The new digital divide is between those using AI and those who are not. Companies must design and develop appropriate people, product, process and privacy solutions around the AI.
It took more than ten years for enterprises to grasp the concept of the cloud and to embrace it. Artificial intelligence is no different. While some organizations have heartily embraced AI and what it can do for information management and decision support, more companies are still in the process of maturing their understanding so they can make the best use of AI.
There is one thing that we already know: AI will be an integral part of how business is conducted in the future, and its impact is already being felt.
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“We are facing a new digital divide,” said Prashant Natarajan, vice president of strategy and products at H2O.ai. “Unlike the previous ones, this divide is not between geographies or economic systems but one that exists within businesses and enterprises—locally, regionally and nationally. This new digital divide separates the haves and the have-nots of digital transformation, namely those that are putting their data to work for them (via AI) and those that are not.”
Today’s AI use cases support this. Forty-one percent of professional marketers surveyed in 2021 said that the use of AI increased their revenues; and in healthcare, AI is sharpening the degree of accuracy in medical diagnostics and making suggestions and recommendations that are assisting in lowering mortality rates and increasing patient satisfaction.
“Human and organizational needs, business trends, evolving customer behaviors and rapid data and technology innovations are some of the key drivers that are making AI an essential foundation of the modern enterprise,” Natarajan said. “In a business landscape that is increasingly informed by both global and local trends, the need to put data to work is more important and relevant than ever before.”
Yet, there are still challenges in understanding and deploying AI that many companies face. It’s not just a matter of identifying the best business use cases for AI. Companies must also design and develop appropriate people, product, process and privacy solutions around the AI solution. If that doesn’t happen, the AI may not align well with the business, its people and its processes.
“The most advanced organizations that are digital at heart exhibit a fine-tuned reliance on people—in leadership, across cross-functional teams and within data, data science and analytics teams,” Natarajan said. “Enterprises that recognize the natural symbiosis between human and AI display striking differences between what they and their counterpart are able to achieve.”
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It’s a point well taken. Great AI doesn’t work in a vacuum. It coordinates with human decision-makers and operates in a symbiotic mode with humans so an optimum decision or operation can be arrived at or performed. The AI can read thousands and even millions of documents and images. It can use machine learning to identify repetitive patterns, and then deduce ideas for a medical diagnosis. At the end of the day, however, it is the X-Ray technician, the radiologist, the surgeon and the doctor who make the ultimate decision, taking into account not only what the AI tells them, but what they know from clinical experience.
To get to this point, companies must design the correct process interface that captures this man-machine symbiosis. That is what’s stymying companies. Where do you draw the line between what AI does and what humans do? How does this ultimate man-machine interface get seamlessly inserted into business processes to optimize operations and revenue? These are the salient questions enterprises must address.
“When investing in AI, think of people first, not technology,” Natarajan said. “Invest in your employees, so that building and growing AI knowledge and learning to incorporate into their roles becomes a long-term strategy that can unlock knowledge and drive new value. Executives’ awareness and investment in AI can support the development of AI for the organization.”