Talk about artificial intelligence has been about robots taking over jobs — until the last few weeks. With the carnage that the coronavirus has brought us, it’s now about whether enterprise AI can help forecast and mitigate the effects as they occur in an effort to save lives, businesses and jobs. But let’s be frank. AI and all its predictive wonders did sod all this time around to help us predict, understand and act on this pandemic nor the massive economic and social panic its created. It could have — maybe even should have — but it didn’t.
The coronavirus is testing America’s health care and human services infrastructure in unprecedented ways. Right now, it’s all hands on deck as communities band together — with state and federal support — to face a fast-moving threat.
We need to understand how we can do better, and whether we can save more lives. Moreover, we need to know technology’s role in why the virus outpaced our public health system’s ability to effectively recognize, and respond to, the emerging crisis. A data issue? The capacity of humans to understand the magnitude of the conclusions presented? An ability of societies to mobilize proactively rather than responding to the abyss? Or we just didn’t focus AI in the place it can excel at – finding and predicting events and surprises – and listening to the results.
Some of these questions are about societal maturity and human conduct, and can be addressed by much smarter people. But hopefully, we look for better integration of intelligent data systems into operations and decision-making. For governments and global health groups, that means resilient systems that collect and analyze on-the-ground reporting — from nurses and physicians across the world — so that local threats can be met with an international response.
For the many private-sector players involved in the response, as well as tech leaders looking to draw a lesson from it, it means utilizing integrated digital systems to proactively recognize and respond to emerging risks and consumer demands.
Conversations about machine learning and AI often lead to questions about jobs; if computers are doing the work what are humans for? It’s true that some tasks, and therefore jobs, are becoming redundant, but there’s strong evidence showing that AI suites can revive and expand the workforce in new parts of the economy.
I raise the workforce debate to make the point that AI and machine learning often do work that humans can’t do, or at least can’t do as fast and with as much insight. Humans, of course, can do many things that computers can’t do. But more importantly for the discussion of AI in operations, humans can do their jobs better when they are working with data-driven tools able to analyze and filter incoming information.
Human resources offices, for example, can spend less time compiling information on new applicants or compiling payroll information, grocery store managers can immediately pinpoint the need to reup inventory or stock shelves, and logistics companies can track orders in real time, cutting out the uncertainty of global shipments. Health officials might better understand the path of the next pandemic to mitigate the impact, uncertainties and months of likely disruptions.
Circling back to our current crisis, it’s obvious now that the first physicians in China who recognized the coronavirus threat should have sparked a stronger response — a debate is raging over the central government’s handling of the outbreak. And as the United States and other countries enact sweeping emergency measures to contain the crisis, there’s an overriding question of whether it should have happened sooner. It’s not about jobs the way it used to be, it should be about salvaging and making jobs better, creating more information and helping us see events that normally we could not get control of. We failed, but maturity of technology, the organizations and culture around it will do better.
Among the many lessons we will learn in the coming weeks, one will be the need to listen to people on the front lines more closely. We are all becoming painfully alert to the need for improved public health monitoring. Governments need to commit to transparency about risks emerging within their borders. And for private sector health care players, we need to encourage data sharing without putting individual confidentiality at risk. So, is it more about integration than AI creating important intelligence? Once done the power of the AI really can be achieved? And we can stop being scared about jobs, and open up new areas of value, fidelity and success.
As operational leaders in healthcare and other industries make quicker decisions on the best approach to AI and ML right now, one key will be taking stock of all functions performed across the enterprise, rather than looking at jobs and roles as they are currently defined among human employees. Where could information be analyzed and disseminated faster? What other data do we need to be watching?
Where is useful information not being shared across departments? When is delayed communication stifling a faster response? We need to understand and invest in the prerequisites that form the foundation of valuable AI. Once done we will start listening to and understanding risk models better – preparing rather than locking away and hoping for the best.
These are all areas where deploying AI can help organizations achieve far greater efficiency and agility. In public health systems, innovations in AI can help us save lives. In all sectors, it can help us to respond to more problems before and through crisis. It failed this time. Failed because it was not used, because the parts to make it important were not there, and when it was the results were too large, too unacceptable to warrant action. Maybe a miss of such magnitude, and the events we have to endure will perhaps offer a small silver lining in more credence given to models about the huge impact of global warning across our societies and economies that still looms above the current situation.
Simon Moss is CEO of Symphony AyasdiAI, an artificial intelligence company offering a software platform and applications to financial, health and telecommunications organizations looking to analyze and build predictive models in financial crimes and liquidity optimization.
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