Introduction
The term “artificial intelligence” or AI is applied to machines built on human intelligence that can do some tasks that a person can do. The building of algorithms and processes allows machines to learn from data, become flexible, and draw conclusions based on information or predictions. AI can be categorized into two types: narrow AI and general AI. While narrow AI is task-oriented, such as image recognition or natural language processing, general AI seeks to mimic the intelligence of humans in many different things. Some AI techniques are machine learning, deep learning, natural language processing, and Robotics. AI is used for many things, including virtual assistants such as Siri and Alexa, self-driving cars, and diagnostic devices in the medical field. It can change different industries, especially due to enhanced productivity, eliminating redundant job roles, and opening up more opportunities. Nonetheless, the ethical aspect of developing AI responsibly is necessary to guarantee that AI will positively impact our community by human standards.
AI has been strategically established in Singapore due to its massive program – NAIS. With this aim in mind, The National AI strategy is meant to turn Singapore into a center of excellence for adopting AI applications and fostering economic growth. Singapore’s strategy uses AI for healthcare, education, financial services, and smart cities to sustain its superior position amid fierce worldwide competition (Goode et al., 2023). Singapore’s proactive stance can be traced to its affirmation of AI’s transformational capacity to trigger economic development across society. The government and businesses based in Singapore are making efforts towards the adoption of AI by the citizens of the country. While Singapore lags behind Indonesia and Thailand in AI adoption, it is still keen to become the leader of AI in the ASEAN region.
In an effort to become the global leader in the AI market, the government has done things like establishing AI Singapore or designating AI as the core technology. Moreover, the country has teamed up with the World Economic Forum’s Centre for Fourth Industrial Revolution to create an AI governance framework focusing on ethics and responsible use. AI is already helping many industries, including the health sector, with tasks such as skin cancer detection, x-ray scanning, and filtering medical literature. This paper aims to discuss AI and its different models while evidencing its impact on the Singaporean business landscape and the government’s initiatives toward its adoption.
Types of AI in Business
Narrow AI (Weak AI) is tailored for a particular assignment/assignment under a constrained environment. Narrow AI is used by businesses in different ways to boost business operations and provide better customer services (Khurana et al., 2022). Businesses also use virtual assistants such as Siri and Alexa to do simple things for their customers, answer questions, and give them personal assistance. Customer-oriented chatbots run on Narrow AI that gives replies to frequently asked questions and navigates people through different issues (Bahja, 2021). Powered by Narrow AI, recommendation systems use behavioral prediction of users and preference analysis to provide relevant products or content, possibly increasing clients’ satisfaction and boosting sales (Bahja, 2021). Narrow AI technologies allow businesses to automate repetitive operations, speed up their activities, and create unique customer experience that results in better market position.
Machine Learning (ML): One aspect of AI is Machine Learning, which involves algorithms that help computers learn from and with data to make decisions. Business operations depend on machine learning in many ways. Machine learning algorithms help companies automate processes, improve efficiency, and make better decisions (Przybył & Koszela, 2023). Machine learning is used in the marketing world by companies to analyze their customers’ information and behavior to offer them personalized ads or recommendations. Fraud detection systems also use machine learning to quickly identify patterns and deviations and stop financial losses. Machine learning plays a role in the diagnosis of diseases, the prediction of patient outcomes, and the optimization of treatment in the healthcare industry. Some ways enterprises use machine learning are demand forecasting, inventory management, and supply chain optimization to enhance operational efficiency (Przybył & Koszela, 2023). Moreover, machine learning also drives chatbots and virtual assistants, improving customer service. Machine learning helps businesses use data to their advantage and promote innovation for making strategic, information-based decisions.
Natural Language Processing (NLP): The concept of Natural Language Processing (NLP) permits machines to engage conversationally with human language significantly. NLP is key in many business applications (Dwivedi et al., 2023). For instance, speech recognition is one of the main applications of NLP, making it possible for machines to transform spoken language into written text. This technology is used in voice assistants, transcription companies, and call centers for better customer relations. A critical application is language translation, enabling companies to speak with foreign clients and create new markets (Bohr, 2021).
Furthermore, NLP also participates in opinion formation through sentiment analysis to determine the opinions of the community and the clients. Studying social media posts, reviews, and surveys is vital for the company to make informed decisions that will improve its products or services. To top it up, NLP drives chatbots offering prompt customer services, increasing customer satisfaction (Dwivedi et al., 2023). Therefore, NLP enables businesses to handle and interpret vast amounts of textual data effectively, enhancing decision-making and customer engagement.
