining a balance between development and the welfare of society. For instance, the benefits of GenAI can be in the areas of content production (30%) in outdoor and indoor marketing campaigns, software development (10-20%), intelligent AI assistants, and consumer interaction. Businesses should customize their approach to best match their strategic goals by developing their own GenAI models or using third-party GenAI solutions – depending on their resources and time restrictions.
The use of GenAI doesn’t come without its challenges. GenAI models can inherit bias, which if left unchecked, could lead to unfair discriminatory outcomes. Furthermore, the decision-making processes of GenAI models can be opaque. This lack of explainability and transparency can be problematic, especially when dealing with sensitive data.
3. The survey indicates a lack of understanding of GenAI among leaders and data privacy and security are major concerns for organisations adopting GenAI – what measures can organisations take to address these issues?
Before we can discuss AI ethics, we need to talk about data ethics as AI decisions are made based on the data available. It is important to figure out how these large language models are trained.
Fair and responsible use of AI is a big focus at SAS as GenAI impacts everyone from vendors to regulators and customers. In order to promote fairness and transparency, businesses need to establish methods that are clear and explain how the models work and any biases they may have.
Furthermore, an organisation’s GenAI literacy and cultural attitude will directly affect its ability to tackle privacy and security issues, which now encompasses data access management and data localization. As employees become more literate in GenAI, they can better leverage its benefits while remaining aware of its limitations.
For every AI project, businesses can clearly define ownership and stakeholder structure. It is important to then decide which decisions can be automated and which require human input. Once that is identified, assign responsibility for all parts of the process, including AI failures. Setting clear boundaries for AI systems includes monitoring and auditing algorithms regularly to mitigate bias and ensure the models are operating as intended.
4. What are the main benefits and challenges associated with GenAI – according to the recent SAS study?
Based on SAS’ global market research, GenAI adoption has been shown to improve staff experience and satisfaction (89%), achieve operational cost savings (82%), and raise customer retention (82%). GenAI can deliver highly accurate data insights in seconds, rendering quick and reliable decisions. By using data analytics, businesses can create personalised experiences for customers, increasing customer retention rates over time.
Adopting GenAI is not without its difficulties, though. Businesses are constantly faced with the rising challenges of citizens’ expectation of real-time services, while others are challenged by a lack of knowledge of GenAI, and insufficient or difficulties integrating GenAI technology into existing systems. The absence of a well-defined GenAI strategy poses a challenge to transitioning from concept to practical use of generative AI.
5. Why do you think China is leading in this field in terms of adopting GenAI and how is the adoption of GenAI different across various regions?
The adoption of GenAI depends on the country’s focus and way of approach to it. For instance, China’s government has given a strong push for Gen AI adoption; this coupled with the government pouring massive resources into AI research and development has led to higher adoption rates.
Here is another example. The Singapore government has been proactive in terms of understanding how the nation can leverage GenAI by developing sandbox, a framework which provides a controlled environment for fintech startups and innovators to test new products and services under regulatory supervision. This enables businesses to manage the adoption of GenAI for various sectors at the same time.
6. What emerging trends in GenAI do you foresee that businesses should watch for in the next five years?
It is anticipated that AI will help us be more vigilant around applications that will become smarter. AI will also create jobs with the right Government support in place. As AI continues to develop, regulatory bodies are expected to implement stricter regulations for its development and deployment across various industries. Companies will increasingly adopt ethical AI practices, focusing on key pillars such as accountability, transparency, and fairness.
7. How might regulatory environments influence the future development and deployment of GenAI?
Local government regulations will play a crucial role in shaping the future development and deployment of GenAI. Regulatory bodies are increasingly focusing on ethical AI development. Similar to SAS’ code of ethics, which includes fairness, accountability, transparency, privacy and robustness in AI models. Often, regulations are associated with slowing down development processes, but the right kind of regulations in place will accelerate development.
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