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Going from AI Hype to Competitive Advantage—Here’s What We Learned at Google Next from Four AI Leaders

Ring Central Blog


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4 min read

According to Irwin Lazar, research leader at Nemertes Research, artificial intelligence (AI) was the “topic du jour” at Google Next 2018, held in San Francisco in late July.  RingCentral announced plans for its new integration plans with Google Cloud Contact Center at the event. 

Lazar was part of a panel at Google Next that dissected the implications of AI on businesses and that also included RingCentral’s David Lee, as well as AI leaders from Box, Google, and Okta. The panelists were there to analyze where we sit on the AI hype curve today, and explain how organizations can leverage AI to build real-world solutions that produce tangible business benefits.

As Faizan Buzdar, senior director of Box, put it, our conversations about machine learning (ML) and AI sound eerily similar to the question raised two decades ago: “How do I use the internet?” Every business knows they need to jump on the AI bandwagon, but few understand exactly how.

David Lee told the Google Next audience, “Just like any real emerging technology, companies who figure out how to leverage AI to boost their business outcomes will find the most value.” Lee introduced himself as an “AI learner,” because in his role he has repeated opportunities to talk to RingCentral customers and partners who are truly bringing to bear innovative AI solutions. Through these learnings, Lee and the RingCentral team can source the most valuable AI solutions and focus on real, tangible business benefits.

The panelists all agreed that the only way to move past the hype is to focus on what we can do with AI here and now. And the panelists were eager to offer up suggestions and tips for how business leaders can leverage AI and machine learning to see a big competitive advantage. Here are a few of their tips and tricks:

#1: Find areas for repeatable success. Buzdar suggests following two guiding principles when choosing whether or not to apply machine learning to any given process. Don’t look at AI as a replacement for experts. Rather, think of AI as more of a copilot, and less of an autopilot. Second, don’t start with curing cancer. Select a scenario across your business processes that is happening at scale and can be automated. Then assume your AI capabilities have the capabilities of a 13-year-old, but apply this power across millions of processes. Through scaling and repeating these AI processes, you’ll see the biggest possible impact.

#2: Your biggest opportunity is human assistance, not replacement. As Chris McLaughlin, GTM Strategy Lead, Cloud AI/ML at Google, told the audience, we’re in the midst of the democratization of AI. We’re able to surface data models and learn from them without relying on data scientists. “AI is being incorporated into really practical brass tacks use cases where we’re slotting AI into things like contact center software. Now instead of having a decision tree, you can replace this with an AI powered bot, speak one thing to it, and it can intuit what it is you’re trying to do.” Businesses should insert AI into workflows that we all have to deal with and leverage it to make finding answers easier and more accurate.

#3: Think about the processes that define you. Through answers to different questions, the panelists seemed to steadily bring the audience back to one defining question: What unique problems can I be solving with AI? Amazon is winning retail because it’s winning the customer experience strategy, and then figuring out how to apply machine learning and AI to make customer interactions even better. Think: what is separating you from the competition and how can you apply AI to that process?

In terms of where AI is headed, the future is bright. Between off-the-shelf solutions and cutting-edge companies, adoption is going to continue to ramp up. McLaughlin added that most companies will get their first exposure to AI when it’s embedded in a solution that is almost invisible to them, such as search or chatbots. AI started in the realm of data science, but now we’re opening it up to all the developers in the world… and now it’s in the hands of millions.

Building on this theme McLaughlin added, “At that point, you are getting access to such broader domain knowledge that the applications of AI both become way, way more effective because they are being used by the people driving business value from tools and way more numerous. We can’t think of all these applications ourselves as tech providers. We have to put it in the hands of more users so they can expand it. And this starts with expanding access.”

Ernesto Tey, VP Global Strategic Alliances at Okta, challenged the audience to “imagine the potential of integrating our data set with the RingCentral data set, with the Google data set, and with the Box data set—how does that make it easier for you to identify niches within your customers to have a higher profit margin or to put out a promotion or to have a higher CSAT (customer satisfaction) score that would not be available by individuals looking at each application in silos… It’s the aggregation of the information and the potential that comes with that aggregation which we’re very excited about.”

The panelists agree that we’re not to a critical adoption point yet. If companies don’t adopt AI and ML practices, they might not fail but they’re certainly not getting ahead when they could be. As Tey put it, those who optimize AI for the sake of their customers, and not just because it’s the shiny new object, are the ones that will drive stronger business outcomes. This is a great time to ask yourself: Does this matter to our business and do we have the data for it? And if your answer to both these questions is yes, it’s time to jump on the AI bandwagon.

Originally published Aug 10, 2018, updated Oct 28, 2020

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