Robotics Process Automation (RPA): RPA refers to using robots called bots software entities to automate routine and repetitive work that humans previously did. RPA bots can imitate human activities to communicate with digital systems carrying out business procedures (Karatop et al., 2015). Business people use this technology for jobs like inputting data, processing transactions, and answering general customer questions. One example is that RPA can be used for banking loan processing and account management. Using RPA in business improves work efficiency and saves human workers for more productive goals.
Cognitive Computing
Cognitive computing incorporates artificial intelligence, neural networks, pattern recognition processes, and natural language processing to imitate the human brain. Businesses in all industries rely heavily on computer vision (Mesmari, 2023). In retail, Computer vision technology includes product identification, stock control, and customer analysis. It facilitates automated check-outs, shelf monitoring, and targeted advertising based on customers’ demographics (Dwivedi et al., 2023). Computer vision is used in manufacturing for quality control, defect detection, and optimization of assembly lines. It also ensures that businesses constantly detect flaws in their production processes and retain high-quality products. Computer vision helps in medical imaging analysis, disease diagnosis, and surgical procedures in the healthcare sector. The tool assists doctors in diagnosing irregularities, tracking patients’ progress, and improving surgery accuracy (Dwivedi et al., 2023). Moreover, computer vision applies in transport and logistics, security and surveillance, agriculture, etc., to give businesses better efficiency, accuracy, and decision-making.
Benefits, costs, and challenges
Enhanced Efficiency
The introduction of AI has enhanced efficiency in some Singaporean businesses. AI increases efficiency as it improves and helps to lower inflation. Automation of routine and repetitive tasks by AI means that the employees can concentrate on more elaborate activities, and in this way, productivity is improved. AI facilitates fast and accurate transaction processing, fraud detection, and risk assessment in the banking, finance, and insurance sectors (Hamada et al., 2021). Predictive maintenance using AI in the production environment helps keep the machines operating at peak performance, thereby reducing failure occurrence and prolonging the lifespan of equipment. This efficiency is not just physical. AI can quickly scan large amounts of data, discern trends, and highlight things the human eye may overlook (Perifanis & Kitsios, 2023). For example, One of Singapore’s leading banks, DBS Bank, launched an artificially intelligent system known as “POSB Smart Buddy” to ensure operational efficiency (Smith et al., 2021). It employs an AI algorithm to facilitate routine tasks like transaction processing, fraud detection, and risk assessment, thereby enabling staff to deal with other complex aspects of workplaces. AI implementation has resulted in enhanced speed and high accuracy in financial transactions, in turn resulting in increased production for the bank as well as more benefits to the customers (Smith et al., 2021).AI supports route optimization, inventory forecasting, and resolving supply chain disturbances in the logistics and supply chain environment. At this efficiency, it is not just a matter of reducing operating costs but also giving the business the capacity for agility and responsiveness associated with current market and customer trends (Perifanis & Kitsios, 2023). As demonstrated by their experience with AI-based systems in Singapore, adopting such systems gives organizations a competent advantage, allowing them to operate on a large scale, which was not possible traditionally.
Improved Customer Experience
AI has drastically changed how customers interact with and engage in Singapore. Businesses can now provide a more efficient and personalized experience through technologies like chatbots, personalized recommendation systems, and AI-powered customer service platforms. For example, AI-driven chatbots can handle multiple customer inquiries at once, respond fast, and learn from every interaction to optimize their future answers (Lee et al., 2018). It facilitates efficiency and good customer experience by ensuring uniformity across different contact points. Additionally, AI enables organizations to analyze data for an understanding of, and prediction of, what customers want and how they behave. For instance, retail firms employ AI to understand individual consumers’ shopping habits, which helps them customize their recommended products and marketing programs. This is evidenced by ViSenze, a visual commerce technology company providing AI-based solutions for retailers and media companies. With visual intelligence, ViSenze helps build hyper-personalized experiences that can be a strong competitive advantage for brands and retailers in the future (Yao, 2023). The product discovery platform incorporates smart search, smart recommendations, and smart tagging, which assist in raising conversion rates and revenue and uplifting order value for retailers. ViSenze gives retail merchants a chance to improve the shopping experience by allowing consumers to find products using images while it assists media firms in transforming images and videos into engagement options (Yao, 2023). Visenze processes over a billion queries per month, besides facilitating services such as Enhanced Search and Merchandising Analytics in an effort to improve e-commerce for retailers. Therefore, by offering this degree of personalization, shoppers get more engaged and ultimately satisfied, thus leading to brand loyalty and frequent business visits (Abraham, 2022).
