For most of us, 2020 has ushered in unwelcome chaos and uncertainty. Who would have guessed in early March that within days, personal decisions that were once mundane – like where, when and how to get groceries – would spur paralyzing anxiety.
What was missing – and still is – is information and, in particular, data that’s helpful. Should I send my kid to school? Is it safe to attend that outdoor wedding? When will the next shipment of toilet paper arrive? All these questions could only be answered by data – and thus knowledge – that just isn’t available to us through conventional means.
Or is it? The truth is, there’s no shortage of data; according to the World Economic Forum, the universe contains 40 times more bytes than observable stars in the sky. The problem is, there’s too much of it, and a lot of it is useless. No individual (or even roomful of people) could possibly churn through all the bits and bytes to find the meaningful indicators that would result in a sound decision or tell us what will happen next.
This, we’ve long maintained, is the job of artificial intelligence (AI). Indeed, since the pandemic began, interest in AI has skyrocketed, according to our recent research, conducted in partnership with ESI ThoughtLab, with almost two-thirds of senior executives seeing AI as highly important to the future of their business.
AI: a Means to an End
But a funny thing happened on the way to this realization. The more companies absorb AI into their business processes, the less they see it as anything out of the ordinary and more as a means to an end. Rather than seeing AI as something done in a secret lab by an elevated brain trust, companies are turning to the technology to do very practical things – things that otherwise would have taken forever to do or just wouldn’t have gotten done. This includes things like accelerating underwriting processes, reducing fraud risk or increasing patient adherence to medication regimes.
Simply, AI is now seen as a set of technologies, like natural language processing, machine learning and Evolutionary AITM, that does the heavy lifting for organizations to meaningfully consume – and act on – huge volumes of continuously growing and always changing data. It’s a way for us to work and see meaning at a scale that’s bigger than ourselves.
In this post-AI landscape, all eyes are now on data – not just any data but data that matters. For businesses, this means getting past the reports and spreadsheets and whatever is tucked away within their systems of record, and looking out to the great beyond – data that’s not always structured and formatted and not always owned by the business itself, including publicly available drone and camera images or social media sentiment, as well as geolocation and psychographic data.
It also means combining this data in new ways, like taking video from street cameras and combining it with store foot traffic data and local tweets to ascertain the business revenue of a particular geography or even what people are buying in that area. These more diverse data sources, it turns out, are also the fastest growing type that businesses plan to use in their AI systems – and thus their decisions – over the next three years, according to our study.
From FUD to FOMO
And therein lies the key question that’s keeping senior executives up at night: What data is your competition using that you’re not? Because in the volatile landscape shaped by COVID-19, it’s never been more clear that, in the paraphrased words of Sun Tzu, “knowledge is power” – knowledge that comes from not just any data but the most relevant and accurate data, analyzed in the right amount of time. Importantly, the most valuable knowledge is gained not from what we think should be the case – or what we want to be the case – but from what the data tells us. This is what AI can deliver.
In these unprecedented times, “gut feel” has never been more irrelevant – and, increasingly, risky. When we look at our customers that are doing well in COVID-19, it’s clear they’re the ones that quickly invested in pulling together relevant data and using AI for rapid forecasting – the national convenience store chain and the multinational food, snack and beverage company that used AI-driven analytics to quickly change their product selection to reflect what customers actually wanted and needed during the pandemic.
And when it comes to health and safety in the pandemic, AI could give us essential – and importantly, informed – insights into which actions would be most effective for preventing the spread of the disease and restarting the economy. Currently, we’re working on a proof of concept of a machine learning-based model that could incorporate the many unknowns of the virus. With enough good data, such a model could accurately predict how different interventions would impact the pandemic without having to precisely understand how these outcomes would emerge. We’re using Evolutionary AI to extend the model from prediction to prescription and actually begin formulating effective intervention strategies. Our results so far suggest that such prescriptions may become possible as the quality and quantity of data improves.
Importantly, this system isn’t designed to replace human decision makers but to empower them: The humans choose which tradeoffs are the best, and the AI makes suggestions on how they can be achieved. It’s easy to see how this type of system could be used for decision making across any number of volatile situations we face today, from the current pandemic to climate change.
Moving toward Intelligent Decisioning
So how can businesses flip the switch and begin taking a more modern and intelligent approach to decision making? According to our study, the first step is modernizing data – i.e., ensuring the accessibility, reliability and timeliness of data for AI and analytics. Nine out of 10 AI “leaders” in our study say they’re in the maturing or advanced stages of data modernization, while literally none of the AI “beginners” rate themselves that way. Changing your culture is also essential – senior leaders need to be willing to not just pull in external data that didn’t originate within their organization but also be willing to base their decisions on the insights generated by this data.
As we enter the final months of a year that’s brought more volatility than most of us care for, it’s time to lift the shroud of uncertainty that keeps us from making the healthiest and most profitable decisions. The leading indicators – the data that matters – are out there. We just have to accept that it’s an inhuman task to make the invisible visible. For that, we have a tool, and its name is AI.
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