\u201cFor 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,\u201d 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.<\/p>\n
As people stay at home, consumers have shifted from personal interactions, where they provide direct feedback, to digital interactions. \u201cInstead of visiting a bank branch where they can speak to the cashier, they are more likely to be banking online,\u201d says Colley. \u201cAs it\u2019s no longer possible to eat out, they\u2019ll 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.\u201d That\u2019s where AI solutions can cut through the noise and find out what consumers feel and need.<\/p>\n
2. Matching demand and supply<\/h4>\n
Companies are interested in matching demand and supply, and that\u2019s going to be really critical coming out of this crisis,\u201d says Davenport at Babson College. \u201cThe good news is there\u2019s more and more external data available on demand.\u201d 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\u2019s used to match up to what their supply chains can produce. \u201cSo that you\u2019re not producing more than you need to satisfy demand and you\u2019re not leaving unfulfilled demand out there,\u201d he says.<\/p>\n
AI solutions can analyse this external data<\/a>. 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: \u201cI suspect that in some industries, the past will be a better guide to present and future activity than it is in others.\u201d<\/p>\n 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. \u201cPeople have to buy detergent, no matter what,\u201d 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.<\/p>\n 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. \u201cIt\u2019s a very costly, inefficient process,\u201d says Tse.<\/p>\n Instead, AI can be used to \u201cquickly identify the type of ID document captured, determine if the security features of the ID are present, perform face-matching \u2013 comparing the picture in the ID to the person in the selfie \u2013 and even help determine whether the person is physically present\u201d, says Robert Prigge, chief executive at Jumio.<\/p>\n \u201cFor 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\u00a0digital transformation<\/a>\u00a0to the front of the line,\u201d says Prigge.<\/p>\n AI-powered cognitive assistants<\/a>\u00a0can perform a company\u2019s back-office tasks. This includes ordering new credit cards, issuing refunds or cancelling orders, says Faisal Abbasi, UK managing director at Ipsoft. He notes: \u201cWhen 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.\u201d<\/p>\n 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.<\/p>\n \u201cAlmost all the companies that I talked to about RPA said, \u2018Oh, we\u2019re just using it to free up people to do more creative, less structured work\u2019,\u201d 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. \u201cMy guess is that it\u2019s 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,\u201d he says.<\/p>\n 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. \u201cCash flow is always an issue in difficult economies,\u201d says Babson College\u2019s Davenport. AI solutions are already in place that analyse data for the purpose of cash-flow forecasting.<\/p>\n One important caveat is \u201cyou have to make sure you have the right data period to create models that would be useful for this current environment\u201d, he says. Once again, AI can only help if the data we feed it is\u00a0representative.<\/p>\n \u201cYou have to go back to recessionary environments to ask, what were your cash needs in the past? And again, it\u2019s difficult because this recession appears to be happening much faster,\u201d says Davenport. Economic data comes in slowly and a recession is typically defined as two quarters of negative GDP growth. He adds: \u201cWe won\u2019t have this data until the end of June. But I think there is not much doubt among economists that we\u2019re in a recession already.\u201d<\/p>\n 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\u2019s 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\u2019 issues. It\u2019s effectively a chatbot that can \u201ctranslate layman\u2019s language into medical terminology and deduce what may be causing the pain\u201d, says Ribeiro-Castro.<\/p>\n Dinesh Venugopal, president at Mphasis Direct & Digital, says: \u201cAI-based chatbots and robot-advisory services<\/a>\u00a0can 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.\u201d 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.<\/p>\n Given that many employees may have to self-isolate during the COVID-19 outbreak, AI can analyse the number of staff needed. \u201cAI companies get requests from<\/p>\n their clients to identify if they are likely to even have enough workers to staff a railroad,\u201d says Davenport at Babson College. In this case, AI can help to match demand and supply, but from a labour standpoint. \u201cIf companies are laying off people, they\u2019d like to know it\u2019s the right number of people. Making sure you have enough people to staff a particular train or a production shaft could be quite difficult.\u201d<\/p>\n Transportation companies represent a significant component of a country\u2019s infrastructure. \u201cThey are faced with an unfortunate\u00a0Catch-22<\/em>\u00a0situation: we, as a society, need to keep critical infrastructure and its employees healthy, however not all of them can manage critical infrastructure remotely,\u201d says Ribeiro-Castro at Imperial College Business School.<\/p>\n What\u2019s more,\u00a0semi-automation is already implemented<\/a>\u00a0in 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. \u201cAI 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.\u201d<\/p>\n Originally published here<\/a>.<\/p>\n3. Document and identity verification<\/h4>\n
![\"Document](\"https:\/\/www.raconteur.net\/wp-content\/uploads\/2020\/04\/ai_st_p4_5-e1587139026332.jpg\")
<\/h2>\n4. Back-office tasks<\/h4>\n
5. Cash-flow forecasting<\/h4>\n
6. Medical support<\/h4>\n
![\"Medical](\"https:\/\/www.raconteur.net\/wp-content\/uploads\/2020\/04\/ai_st_p5_3-e1587139264210.jpg\")
The COVID-19 crisis has put unprecedented pressure on NHS staff as public health has taken centre stage. \u201cMedical services have been terribly shaken and our beloved NHS may be near a coup de gr\u00e2ce,\u201d says Dr Alex Ribeiro-Castro, data scientist and senior teaching fellow at Imperial College Business School in London.<\/p>\n7. Staff demand, supply and infrastructure<\/h4>\n
\n