Data-Driven Decision Making
The advent of the age of AI is marked by business decisions based on data in Singapore, with an increase in capability to handle large volumes of information. AI helps to improve decision-making and reduces uncertainty by reducing asymmetric information. Using AI in data analysis, companies obtain information helpful for determining strategy, developing products, and establishing an appropriate place on the market. Analytic tools driven by AI can predict trends in the market, consumer behavior, and possible risks for business and let companies make proactive decisions (Chui et al., 2015). Additionally, by using AI to understand consumer preferences and market changes, firms can design innovative products based on the existing market conditions.
Furthermore, using artificial intelligence (AI) in financing involves predictive analysis, enabling companies to assess risks and invest. Some examples of these companies include DBS Bank, Singtel, and Ensign InfoSecurity. These firms use artificial intelligence to improve customer services, automate procedures, forecast market trends, and enhance cyber security (Lago & Trueman, 2019). These companies use AI to analyze customers’ behavior, questions and needs to offer tailor-made financial advice and quick assistance. Furthermore, companies like Active.Ai provide conversational banking using AI to banks and other financial institutions, allowing them to make data-driven business decisions (Lago & Trueman, 2019). Using AI technology, Singaporean businesses are allowed to achieve better operational effectiveness, enhance services to clients, and develop new products or services. Such data-driven insights are a treasure in modern business conditions like those of Singapore, where the quick adaptation capacity to market changes is very important. The ability of AI to give timely and usable information helps Singapore’s companies stay active, competitive, and up in front of their competitors. Therefore, using AI-driven data analytics as an operational advantage to business strategy is crucial in the digital era.
Costs Associated with AI Adoption
Though using Artificial Intelligence (AI) has many benefits for Singaporean companies, there is also some cost involved. However, these cannot be equated with money only since they must consider the infrastructural, expert, and organizational aspects that should be done to adopt artificial intelligence optimally. The importance of comprehending these costs by businesses and their decision-making processes about AI incorporation must be balanced.
Initial Financial Investment
Intel is a Singapore-based healthcare technology company that partners with Ai Singapore for AI-driven Clinical Trials to enhance research and development, and it has reported high costs to keep up with AI developments (Goode et al., 2023). In the era of AI integration, most companies in Singapore, for example, the government agency, Gov Tech Singapore, have reported that it has invested significant costs to ensure digital transformation in most of the sectors in the country, such as public services, health care, and transportation (Amaglobeli et al., 2023). From a broad perspective, to stay relevant, businesses must be ready to invest a high amount in AI. The costs involved include expenses of purchasing AI software and hardware. The aim is to cover the initial outlay required for specific AI applications such as manufacturing automation, customer relationship management, and big data (Goode et al., 2023). Also, these companies incur the costs of integrating AI systems with current business infrastructure. Effectively and successfully adopting and implementing AI developments require extra expenditure on appropriate equipment and software. These companies in Singapore report that managing and maintaining sophisticated AI technology requires skilled jobs, thus forcing them to seek and employ expertise, which involves high labor costs (Amaglobeli et al., 2023). Also, the companies incur retraining costs for their staff to cope with the new AI tools that might be adopted. As a result, it increases the total cost needed in a business. These initial expenses are impediments, especially for small business entities with limited financial funds; therefore, these should be assessed alongside the likely gains associated with AI applications.
Maintenance and upgrades
One of the notable companies in Singapore that proves that AI has increased maintenance costs is Singapore Airlines, which has incorporated chatbots powered with artificial intelligence for superior customer service (Pinker, 2019). The company highlights that when adopting AI, it is essential to consistently pay attention to its scalability and integrative nature, and it involves high maintenance and upgrading costs. Through AI, companies may be efficient in their operation, enhancing customer satisfaction and thus helping them adjust to the needs of dynamic markets. This makes it essential to upgrade despite the costs involved. It is also important that Singapore companies put appropriate management and security provisions in place before the AI rollout. Such can include the purchase of data storage, data governance, or even cyber security means to safeguard such confidential information, increasing costs. The government has organized national programs to promote responsible handling of data and assist companies in applying safe and responsible AI in their operations.
