Artificial intelligence (AI) has come out of labs and movies and pervaded our homes! From Alexa to Nest to Siri to Uber, we are surrounded by smart machines running on incredibly powerful and self-learning software platforms. And this is just the beginning. Now, AI is transitioning from being just our personal assistant to much more disruptive domains by rapidly outperforming the most talented of us in many endeavours. From AI platforms out-competing humans at some of the most challenging games like Jeopardy, chess to the still relatively nascent driverless cars.
In healthcare, smart AI systems are quickly surpassing the capabilities of human radiologists researchers at Houston Methodist Hospital utilise AI software, which interprets the results of breast X-rays 30 times faster than doctors and with 99 per cent accuracy.
AI applications across the industries will have a significant transformational impact, but it will be accompanied by changes in skills that employees need to learn. Adoption of bots in bank will start replacing low-skilled branch employees in the next three-four years. As AI technologies will be embedded in each and every function across the BFSI (Banking, Financial services and Insurance) value chain, there will be a need for reskilling the current workforce to align them with more value-added services such as product development, risk management, underwriting loans, etc. For new recruitments, there will be an increased demand for professionals with focus on Fintech and understanding of new-age technologies such as AI.
According to an EY poll of 200 senior global AI professionals on the current state of AI adoption and integration, talent is a principal concern, with 56 per cent reporting a lack of AI experts as their greatest barrier to AI implementation within business operations. While new roles such as automation specialists, information architects, bot programmers etc. are emerging, they will be relevant for structured AI implementations. At the same time, a lot of existing roles will need to be augmented to ensure that humans and machines are able to work seamlessly. For example, to get the most from AI, managers must become augmented managers and participate in the instructional experience by learning processes.
One important but often-overlooked aspect of augmenting work is to recognise the relationship that exists between augmenting a job/role/process and automating it. In many ways, automation and augmenting are two sides of the same coin. To effectively augment, one needs to automate. For instance, in the retail industry, a lot of jobs across inventory management and logistics will be automated to achieve higher efficiency.
While some job roles will be merged to be able to better work with the robots, the retail sector will see an increase in augmented jobs that include working with new technologies such as data analytics, IoT and blockchain. Jobs will increase for profiles such as IoT architects, system integrators and data analysts. This positive sentiment is echoed in the survey as well overall 62 per cent of employees have a positive sentiment towards AI which suggests stakeholders are increasingly embracing the potential of AI.
This year, as businesses strategised on how to integrate AI into their operations, they were challenged by a shortage of experts with requisite knowledge of the technology. This serves to demonstrate that successful AI integration is not just about the technology, it's about the people. Looking to 2018, organisations should prioritise talent acquisition and cultivation-both by recruiting individuals with strong technical backgrounds and investing in skills and training programmes to help retain and foster leading AI practitioners.
Preparing potential workforce by identifying and training them on appropriate skills before the need arises requires an understanding of emerging skills requirements. The demand for AI talent is driving top-business schools to include AI and machine learning (ML) in their curriculum from next year.
While most organisations have embarked on their AI journey, the pace of adoption is bound to accelerate in 2018. The aspirational goal of AI is to take intelligence and put it into machines. To be successful, leaders will identify a business challenge and then determine where the technology can solve a problem. To be innovative, leaders will transform a traditional process/industry by discovering new techniques and knowledge to find the answers that are not obvious like 'what if a company knows what the customer will buy even when the customer has no idea?' But all this can only be realised with the support of AI-savvy professionals who can identify AI opportunities, and AI implementation specialists who holds good knowledgeable on AI components, including ML, Data and other underlying technologies.
Milan Sheth, Technology Leader, EY