December 20199 adoption. This was seen in the case of Grab vs. Uber, and can be seen in the battle between ecommerce leaders every day. Though even if the business sees the value in implementing ML, there are a few barriers to adoption that are often missed: Data availability, data quality and data analysis capabilities. Asia has a wealth of yet untapped data. For instance, only 53 percent of the Asian respondents are using alternative data in their ML work as compared to 93 percent in the Americas. In our lab, we developed a Data Science Accelerator for financial institutions to increase the speed at which data can be turned into usable input for a data scientist. Unsurprisingly, the traction from Asia was unprecedented as it provides data science ready data saving stress on the existing talent pool.Lastly, the gap between the C-level leaders and the data scientists needs bridging. A common vocabulary around what AI/ML can and cannot do needs to be understood. Business leaders don't have true visibility on how AI/ML are used to solve their business problems. Therefore, they lack clear articulation of problem statements in a way that can be readily turned into a useful AI/ML application by a scientist, an area that will have to mature over time.AI and ML as Part of Core Business Strategy in AsiaKnowing that 76 percent of the parties interviewed in our machine learning survey are implementing ML in their core business strategy, 8 percent ahead of Europe, is encouraging. Though if you dig deeper, only 29 percent have actually deployed ML in their core business. This is likely based on the fact that we are yet to mature in ML adoption and while ML has been accepted at a strategy level it still needs to percolate down to the ground for adoption at scale. Some of this is also on account of wanting to appear as an innovation company in the perception battle. The hedge funds where innovation and investment has been traditionally focused are certainly ahead although the greater availability of tools is likely to materially level the playing field--on both the buy and sell sides.Another observation is that senior leadership need to appropriately incentivize their business managers to support ML adoption with their business lines. Real value from ML efforts needs the right ingredients and real understanding of what is possible and what is needed to make it happen. Educating this powerful segment of employees, identifying their communication gaps, and strengthening their ML/AI understanding over time is one approach. Elevating the data science team to the C-Suite level, creating a cross functional body responsible for the execution of the organization's AI and ML projects, is another. Prioritization of the Application of ML and AI in AsiaBeyond data availability and quality, talent is and remains the ultimate deciding factor. Although, nearly half of our survey respondents informed us that their companies will be increasing the number of scientists on staff in the next 12 months, actually recruiting, training, and retaining these scientists is a different story. The Singapore government for example has set up multiple training resources to address the dearth of professionals trained in the field which addresses one part of the equation. A good data scientist is not just a whiz with numbers but also brings a certain level of relevant domain expertise to the table. That's why the more tech centric and savvy departments enjoy the benefit of data science sooner, think payment processing, than those who are still stuck with largely manual processes, such as procurement and operations. Let's assume all the ingredients are on hand: Skilled talent, connected data, and a department ready to transform, then the final hurdle is proving the potential ROI of this investment to the executive leadership. Will you indeed contribute to the bottom line in one year's time? If the answer is unknown then you better rethink your strategy or realign your expectation.Sanjna Parasrampuria
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