During the pandemic, we’ve seen some of the most rapid implementations of technology in decades. Millions of people were suddenly required to work from home—countless offices, restaurants, and even healthcare spaces shut down. Such dramatic changes that occurred essentially overnight required technology to adjust at a breakneck speed.
Consider telemedicine, for example. We’ve been talking about effectively adopting telemedicine for 20 years, but it took less than a month to become widely available when we needed it. After sitting on the margins for years, telemedicine suddenly became an organizational imperative, and now it’s here to stay.
There’s a lesson here for Enterprise AI. While AI is reportedly a priority for organizations, few projects get beyond the initial proof of concept phase to become an enterprise-wide strategy. AI needs to be more than a strategic priority—it needs to be a necessity.
And when you make AI an organizational imperative, you will see results.
Telemedicine Was Adopted Quickly and Is Here to Stay. Why Can’t AI?
Again, telemedicine quickly became an organizational imperative, and its usage has exploded since the beginning of the pandemic. A McKinsey report states that the number of telehealth visits has increased over 50 fold. Insurance claims that involve telemedicine have jumped also. According to FAIR Health’s Monthly Telehealth Tracker, the number of telehealth insurance claims increased 4,347 percent from March 2019 to March 2020.
Here’s the key takeaway: Telemedicine became popular due to COVID-19, but its convenience has ensured that it’s something that isn’t going away. Today, 64 percent of providers feel more comfortable using telehealth post COVID, and 57 percent view it more favorably than they did before the COVID pandemic hit.
All it took was a push for widespread implementation, and opinions on its usage quickly changed. If we treat AI with a similar sense of urgency, we’re likely to witness the same phenomenon. Once AI is fully incorporated into enterprise systems, the resources and time saved will no longer make it viewed as a convenience, but instead as a necessity.
AI Usage: Where Are We Now?
AI has primarily been a strategic priority for most enterprises, with a mildly successful rate of implementation. Thirty-seven percent of organizations have implemented AI in some way, a 270 percent increase in the past four years. According to Gartner, 80 percent of emerging technologies will have foundations based on AI by next year. This data showcases a mark of progress, but it’s not happening as fast as it can (and should) be, especially considering that the recent spike in customer demand has made the need for AI implementation even more crucial.
At Verint, we’ve seen that many businesses were unprepared for the one-two punch of transitioning many of their workers to remote settings, while also handling a spike in customer inquiries. With traditional call centers temporarily disrupted or even shut down, enterprises must rely more on AI to keep up with customer service demand. Intelligent Virtual Assistants (IVAs), equipped with natural language understanding, can accurately interpret user intent and help redirect calls to streamline the process.
We’ve seen firsthand how Verint’s IVAs enabled our customers to thrive during the pandemic, even when their contact centers were severely disrupted. That’s because our AI technology is meant to expect the unexpected and even adjust to changes in customer needs and expectations.
Like telemedicine, AI isn’t going away after the pandemic. Even government organizations, which are often behind in technology, are impressed with the results of deploying AI.
How to Prioritize AI
Now that we know some of the effects of prioritizing AI, how do you go from merely strategizing to implement AI to making it an operational imperative? The short answer is to commit to AI investment. Take a look at these governmental initiatives aimed to boost AI deployment.
For example, there’s the American AI Initiative, an executive order signed in 2019 that tasked federal agencies with devoting more AI research and training resources. Many saw it as a nice sentiment, but considering that it never proposed a specific dollar amount, it lacked teeth. Compare that to the Pan-Canadian Artificial Intelligence Strategy, a $94-million plan to invest in AI research. The EU commission investment, in which the EU agreed to increase investment in AI from $565M in 2017 to $1.69B by the end of 2020. As opposed to the American AI initiative, Canada and the EU have committed funds and resources to AI research.
Enterprises should prioritize AI in a similar fashion—and treat this technology as the necessity that it is, rather than a down-the-road endeavor. That’s why it’s vital that enterprises make sure any directives and announcements of AI intentions are supported with appropriate resources.
It’s All in the Revenue
AI isn’t connected to revenue right now, but it should be.
Telemedicine became a necessity because hospitals could no longer partake in lucrative elective surgeries. It’s estimated that US hospitals lost more than $50 billion per month by abstaining from non-emergency procedures during the pandemic.
Salesforce recently found that 70 percent of customers say seamless customer service interactions impact their purchasing power. It costs six to seven times more to attract a new customer than to retain one, and if customers can’t receive a fast resolution, it’s easier than ever to go elsewhere.
Prioritizing AI means understanding how this technology figures into your revenue. If you successfully deploy intelligence across your organization, start with AI projects that will directly impact revenue.
That’s how you make AI an organizational imperative.