Taking the GenAI bull by the horns, organizations in the region may be in danger of overreaching themselves. Would a sandbox approach help?
A recent SAS survey found that early adopters are finding plenty of obstacles in using and implementing GenAI, with 9 out of 10 senior technology decision makers admitting they don’t fully understand GenAI and its potential to affect business processes.
Yet, despite this understanding gap, 75% of organizations have set aside budgets to invest in GenAI in the next financial year.
DigiconAsia discussed some of the key findings with Deepak Ramanathan, Vice President, Global Technology Practice, SAS, to gain insights into the challenges and ways to circumvent the pitfalls of GenAI adoption in Asia Pacific.
How do you foresee GenAI impacting the economic landscape of your industry within the APAC region?
Deepak Ramanathan (DR): Many opportunities have been made possible by Generative AI (GenAI), such as the ability to create content automatically, customize it highly, and make well-informed decisions. The incorporation of GenAI into APAC businesses has the potential to significantly transform the region’s economic environment through increased productivity, significant expansion, and fostering innovation in various industries. Companies that adopt GenAI gain a competitive edge in markets.
Spending on Gen AI is expected to reach $26 billion by 2027, according to IDC, with developing economies adopting the technology at a rate that is 30% higher than that of established economies. It is imperative that GenAI be integrated with governance, risk, and compliance (GRC) systems. If this isn’t done, there may be missed chances to automate processes like creating policies and managing controls, which might put businesses at risk of not complying with evolving regulations. Having said that, it is crucial for companies to proceed with caution and follow privacy guidelines.
With 94% of APAC organizations planning to invest in GenAI next year, but only 10% of organizations are fully using and implementing GenAI in APAC, how do you propose organizations should allocate their budget for GenAI initiatives?
DR: Data infrastructure is essential to utilizing GenAI to its maximum potential. Customized business apps that add value for clients while upholding the idea of striking a balance between progress and societal welfare are part of an all-encompassing GenAI approach. The fields of software development, content creation, consumer interaction and intelligent AI assistants are a few examples of where GenAI might be useful.
Depending on their resources and time constraints, businesses should tailor their strategy to best match their strategic goals by employing third-party GenAI solutions or creating their own GenAI models.
The use of GenAI doesn’t come without its difficulties. GenAI models are susceptible to bias, which, if unchecked, may produce unfairly biased results. Moreover, GenAI models’ decision-making procedures may be opaque. It can be troublesome when there is a lack of explainability and transparency, particularly when handling sensitive data.
The survey indicates a lack of understanding of GenAI among leaders and data privacy and security are major concerns for organizations adopting GenAI, what measures can organizations take to address these issues?
DR: Since AI choices are based on the data at hand, we must first address data ethics before moving on to the topic of AI ethics. Determining the training mechanism of these massive language models is crucial.
Since GenAI affects everyone from vendors to regulators to customers, SAS places a high priority on the fair and responsible use of AI. Businesses need to set up transparent procedures and provide an explanation of their models’ operation as well as any biases in order to foster fairness and transparency.
Moreover, an organization’s capacity to address privacy and security concerns—which now include data access control and data localization—will be strongly impacted by its level of GenAI literacy and cultural mindset. Employees who are more knowledgeable about GenAI will be able to better make use of its advantages while still being conscious of its limitations.
Businesses can specify ownership and stakeholder structure for each AI project in detail. The next crucial step is to determine which decisions can be automated and which ones need human involvement. As soon as that is found, take accountability for every step of the procedure, including any AI errors. Establishing boundaries for AI systems entails routinely auditing and monitoring algorithms to reduce bias and make sure the models are performing as planned.
What are the main benefits and challenges associated with GenAI, according to the recent SAS study?
DR: The adoption of GenAI has been demonstrated to increase customer retention (82%), save operational costs (82%), and enhance employee experience and satisfaction (89%), according to SAS’s worldwide market research. GenAI can provide extremely accurate data insights in a matter of seconds, enabling prompt and dependable decision-making. Businesses may use data analytics to tailor client experiences, which will eventually lead to higher customer retention rates.
However, implementing GenAI is not without its challenges. Companies must continually contend with the growing hurdles posed by citizens’ expectations for real-time services.
Other challenges include inadequate or challenging integration of GenAI technology into current systems and a lack of awareness about the technology. The transfer of generative AI from concept to real-world application is hampered by the lack of a clear GenAI approach.
Why do you think China is leading in adopting GenAI, and how is the adoption of GenAI different across various regions?
DR: The nation’s approach and focus will determine how widely GenAI is adopted. For example, China’s government has heavily encouraged the use of GenAI and this, together with the government’s large investment in AI research and development, has increased adoption rates.
In Singapore, by demonstrating its proactive grasp of how the country might employ GenAI, the government has created a ‘sandbox’ – a regulated environment where fintech firms and innovators may test new products and services under regulatory supervision.
This makes it possible for companies to oversee the simultaneous implementation of GenAI across multiple industries.
What emerging trends in GenAI do you foresee that businesses should watch for in the next five years?
DR: GenAI is rapidly transforming the way we live and work. It is expected that AI will make us more aware of apps that are becoming increasingly intelligent. AI will be harnessed in specialized fields such as healthcare and finance, helping healthcare to make more accurate diagnoses and develop personalized treatment plans for patients while automating financial reporting and analysis.
As the use of GenAI grows, there will be a stronger focus on ethical and responsible AI practices. Stricter rules for AI development and use across industries are anticipated to be implemented by regulatory organizations as the technology advances. Businesses will increasingly adopt ethical AI practices, emphasizing principles like accountability, transparency, and fairness.
