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Using AI to cope in the coronavirus era

COVID-19 is having serious implications for businesses across the globe, as they adapt to the ‘new normal’ of operating an organisation remotely. Here are seven business functions at risk and the AI solutions that could help:

1. Sales prioritisation

Sales and business development are suffering and AI-powered sales performance solutions can help. So-called propensity models can identify which customers are most likely to buy a product or service from a company, says Dr Tom Davenport, president’s distinguished professor of information technology and management at Babson College, Massachusetts. These models can help those working in sales improve their productivity and effectiveness, by showing them which customers to prioritise.

“For brands, having insight into what their customers think and want has always been a key priority, but the COVID-19 pandemic has made this understanding even more critical,” says Chris Colley, principal of customer experience at Medallia. But he notes, at the same time, collecting data on what customers think has become more challenging.

As people stay at home, consumers have shifted from personal interactions, where they provide direct feedback, to digital interactions. “Instead of visiting a bank branch where they can speak to the cashier, they are more likely to be banking online,” says Colley. “As it’s no longer possible to eat out, they’ll be ordering online deliveries. The same pattern is being replicated in sectors across the board. This shift is creating a ton of new, unstructured data, which can be hard to make sense of.” That’s where AI solutions can cut through the noise and find out what consumers feel and need.

2. Matching demand and supply

Companies are interested in matching demand and supply, and that’s going to be really critical coming out of this crisis,” says Davenport at Babson College. “The good news is there’s more and more external data available on demand.” A big steel company, for example, has information about the various factors that might influence demand for steel, such as the demand for automobiles. These demand measures depend on external data that’s used to match up to what their supply chains can produce. “So that you’re not producing more than you need to satisfy demand and you’re not leaving unfulfilled demand out there,” he says.

AI solutions can analyse this external data. But, as Davenport points out, AI typically relies on data from the past, while the COVID-19 crisis is unprecedented. Therefore, companies have to ensure they use data that is representative. He says: “I suspect that in some industries, the past will be a better guide to present and future activity than it is in others.”

Davenport notes that we do have data from the 2008 financial crash to go on, but the current crisis is happening at a faster rate and its consequences might differ. There are, however, some industries where spending patterns might continue at similar rates, such as groceries and consumer staples. “People have to buy detergent, no matter what,” he says. Conversely, expensive consumer goods might struggle. Data from the last financial crisis might give some indication of how much demand there might be.

3. Document and identity verification

AI can work on identity and document verification, says Dr Terence Tse, associate professor of finance at ESCP Business School. Think of a bank, for instance, that needs to verify its customers for onboarding and compliance. This is often done by human checkers, who check payslips or driving licences. “It’s a very costly, inefficient process,” says Tse.

Instead, AI can be used to “quickly identify the type of ID document captured, determine if the security features of the ID are present, perform face-matching – comparing the picture in the ID to the person in the selfie – and even help determine whether the person is physically present”, says Robert Prigge, chief executive at Jumio.

“For the past few years, digital account opening has been at the top of the list of technologies organisations intend to add or replace, but COVID-19 is pushing this element of digital transformation to the front of the line,” says Prigge.

4. Back-office tasks

AI-powered cognitive assistants can perform a company’s back-office tasks. This includes ordering new credit cards, issuing refunds or cancelling orders, says Faisal Abbasi, UK managing director at Ipsoft. He notes: “When the cognitive assistant is unable to handle a task due to its complexity, this can be seamlessly handed over to human agents to manage. This ensures the time of those team members is spent solving the most challenging problems and focused on value-add activities.”

This process is often referred to as robotic process automation (RPA) and is increasingly combined with machine-learning. It spans all sorts of back-office service operations, as long as they are structured tasks, such as automating the claims processes of insurance companies or banks.

“Almost all the companies that I talked to about RPA said, ‘Oh, we’re just using it to free up people to do more creative, less structured work’,” says Davenport at Babson College. But he notes that if the current COVID-19 crisis leads to a severe recession, which seems likely, companies will use it to replace workers. “My guess is that it’s going to contribute to substantial job losses or at least slower growth of employment after the recession because companies will have automated a fair amount of work,” he says.

5. Cash-flow forecasting

Over the next few months, cash flow is likely to continue to be a serious concern for smaller businesses as revenue streams dry up. But there are a number of forecasting AI solutions that can help. “Cash flow is always an issue in difficult economies,” says Babson College’s Davenport. AI solutions are already in place that analyse data for the purpose of cash-flow forecasting.

One important caveat is “you have to make sure you have the right data period to create models that would be useful for this current environment”, he says. Once again, AI can only help if the data we feed it is representative.

“You have to go back to recessionary environments to ask, what were your cash needs in the past? And again, it’s difficult because this recession appears to be happening much faster,” says Davenport. Economic data comes in slowly and a recession is typically defined as two quarters of negative GDP growth. He adds: “We won’t have this data until the end of June. But I think there is not much doubt among economists that we’re in a recession already.”

6. Medical support

The COVID-19 crisis has put unprecedented pressure on NHS staff as public health has taken centre stage. “Medical services have been terribly shaken and our beloved NHS may be near a coup de grâce,” says Dr Alex Ribeiro-Castro, data scientist and senior teaching fellow at Imperial College Business School in London.

He says health tech may offer a temporary buffer to allow non-critical ailments to be treated, leaving clinics and hospitals free to focus on critical cases. An example is Doctorlink, which provides online doctor’s appointments and has algorithms that can provide medically endorsed diagnostics. Another is Babylon Health, which is building an AI-based health app that can help diagnose patients’ issues. It’s effectively a chatbot that can “translate layman’s language into medical terminology and deduce what may be causing the pain”, says Ribeiro-Castro.

Dinesh Venugopal, president at Mphasis Direct & Digital, says: “AI-based chatbots and robot-advisory services can very well be useful in relieving the administrative burden on extremely busy and under-resourced healthcare staff, automating processes such as screening patients for symptoms and recording necessary information.” By reducing the amount of face-to-face interaction between patients and hospital staff, this goes a long way to lessening the risk of spreading infection, he says.

7. Staff demand, supply and infrastructure

Given that many employees may have to self-isolate during the COVID-19 outbreak, AI can analyse the number of staff needed. “AI companies get requests from their clients to identify if they are likely to even have enough workers to staff a railroad,” says Davenport at Babson College. In this case, AI can help to match demand and supply, but from a labour standpoint. “If companies are laying off people, they’d like to know it’s the right number of people. Making sure you have enough people to staff a particular train or a production shaft could be quite difficult.”

Transportation companies represent a significant component of a country’s infrastructure. “They are faced with an unfortunate Catch-22 situation: we, as a society, need to keep critical infrastructure and its employees healthy, however not all of them can manage critical infrastructure remotely,” says Ribeiro-Castro at Imperial College Business School.

What’s more, semi-automation is already implemented in certain forms of public transport. Ribeiro-Castro cites Navya, a company that designs and manufactures autonomous vehicles, such as shuttle buses at airports or theme parks. “AI is already being used more generally in the transportation sector to do things such as increase passenger safety, reduce traffic congestion and accidents, lessen carbon emissions, and also minimise overall financial expense.”


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