This insinuates that AI systems need consistent updating to remain efficient and reliable. The AI applications are continually updated with new software, tested regularly, and troubleshooted to run at maximal efficiency. This is because artificial intelligence technology is constantly changing. Thus, companies must always invest to be updated with the newest versions; otherwise, they will stay behind their competitors (Lee et al., 2018). Not updating AI systems is risky as it will likely have adverse consequences that may nullify gains from its use.
Infrastructure and expertise
Implementing AI involves costs related to infrastructure and technology, such as acquiring the necessary hardware and software. Ninja Van, a local logistics company in Singapore, has made investments in AI-enabled robots to enhance its sorting and delivery processes (Burgos, 2022). Although this signifies the commitment of Singaporean businesses to adopt AI technologies and improve their operations, companies such as Ninja Van have incurred increased costs to streamline their logistics. Hence, the technological infrastructure behind AI adoption should be powerful, with superior data processing, retrieval, and management capabilities. It means that, in most cases, they are required to buy new IT systems to support AI. It involves envisioning such elements of security as hardware, secure, scalable cloud services, and fast and reliable broadband (Burgos, 2022). The establishment and maintenance of such infrastructure is a costly affair, more so in case business firms relocate from information system-based models. Also, skilled people are crucial for any AI initiative, and companies in Singapore comprehend this concept well; therefore, they are likely to invest increased resources in training and talent acquisition. This is evident in Taiger company, which is an AI startup and commits to training its’ staffers in AI (Chong, 2022).
Challenges in implementing AI
Difficulties in integrating with existing systems
Established companies like banks such as DBS and OCBC face difficulties when integrating AI in Singapore (Yu, 2023). In this context, these organizations struggle to integrate revolutionary AI solutions with their traditional legacy systems. However, this journey has been impeded by the inability of legacy architecture to accommodate new artificial intelligence frameworks. For example, DBS, a bank known for being creative, prioritizes using AI to improve customer services and mitigate risks. However, it is challenging to merge their AI algorithm with their decades-old system. As a result, it is complex and laborious (Yu, 2023). Just like OCBC tries to use AI for fraud detection and personalized banking services but faces problems of integrating AI capability with existing system infrastructure into a seamless operation unit. The main difficulties arise due to the inflexible systems’ architectures and dissimilar data models, which are incompatible with modern AI solutions in the first place. It is, however, far from simple to achieve this. Besides the technical integration, it still has to conform to requirements concerning security, scalability, and regulation.
However, with such challenges, the Singaporean government has put in place such programs as The Smart Nation Programme that work together with entities like AI Singapore and others to facilitate the sharing of knowledge and support the transformation of the traditional systems (World Bank, 2020). This involves working with other tech companies, banks, and regulators so as to come up with common standards that make it easier for them to incorporate AI into their existing systems, paving the way for an AI-powered future in Singapore in different industries.
Skills gap and talent shortage
The shortage of AI experts in Singapore is also a reflection of what happens globally, a problem for companies that need highly skilled personnel for AI implementation and administration. Despite efforts by AISG, a government-led initiative aimed at developing local talents in AI-related fields, the problems continue to remain unsolved (Heng, 2023). AISG’s programs are designed to provide people with AI skills, while there is much more need for such staff than really exists. Hurdles are experienced by firms within industries including healthcare, as well as financial services, when looking for able enough artificial intelligence professionals. It makes competition more severe, leading to an ongoing need for educating, training, and partnering to build a strong enough AI-focused workforce that can drive companies toward sustainable AI-directed innovations in Singapore.
