Shanhong Liu, a technology and telecommunications researcher for Statista, opined that the limitness applications of artificial intelligence (AI) – the creation of intelligent hardware or software that seeks to replicate human behaviours, such as learning and problem solving.
Despite acknowledging that AI continues to the subject of people’s imagination and science fiction for decades, Statista, nonetheless, is forecasting spending on AI to exceed US$47 billion in 2020, with financial institutions accounting for over 23% of overall spending.
“AI disrupters will enjoy a differentiated and sustainable measure of success ranging from superior customer engagements, accelerated rates of innovation, higher competitiveness, higher margins, and productive employees. Organisations worldwide must evaluate their vision and transform their people, processes, technology, and data readiness to unleash the power of AI and thrive in the digital era," she added.
She cautioned, however, that AI's rate of adoption will be challenged by evolving national regulations, a lack of an ethical foundation, and a lack of transparent and self-explaining algorithms critical for the trust and avoidance of unintended negative outcomes.
In this episode entitled “Strategies for getting AI off the ground” we spoke to Andrew Psaltis, APAC Chief Technology Officer for Cloudera on his take of AI and how it ties closely to data.
The groundwork for the discussion on artificial intelligence started off with a definition and quickly moved to how enterprises are interpreting and/or applying the technology.
Click on the play button to listen to Psaltis’ view.
- And where does this definition fit in the context of analytics and all the other related technologies we hear about today?
- How are enterprises in Asia interpreting the use of AI?
- Given the variety of interpretation of the technology and the use cases, how does an organisation approach integrating an AI strategy with what may well be an existing way of using analytics?
- Who should own an AI initiative at the whole-of-enterprise level and how can one mitigate against the risks of failure?
There is this July 2019 article on Forbes that called out stats from a survey: 25% of organizations worldwide that are already using AI solutions report up to 50% failure rate. Can you cite one pitfall that is leading to failed AI projects?
I guess that’s where the crux of the challenge lies. We often get enamoured with the potential of a technology that we ignore all the investments that have gone before it until the sales contract with the vendor has been signed and the stakeholders realize this new AI project is much more than a point solution. And maybe we should have taken a bit more time figuring out how do we take what we have and use AI to bring it to the next level.