The pandemic has taught a similar lesson in artificial intelligence (AI): Organizations are on the right track with their AI strategies or, if so, they need to dramatically step up the pace of investments. Children’s Hospital Information Director Dan Nigrin notes that artificial intelligence applications that promote telehealth, for example, “are not necessarily related to covid, but the pandemic has certainly accelerated the uptake. account and use of this type of telehealth. tools “.
In a recent survey on. According to 301 business and tech leaders, 38% report their plans for investing in AI have not changed as a result of the pandemic and 32% say the crisis has accelerated their plans. The percentages of unchanged and accelerated AI plans are higher in organizations that already had an AI strategy in place.
Consumers and business decision-makers are realizing that there are many ways in which AI increases human effort and experience. Technology leaders in most organizations see AI as a critical capability that has accelerated efforts to increase operational efficiency, gain deeper insights into customers, and shape new areas of business innovation. .
AI is not a new addition to the technological arsenal of companies: 62% of respondents use AI technologies. Respondents from large organizations (those with annual revenue exceeding $ 500 million) have almost 80% higher implementation rates. Small organizations (with revenues of less than $ 5 million) are at 58%, slightly below average.
But most organizations haven’t developed plans to guide them: just over a third (35%) of respondents indicate that they are developing their artificial intelligence capabilities under the auspices of a formal strategy. AI plans are more common in large organizations (42%), and even small businesses are, at 38%, slightly above average.
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Of those who do not have a current AI implementation, a quarter say they will implement the technology in the next two years, and less than 15% say they have no plans. Here, the gap between large and small is widening: less than 5% of large organizations do not have AI plans, compared to 18% of smaller ones.
More apps get closer to the source
More and more, businesses are moving their IT infrastructure to cloud-based resources for a myriad of reasons, including profitability and IT performance. At Schneider Electric, an energy management company, the cloud has been essential “not only to digitally transform our businesses, but also to digitally transform the businesses of our customers,” says Ibrahim Gokcen, who was until recently the director. Schneider technique. “It was a clear and strategic area of investment for us before the crisis”.
As such, it’s no wonder most companies are putting AI in the cloud – 77% are implementing cloud-based AI applications. This makes cloud resources much more popular than hosting on servers or directly on end devices such as laptops or smartphones.
Cloud-based AI also allows organizations to operate within a partner ecosystem that includes app developers, analytics companies, and customers themselves. Nigrin describes how the cloud allows one of Boston Children’s Hospital partners, Israeli medical technology developer DreaMed Diabetes, “to inject artificial intelligence” into the remote management of insulin. First, patients upload data from their insulin pump or meter to the cloud. “The patient gives access to this data to the hospital, which in turn uses software, also in the cloud, to process the data and use its algorithmic approach to suggest adjustments to the insulin regimen the patient is taking,” which offers a huge time saving. and additional knowledge for physicians.
But as the cloud offers significant AI-related benefits for businesses, a growing number of applications must use the infrastructure capabilities of the “ edge, ” the middle IT layer between the cloud and the devices whose they need. computing power. The advantage is that these compute and storage resources, hosted on edge servers, are closer to a device than cloud computing data centers, which can be thousands of miles away. This means the latency is lower, so if someone is using a device to access an app, the delay will be minimal. And while edge computing doesn’t offer the infinite scalability of the cloud, it is powerful enough to handle data-intensive applications like artificial intelligence.
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