Appier’s New Survey on AI Adoption in Asia Pacific: Indonesia Leads the Pack in AI Implementation

    Rabu, 5 September 2018 - 11:17 Editor : Redaksi Dibaca : 114 Views

    Indonesia ranks first in terms of AI implementation (65%), followed by China (63%) and India (62%)
    Gathering and integrating big data remains key obstacle to AI adoption in the region

     

    TAIPEI, TAIWAN – Media OutReach – September 5, 2018 – Appier, a leading artificial intelligence (AI) company, has announced findings from a commissioned study conducted by Forrester Consulting on behalf of Appier. The study, Artificial Intelligence Is Critical To Accelerate Digital Transformation In Asia Pacific, revealed that among the eight countries surveyed, Indonesia ranks first in terms of AI implementation, with 65% of respondents there stating that they have either implemented AI in their business or are even expanding or upgrading their capacity.

    The study, which aimed to shed light on trends in AI adoption across Asia Pacific, also found that across the region, more than half (53%) of those surveyed reported that their biggest challenge in adopting AI technologies is gathering and integrating big data. This demonstrates that enterprises are still struggling to cope with the growing volume of business data — more than ten years after the term “big data” was first coined.[1]

    “Artificial intelligence is the key to digital business; it has the potential to transform everything from business operations to the customer experience,” the study stated. “By leveraging big data-driven AI platforms, enterprises can deliver business value throughout the entire customer life cycle.”

    Among the key findings from the study:

    Emerging economies lead in AI adoption

     


    Figure 1. Percentage of AI implementation in businesses across APAC

     

    According to the study, Indonesia ranks first in terms of AI implementation (65%), followed by China (63%) and India (62%). These three topped more developed economies such as South Korea (57%), Singapore (50%), Japan (47%) and Taiwan (44%).

    However, the survey revealed that the gap is poised to narrow in the coming year: a higher percentage of respondents in mature economies reported plans to implement AI technologies in the next 12 months. Plans to implement AI technologies are highest in Australia (35%), followed by Singapore (31%) and Taiwan (28%).

     

    Improving business operations — key goal driving AI adoption

    AI is poised to reframe both how businesses operate and how they interact with consumers. Broadly speaking, AI offers two primary types of benefits to companies:

    • Operational benefits: simpler and more efficient business processes, greater scale, better risk prediction
    • Customer engagement and experience: better products and solutions, faster innovation, better insights into consumer behavior

    Of the two, businesses across the region expect to realize the greatest benefits from AI in business operations. Seventy-one percent of respondents identified improved business efficiency as the benefit they most anticipated from implementing AI technology, whereas 62% expected improved products or services. The lone exception is Singapore, where businesses see the greatest gains from AI accruing to customer engagement.

    Figure 2. Priorities of businesses across APAC in using AI technologies

     

    Product gains: improving existing solutions vs driving product innovation

    That’s not to say businesses don’t expect AI to deliver better customer engagement, products and services. When it comes to the role enterprises expect AI to play in product development, the survey identified two overarching goals:

    • Improving existing solutions
    • Driving product innovation

    Interestingly, when mapped between the two, companies across the region fell on different parts of the spectrum. In markets such as Taiwan, South Korea and Indonesia, respondents prioritize driving innovation with AI, whereas those in Japan, Singapore and Australia want to use AI to improve their existing solutions.

    Figure 3. Roles of AI implementation for businesses across APAC

     

    Challenges threaten to slow AI adoption, impede gains

    However, while the upside of AI may be clear, there are still significant hurdles standing in the way of broader AI adoption. More than 1 in 2 (53%) of Asia Pacific respondents stated that the biggest problem they faced in adopting AI is gathering and integrating big data. And, companies looking to adopt AI also struggle to overcome operational issues, such as building cross-functional teams to work with agility (51%), identifying the right data management and predictive analytics platforms (52%), sourcing data from the diversified channels (49%), and identifying the right technology or service partners (43%).

    Figure 4. Challenges faced by businesses across APAC in using AI technologies

    “Forrester’s survey reveals that enterprises looking to adopt AI face real, urgent challenges that hinder their ability to take advantage of the many benefits AI technology promises to offer,” said Yu. “To clear these hurdles, it’s important for organizations to choose partners who offer expertise in engineering and data management as well as in building precise and effective models, so they can get started making the most of everything AI has to offer.”

    About the study

    The study surveyed 260 business and IT leaders involved in the decision-making process of deploying new technologies such as AI. Respondents hailed from the retail, IT/telecom, financial services and insurance industries across eight markets in Asia Pacific, including Japan, South Korea, Singapore, Taiwan, China, India, Australia and Indonesia. Learn more: https://www.appier.com/en/report_forrester.html.

    About Appier

    Appier is a technology company which aims to provide artificial intelligence (AI) platforms to help enterprises solve their most challenging business problems. For more information please visit www.appier.com.

     


    [1] https://datafloq.com/read/big-data-history/239



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