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AI technologies are becoming prevalent in enterprises around the world. While the adoption rate varies between businesses, a majority of them — 95% in a recent S&P Global report — consider AI to be important in their digital transformation efforts. More than $50 billion was expected to be invested in AI systems globally in 2020, according to IDC, up from $37.5 billion in 2019. And by 2024, investment is expected to reach $110 billion.
Corporate enthusiasm for AI can be attributed to its potential, Sanjeev Vohra, global lead for applied intelligence at Accenture, explained during a panel discussion at VentureBeat’s Transform conference. He says he’s observed a “massive shift” toward the use of technologies like AI, analytics, and machine learning to create a “greater good” for companies as well as boost revenue and efficiencies, particularly in the last year and a half.
“The C-suite, or the top level of companies that are getting interested in this technology, are seeing how they can actually use AI for business,” Vohra said. “It’s moved out of the experimentation zone … to something scaled. Businesses are using AI to scale business value and enterprise value.”
In an example of the trend, Accenture recently partnered with Intel and Sulubaaï Environmental Foundation, a Philippine-based nonprofit dedicated to protecting Palawan’s natural resources, to pilot Project Corail, an AI-powered platform that monitors and analyzes the resiliency of coral reefs. Since its launch in May 2019, it’s collected 40,000 images of the reef surrounding Pangatalan Island, which researchers have used to gauge reef health in real time. If the program in Palawan is successful, Corail could be used to monitor more of the world’s at-risk reef population.
Trends in enterprise AI
Vohra says that there’s been four fundamental changes in the enterprise AI space over the past few years. First, the cloud and data have come together for companies, giving them a higher level of compute, power, and flexibility. The second is data — data is now more readily available inside of companies for analytics, AI, and machine learning. The third is speed. Companies are getting serious about what realistically can be deployed in six months, Vohra says. And the fourth is a scarcity of and demand for talent — especially data science and cloud talent.
Indeed, cloud transformation is moving quickly. The global public cloud computing market is set to exceed $362 billion in 2022, according to Statista, and IDG reports that the average cloud budget is up from $1.62 million in 2016 to a whopping $2.2 million today. The pandemic led to significant increases in AI adoption across certain industries, like manufacturing. But to Vohra’s point, there’s a lack of skilled people to develop and maintain AI technologies.
“Ultimately, this technology has to create that value, or the value that companies or organizations are looking for depending on where they are, what they already have, and what they want to change,” Vohra said. “Analytics, AI, and automation practices all [have to come] together along with data management … to create the solutions required for a company to become intelligent.”
When asked about learnings that enterprises can apply to their AI strategies, Vohra said that understanding the needs of customers should be first and foremost, followed by training staff to use AI and completing the “last mile” of implementation ahead of productization, like data prep. While data is more available now within enterprises than arguably ever, organizations sometimes struggle to deliver on their data approaches. According to an MIT Technology Review Insights and Databricks survey, what remains one of the most significant challenges for executives looking to deploy AI is the lack of a central place to store and discover machine learning models.
But the benefits are worth overcoming the blockers. McKinsey predicts that automation alone could raise productivity growth globally by 0.8% to 1.4% annually.
“I think data is definitely the new oil, and it’s very critical to curate datasets properly. If you don’t have the right datasets, your AI actually can’t be effective and can’t learn,” Vohra added. “For most of these engagements, there’s a lot of time spent to increasing and enhancing the customer record. There are many things you can do with just by figuring out how to assess the right datasets, or curating the right datasets and then using patterns to forecast issues and raise alerts.”
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