Analytics & Big Data
May 14, 2020

The power of augmented intelligence in uncertain times

In times of uncertainty, businesses must adapt and make high-stakes decisions quickly. Artificial intelligence (AI) and machine learning are powerful technologies that play a part, but true success requires human judgment infused with a healthy dose of empathy. Enterprises that seamlessly connect these different ways of thinking will be likely to thrive as we enter a new normal.

Augmented intelligence – a blend of machine intelligence and human judgment – is always powerful, but during times of economic and business uncertainty, it's essential. An empathetic approach to augmented intelligence allows teams to uncover insights and translate them into actions that drive meaningful results.

This approach is particularly important when unprecedented changes render many existing analytical models obsolete. For instance, in banking – wherein new types of fraud are emerging – systems and processes require continual updating with fresh data to fuel better predictions and insights.

This is why a critical component underpinning the power of augmented intelligence is a strong data foundation. Many forward-thinking enterprises that have invested in clean and reliable data – backed by a data-driven culture and agile methodologies – are able to act at speed and transform at scale, even in this rapidly changing environment.

The interplay between human and machine

Examples of augmented intelligence in action can be found almost everywhere. For instance, the economic strain of COVID-19 presents an ethical challenge for banks that are deciding whether to collect outstanding loans. Should they proactively offer forbearance or focus entirely on their commitment to shareholders? Analytics informs the logical side of the decision, while human judgment informs the empathetic side. The banks that are leveraging technologies, analyzing data, and applying empathy are the banks that will have loyal customers when the uncertainty subsides.

Another example is healthcare organizations' use of machine learning to map high-risk populations against real-time COVID-19 case data. This supports simulation models that help emergency rooms and hospitals predict the quantity and severity of potential cases. Applying human judgment to these predictions ensures that hospitals can consistently provide outstanding patient care.

Maintaining business continuity and increasing resilience

Even sophisticated technologies like AI and machine learning aren't able to predict black swan events such as recessions, pandemics, or terrorist attacks. And although AI can accelerate decision-making, it's unable to deal with a multitude of variables. If an analytical model didn't have certain information when trained, it won't know how to act. When you're looking at a potential response that differs from everything that's been done before, human intervention is unavoidable.

However, it's important to remember that – when leveraged as part of augmented intelligence – AI and machine learning are powerful catalysts. Adding human judgment empowers enterprises to rapidly detect, react, and adapt to change, which is essential for business continuity and increasing resilience.

This need for resilience is especially important in supply chain. Anomaly detection technologies are vital in flagging unusual spikes in product demand, pinpointing where those spikes are happening, and tracking how they're evolving across multiple locations. This information provides employees with a comprehensive view of the situation so that they can proactively adapt inventory management to meet demand.

Of course, data alone may not provide the full picture. Connecting and speaking to sales representatives across different regions creates an additional – and essential – layer of information. This information can capture regional, cultural, and language variables that can be added into the analytics strategy. Though anomaly detection provides a strong starting point, businesses can make more accurate predictions and more informed decisions when they combine data-based and human insights.

About the author

Amaresh Tripathy

Amaresh Tripathy

Global Business Leader, Analytics

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