EY Partner Gavin Seewooruttun talks artificial intelligence strategy

15 August 2018 Authored by Consultancy.com.au

Gavin Seewooruttun, EY’s Asia-Pacific Advisory AI leader gives three pieces of advice so companies can ensure that they are getting the most out of their AI investments: align AI to purpose, manage input to generate good output, and make sure you end up using the projects.

“Every day, developments in AI, machine learning and even robo-advisors appear in mainstream media headlines. Although most companies are just beginning their AI journey, the momentum in this field is growing rapidly,” says Gavin Seewooruttun. 

Seewooruttun – who works out of the consulting firm's Melbourne office, has been with EY as a Partner since 2014. He holds a degree in electronic engineering from the University of Southhampton and a degree in banking and finance from the London School of Economics and Political Sciences. 

For the EY Partner, achieving commercial results with AI must be balanced by a sense of social responsibility. Socially, the use of AI will raise ethical questions for workers, private individuals and communities. First, and most obvious, is the implications for jobs. In the future, it has been estimated that up to 50% of low-skilled labour jobs may be potentially replaced by automation. 

Seewooruttun uses an MIT Technology Review from 2016 as an example, stating that “70% of Asian executives expect AI to result in substantial job losses in the next five years.” Such a large displacement of jobs within the labour force in such a short period of time makes many wonder “if our society is moving fast enough to address that impact and the needs of an AI-enabled workforce,” he said.

However, beyond job losses, the impacts of AI on society may be broader than once considered. Take advances in automation as an example. The rise of drones can have two seemingly unconnected outcomes. On the one hand, Uber teaming up with aerospace companies to produce prototypes of Vertical takeoff and landing (VTOL) aircraft is likely to create the future of urban travel. On the other hand, consider the fact that the same drone technology was recently used in an assassination attempt on Venezuelan President Nicolas Maduro. 

In terms of AI technology, Seewooruttun says, “There are other risks that have not been as widely publicised as potential job losses. For example, there is potential for systematic bias in decision-automation algorithms, either by accident or design. This can result in adverse social consequences such as denying a segment of the population access to insurance.”

Without a viable AI strategy, he contends, it is difficult to navigate a successful path through the benefits, opportunities and risks of AI. To do so, companies will need to map out their specific desires – whether that’s cutting costs, improving productivity or restructuring the workforce – and then prepare the journey. Seewooruttun notes three steps to consider:

EY Partner Gavin Seewooruttun talks artificial intelligence strategy

Align AI to purpose

“When imagining how AI might change their organisations, many business leaders become too narrowly focused on the tactical impact. In doing so, they risk missing the big picture. AI is fundamentally changing the role of people at work and thereby the very reason why organisations exist. When the AI focus is purely tactical, there is no impetus for the whole organisation to respond to both the opportunities and the risks, across the value chain.” 

“Instead, to derive the full value of AI, organisations will need to think more broadly about how, where and when to deploy AI technologies and processes to deliver meaningful top- and bottom-line results. To do that, they will need to align their strategy and their technologies to the company’s overall purpose. Defining and developing a coherent AI strategy aligned to purpose will be essential to guide successful transformation.”

Manage input to generate good output

“Enterprise technology programs are not easy to execute in the best of circumstances. Implementing AI solutions is even more difficult, thanks largely to three key differences. First, AI solutions are (mostly) trained, not coded. That means that a significant amount of development work that would have traditionally fallen to an IT team now must shift to subject-matter experts who may have limited capacity to spare.”

“Second, AI solutions consume unstructured data such as documents and rich media. Managing the “ground truth” in real time becomes critical, while complying with data privacy regulations may become harder because sensitive data may be embedded in free-text documents. Third, the pace of AI innovation is unprecedented and software vendors are under continuous pressure to stay ahead of the curve. Each of these characteristics will shape the way organisations will select and deploy AI solutions.”

“Meanwhile, in our work with a range of EY clients, we’re seeing significant differences in the capabilities of AI software and their compatibility with established data and analytic platforms. Understanding these characteristics, and identifying which technologies and processes best suit the needs of the organisation are at the root of developing successful AI solutions.” 

Consumption

“Finally, there is no point in creating a great solution if no one uses it. In this new territory of AI, it is not uncommon for companies to work with providers to develop hundreds of predictive algorithms, yet implement only a handful of them. Companies will need to build strategy and capacity for operationalising AI processes and apply a rigorous approach to steering AI transformation programs to successful impact.”

Seewooruttun concludes that AI has already begun to change the world around us, particularly in a business sense. For an organisation to better position itself to embark on an AI journey, it is important that they adopt a “purpose-led transformation.” Only then can a company navigate through its journey to future growth. 

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