How AI will drive the human workforce of the future
Thu 18 Jan 2018 | George Zarkadakis
Ahead of his session at Big Data World, London 2018, George Zarkadakis, Digital Lead at Willis Towers Watson, discusses the role of AI techniques for advancing workforce opportunities
Data underlies everything we do at Willis Towers Watson; whether in insurance, in human capital or benefits. We support our clients to make use of their data too, with the help of insights, in order to significantly improve their competitiveness.
As data becomes more varied and voluminous, machine learning has enabled a quantum leap in data utilization, offering opportunities for gaining new customer insights, engaging the workforce, predicting outcomes across a range of business problems, or developing new products and services.
In the insurance segment of our business we have been pioneering a number of machine learning solutions, including in modelling and predicting risk as well as in dynamically assessing the risk profile of car drivers.
In human capital and benefits, we are also working on extending the capabilities of our existing HR software, particularly in the areas of HR service delivery via digital assistants, as well as in predictive modelling for workforce analytics.
Artificial intelligence can be seen as a technology that not only accelerates the digital transformation journey, but takes it to another level altogether. In terms of organizational design, AI can be an enabling technology for the realization of platform organizations, or “networked ecosystems”; which is a radical way of unleashing human potential.
We see this already taking place with organizations that have made progress with digital transformation, and are now using data and machine learning to quickly optimize expertise and technology in solving a variety of business problems faster and better than their competitors.
Haier Group, the consumer electronics and home appliances company, for example, has readied itself to make the most of the IoT opportunity in its global markets by effecting a distributed organizational model.
Business decisions that affect people should always be filtered by human leaders before becoming finalized and executed
Key to the success for such innovative companies is having access to the right talent quickly; artificial intelligence and data are used to develop internal talent platforms that can seamlessly combine talent from inside, as well as outside the organization, thus solving one of the biggest challenges that business face in their effort to become agile and manage their varied, and distributed, workforce.
We are still in the early days of the “fourth industrial revolution”, but even so we can clearly see a number of emerging challenges that we must solve in order to ensure an equitable future for human workers.
For recruiting, which is increasingly mediated by machine learning algorithms, it is vital that we ensure that candidate profiling does not include biases and data is used to increase, rather than decrease, diversity and inclusion.
Another area of potential risk is the overdependence on machine learning algorithms to provide suggestions and predictions. We humans may not be perfect but we are the product of millions of years of evolution that furnished us with valuable instincts, such as empathy and common sense – traits that are impossible to code in a machine, at least in the foreseeable future. Business decisions that affect people should always be filtered by human leaders before being finalized and executed.
There always exists a need to understand and serve customers better, and faster. But this is a huge challenge, especially for large organizations with legacy systems, bureaucratic processes and established cultures that are generally not a good fit for the new world of digital innovation.
Although everyone talks about data, it is often difficult, if not impossible, for employees to access data and use it as part of their everyday work. Machine learning algorithms and tools allow for experimenting with multi-dimensional data sets, but how can teams make use of these capabilities when data is not easily nor readily available?
Priority has to be placed on the creation of open data platforms in organizations, coupled with HR leaders managing the necessary cultural shift that will lead to data-driven processes. Once a baseline has been achieved each team will be able to explore its own innovative avenues in improving customer experience and business outcomes by leveraging additional data and data source.
George Zarkadakis will be speaking at the forthcoming Big Data World London, which takes place on 21st and 22nd March 2018 at London’s ExCeL Centre. To hear from Zakardakis and other AI and machine learning experts from around the world, register today for your FREE ticket.
Tags:AI Big Data business feature machine learning
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