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Artificial Intelligence Drew Graham

Differences in AI Investment Accentuate the Digital Divide

Investments in advanced technologies like AI are a key to a successful digital transformation.

For many manufacturers, digital transformation efforts are yielding tangible results including operational efficiencies, improved time to market as well as the ability to better meet ever-evolving customer expectations. However, according to Forrester, up to 93% of companies agree that innovation technologies are necessary to reach their digital transformation goals.

For instance, an organization’s ability to understand and effectively leverage artificial intelligence (AI) has surfaced as a key differentiator.

According to a recent Cognizant report, “Investing in AI: Moving Along the Digital Maturity Curve,” AI plays a critical role in enabling businesses to churn through data at the scale and precision required to succeed in today’s global environment. The use of AI signals a shift in focus from the data collection phase, including initiatives like the Internet of Things (IoT) to generate data, to the data insights phase (i.e., AI). Not even half of firms classified as beginners think they’ll have achieved AI maturity over the next three years, whereas half of leaders already do so today.

Widening the digital divide

Perhaps the biggest surprise, according to Cognizant Senior Vice President and Global Head of AI Bret Greenstein, surfaces when considering current and future investments for leaders and beginners. 

“It turns out leaders are not only investing more now, they are also accelerating that investment more than beginners 3 and 5 years out,” he says. “That means the digital divide will grow for those who are underway and accelerating their transformational journey.  Given the technical and business skills needed for an AI based digital transformation, some companies will never catch up. This is a bigger impact than we saw in the past with the migration to web/mobile where there was room for companies to be ‘fast followers’.” 

Closing the gap

Effectively closing the gap means organizations need to embrace evolutionary AI tactics to get predictive and prescriptive analytics capable of driving the business. It’s critical to enable data and AI to scale beyond the limits of what’s been possible with reporting and data warehouse technologies.

Where AI is most impact for today’s manufacturers is in the ability to go beyond leveraging AI to improve the automation of menial tasks to ultimately expand the organization’s modelling and simulation capabilities, according to the study results. “Together, modeling and simulation deliver the power of evolutionary computation. Using large numbers of parallel simulations of the business and possible decisions, organizations can find the best possible outcomes. And by learning from these simulations — in parallel as they play out — they can uncover optimal outcomes more quickly and cost-effectively than any other method.”

Of course, full-blown AI deployments remain the exception rather than the rule across industries, explains Greenstein. “Today, most AI projects are data projects and up to 90% of the work required lies in getting access to and preparing data for use with AI,” he says. “To turn the tide and see the greatest impact from AI, businesses should hone their strategies and establish a solid data foundation, maintain a human-centric approach, and develop their governance structures. By getting these basics in place, organizations will be able to harness AI’s creativity to design better products, enhance personalization, streamline and automate operations, improve cost containment and reduce overall risk which, ultimately, all combine to deliver optimal business outcomes.”

Greenstein tells IndustryWeek, “The truth in AI and data transformation is that getting started begins a process of learning – business, technical and people – that helps to identify more areas where it applies which, in turn, further grows skills,” he says. “Some companies mentioned that they started early, but had a tough time realizing value so adoption slowed, keeping them in the beginner category. In these cases, we saw the companies that treated AI and data as a business imperative first had the highest success with early projects, which lead to them to becoming leaders.” 
 

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