Suddenly, everybody is talking about generative artificial intelligence (AI). (Disclaimer: this article is written by a human.) The idea of software that generates dynamic, customized content is exciting. While chatbots have existed for years, a rapidly expanding suite of generative AI-based image, video, and text generators such as DALL-E 2, Fotor, Runway, AlphaCode, and ChatGPT (just to name a few) have the potential to democratize AI and put it into the hands of every person and every organization. Integrating these into mainstream software products in the form of “co-pilots” to assist in everyday tasks hold even more promise.
Generative AI offers particularly strong potential as an economic catalyst across Asia, building on advanced levels of digital adoption. Already, India and China are global centers of tech talent. Japan, Korea, and Singapore lead in smart cities and robotics, while a vibrant and growing startup ecosystem flourishes in Beijing, Jakarta, Bangkok, and beyond. All this provides a foundation for the region’s developers to create the next wave of locally relevant solutions.
Implemented responsibly, generative AI stands to create a ripple effect—one that transforms industries, fosters productivity and innovation, and improves billions of lives. So, as the technology reaches an inflection point, what are some of its main uses and early success stories in Asia? And how should the region’s organizations prepare to innovate?
Unlocking human creativity and potential
The key promise of generative AI is to streamline virtually any routine language- or process-driven task, supporting the capabilities of humans while freeing up more creative and productive uses of time. Leading businesses across Asia are beginning to explore these possibilities and state their ambitions. For example, Panasonic Connect introduced an AI virtual assistant for its 12,500 employees in Japan in February 2023. Meanwhile, in India, leading online travel company MakeMyTrip (owner of the Goibibo and redBus travel apps), has introduced voice-assisted booking in Indian languages, starting with Hindi, to complement the work of its human agents.
In an IDC survey, about 70% per cent of Asia-Pacific organizations say they are either exploring or have committed to invest in generative AI technologies. “We believe all business professionals will use AI on a daily basis,” says Hiroki Mukaino, senior manager of IT and digital strategy at Panasonic Connect. “Our choice was not whether to use AI, but when to start using it.”
According to Microsoft’s 2023 Work Trend Index, which is based on a survey of 31,000 people in 31 countries, 78% of respondents in the Asia-Pacific region would delegate to AI, where possible, to reduce workloads. Three in four admitted they would be comfortable doing so, not just for administrative tasks but also for some analytical or creative aspects of their role. Given the imperfections of generative AI – that it is only as good as the information fed in – it is easy to foresee a shift in human labor to manage, modify, analyze and assure the quality of algorithm-driven output. As digital workforce innovation analyst Dr Joseph Sweeney says, “AI will be baked into everything we do, and roles and jobs will change.”
The opportunity for developers in Asia is to harness an innovation tool that has the potential to quickly make a broad and powerful impact. From India to Indonesia, generative AI can be wielded by every person, whether they work for a large firm, startup, or as a freelancer. In the words of Dr Sweeney, virtually any business process or customer transaction can be “expressed as a linguistic pattern." Eric Boyd, corporate vice president of Microsoft’s AI platform, describes the powerful ability of Azure’s OpenAI to generate and summarize content, produce code, and reason from gathered data. “Those four use cases are the dominant ones,” he says. “We are just scratching the surface of the types of applications that we can see.”
Technology forerunners in Asia
The organizations experimenting with generative AI either anticipate or are already experiencing tangible outcomes. For example, at Panasonic Connect, the implementation of ConnectAI, built on the Microsoft Azure OpenAI platform, is simplifying tasks that can eat up a worker’s day, such as drafting emails, gathering information, and writing code. Now, employees simply type out a question in natural language to obtain help. Currently, the platform is logging 5,000 questions daily. Panasonic Connect’s CEO, Yasuyuki Higuchi, even used the tool to draft a welcome speech for new employees. In future, “humans will concentrate on highly advanced work, rather than fairly simple work,” Higuchi says. “I think this is necessary.”
While impacts are so far anecdotal, benefits cited by employees include being able to read a summary of a long legal document in 10 minutes rather than the full version in an hour. In the IT and digital department, responses to employee IT surveys are being crunched in an hour rather than taking a whole week. This potential to improve labor force productivity is particularly vital in Japan, where almost one-third of the population is aged over 65. Set against a shortage of workers, generative AI is one way of “increasing employee productivity,” said Mukaino. “AI allows us to focus on creative tasks that only humans can do.”
Meanwhile, MakeMyTrip’s chatbot solution, also powered by Azure OpenAI Service, stands to open up mobility to hundreds of millions more citizens of India, the world’s most populous nation. These are people who may feel more comfortable booking a holiday through voice interaction rather than navigating a smartphone app. “The basic set of questions that an agent would typically ask will now be asked by these chatbots,” says Sanjay Mohan, MakeMyTrip’s chief technology officer. “We look at them as an intelligent aide to our human agents. They will get more qualified leads that they can close better. We believe the productivity and efficiency of our human agents will get a significant boost as a result.”
In a nation that is home to almost 400 separate languages, the goal is to support voice interactions so that anyone in India will be able to use the platform. To achieve this, the natural language models powering the AI platform still need fine tuning. “With conversational chatbots, voice support for languages needs to be refined for the colloquial usage of the language, something that a customer in a small town or village in India would speak or understand,” Mohan says. Another ambition is to develop a multimodal chatbot interface that simultaneously incorporates text, voice, video, and image interactions. “It’s still a very nascent technology. I don’t think anyone in the industry has been able to figure out that one yet.”
What’s next for the region’s organizations?
Globally, there is real awareness that generative AI should only be introduced responsibly and with care. Organizations need large amounts of data resources to train AI models, particularly in Asia, on the subtleties of dialects and vernacular. Also needed are world-class security and privacy protections—for example, not compromising personal customer or employee data. Issues to do with the integrity and accuracy of AI models need to be dealt with, as well as the reality that human input to monitor generative AI will still be required. As Mohan says, “the performance characteristics of the feature have to be world-class before we roll this out to 100 per cent of our customer base.”
In considering the way forward, organizations in Asia also need to consider their commercial imperatives. For some, generative AI offers a way to generate efficiencies, while others – particularly niche startups – may be looking to disrupt the market and establish a competitive advantage. Sweeney believes many small- to medium-sized enterprises can afford to take an incremental approach. Generative AI will be infused into their cloud-based software subscriptions as soon as it becomes viable. However, larger organizations may be more interested in building custom-specified AI models.
The key building block is becoming a data-driven business, fortified by digital and cloud capabilities. Essentially, the purpose of having massive-scale cloud computing infrastructure is to support in-house app development capability. Organizations that build their own enterprise-grade apps can apply generative AI to rewrite complex workflows, better engage with employees, customers, and other stakeholders, and generally optimize how their business runs.
This content was produced by Microsoft. It was not written by MIT Technology Review’s editorial staff.
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