References
Abraham, M. (2022, February 15). Customer experience in the age of AI. Harvard Business Review. https://hbr.org/2022/03/customer-experience-in-the-age-of-ai
Amaglobeli, D. et al. (2023) Transforming public finance through GovTech, IMF eLibrary. Available at: https://www.elibrary.imf.org/view/journals/006/2023/004/article-A001-en.xml
Bahja, M. (2021). Natural language processing applications in business. E-Business – Higher Education and Intelligence Applications. https://doi.org/10.5772/intechopen.92203
Bohr, A. (2021). The rise of artificial intelligence in healthcare applications. Redirecting. https://doi.org/10.1016%2FB978-0-12-818438-7.00002-2
Burgos, J. (2022) Singapore unicorn Ninja Van Steps up Investments in Automation Amid E-Commerce Boom, Forbes. Available at: https://www.forbes.com/sites/jonathanburgos/2021/11/08/singapore-unicorn-ninja-van-steps-up-investments-in-automation-amid-e-commerce-boom/?sh=37151bdd447e
Chong, C. (2022) AI startup Taiger liquidates Singapore entity, over 80 staff cut, Businesstimes.com.sg. Available at: https://www.businesstimes.com.sg/startups-tech/garage/ai-startup-taiger-liquidates-singapore-entity-over-80-staff-cut
Chui, M., Manyika, J., & Miremadi, M. (2015). Artificial intelligence in business: From research and innovation to market deployment. ScienceDirect. https://www.sciencedirect.com/science/article/pii/S1877050920307389
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion paper: “So what if chatbot wrote it?” multidisciplinary perspectives on opportunities, challenges, and implications of Generative Conversational AI for Research, practice, and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Goode, K., Kim, H.M. and Deng, M. (2023) CSET – Center for Security and Emerging Technology. Available at: https://cset.georgetown.edu/wp-content/uploads/CSET-Examining-Singapores-AI-Progress.pdf
Hamada, M., Temirkhanova, D., Serikbay, D., Salybekov, S., & Omarbek, S. (2021). Artificial intelligence to improve the business efficiency and effectiveness for enterprises in Kazakhstan. SAR Journal – Science and Research, 34–41. https://doi.org/10.18421/sar41-06
Heng (2023) Ai Singapore and the Digital and Intelligence Service signed a memorandum of understanding to enhance AI capabilities for Singapore’s digital defence. Available at: https://www.mindef.gov.sg/web/portal/mindef/news-and-events/latest-releases/article-detail/2023/May/13may23_nr2
Khurana, D., Koli, A., Khatter, K., & Singh, S. (2022, July 25). Natural language processing: State of the art, current trends and challenges – multimedia tools and applications. SpringerLink. https://link.springer.com/article/10.1007/s11042-022-13428-4
Lago, C., & Trueman, C. (2019, March 12). How Singapore is using AI. CIO. https://www.cio.com/article/221994/how-singapore-is-using-artificial-intelligence.html
Lee, J. D., Davari, H., Singh, J., & Pandhare, V. (2018). A systematic literature review on the impact of artificial intelligence in the workplace. ScienceDirect. https://www.sciencedirect.com/science/article/abs/pii/S1877050920307389
Mesmari, S. A. (2023). Transforming data into actionable insights with cognitive computing and AI. Journal of Software Engineering and Applications, 16(06), 211–222. https://doi.org/10.4236/jsea.2023.166012
Perifanis, N.-A., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on Business Value in the digital strategy era: A literature review. Information, 14(2), 85. https://doi.org/10.3390/info14020085
Pinker, A. (2019) SINGAPORE AIRLINES FOCUSES ON AI USER EXPERIENCE, Medialist Innovation. Available at: https://medialist.info/en/2019/08/26/singapore-airlines-focuses-on-ai-user-experience
Przybył, K., & Koszela, K. (2023). Applications MLP and other methods in artificial intelligence of fruit and vegetable in convective and spray drying. Applied Sciences, 13(5), 2965. https://doi.org/10.3390/app13052965
Ribeiro, J., Lima, R., Eckhardt, T., & Paiva, S. (2021). Robotic Process Automation and Artificial Intelligence in Industry 4.0 – A literature review. Procedia Computer Science, 181, 51–58. https://doi.org/10.1016/j.procs.2021.01.104
Smith, W. by M., Pajo, D., Richards, M., & Cole, M. (2021, July 26). One hundred one of the most innovative Singapore-based artificial intelligence companies. Futurology. https://futurology.life/101-most-innovative-singapore-based-artificial-intelligence-companies/
World Bank (2020) Digital Regulation Platform. Available at: https://digitalregulation.org/singapore-regulatory-collaboration-in-support-of-the-smart-nation-program/
Yao, R. (2023). Visenze Case study| Google Cloud. Google. https://cloud.google.com/customers/visenze
Yu, E. (2023) DBS Bank uncovers big data challenges with AI use – and solutions, too, ZDNET. Available at: https://www.zdnet.com/article/dbs-bank-uncovers-big-data-challenges-with-ai-use/
write