The Hunger for Data is Asia’s Main Threat to AI Development

by October 1, 2018 0 comments

Artificial Intelligence

Artificial Intelligence (AI) has evolved to be a crucial tool in transforming businesses from followers to leaders. AI promises to take business operations and customer experiences to a new level.

The dependence on Artificial Intelligence has made nations to go data hungry. For a majority of businesses in the Asia-Pacific, data dependence has become the main hurdle in adopting AI. Increased data dependency has led nations struggling to gather and integrate big data into their operations. With the technological revolution spreading its frontiers with each passing day, the volume of data generated grows by leaps and bounds and companies must find a way to leverage big data to reap the benefits of AI in the years to come.

 

The Tougher Challenge

The results published in a Forrester report highlighted that right data management identification and predictive analytics platforms and sourcing data from diversified channels are the most common difficulties which businesses face when they deploy big data into their operations.

In the path towards data deployment, companies have noted difficulties in building cross-functional teams who can work with agility, and identify the right technology or service partners to adopt AI. These challenges hinder their ability to take an advantage of AI technology on the offering. To clear these hurdles, organizations must take a step forward and choose partners offering expertise in engineering and data management needed to build precise and effective models.

 

The Early Adopters

Asian nations are witnessing exciting development integrating data into AI operations. The Forrester study explains that Indonesia the leader in the Southeast Asian nation region for AI implementations. About two-thirds of Indonesian companies deploying data have stated that they have either implemented AI or are expanding/upgrading their capacity which will help them in the future to merge data with their AI operations.

Top countries in the South East Asian region for AI implementation are all emerging markets. The first spot being taken by Indonesia closely followed by China and India, ahead of more developed economies of the Southeast Asian nations like Japan, South Korea, Singapore and Taiwan.

Integrating AI into business operations brings two main benefits one being more efficient business operations that comes with better risk predictions and second being customized products and solutions offerings based on improved insights into customer behavior.

In the case of Asia, companies generally expect to witness more benefits in business processes as when compared to customer engagement strategies. The only exception to this is the way people at Singapore do business and expect improvements in customer experiences to be more prominent than business processes.

 

The Futuristic Path Ahead

The future spells interesting trends for Southeast Asian nations. In markets like Taiwan, South Korea, and Indonesia, AI is being predominantly harnessed to drive product innovation and improve business processes. On the other hand, business processes in Japan, Australia and Singapore have their focus turned to use AI to improve existing business solutions for higher client engagements.

IDC estimates that the global data-sphere subject to data analysis may see a robust growth in the years to come, mainly dominated by the data processed by emerging technologies. With a lot at stake, the urgent need is to be able to expand network storage capacity quickly and cost-effectively without disrupting service level agreements. The world has already invested nearly US$39 billion in AI, where Asia-Pacific region’s spending is expected to reach US$4.6 billion by 2021. With so much at stake, adequate data management can prove to be a make or break deal to capture AI dominance in the future ahead.

The benefits of AI are increasingly gaining into mass interest and it is important for companies to take a futuristic action now and overcome difficulties that come up with big data integration, or face the risk losing to their competitions.

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