K
Kathleen Martin
Guest
“We need to be an AI-enabled company.” Replace the “AI” with any technology from history and this comment becomes a common refrain across businesses lured by the promises of new technology and fueled by FOMO (a fear of missing out). As enterprise strategists and former CXOs who have lived through many “technology is the solution, now what was the problem?” conversations, we talk extensively about this issue. To paraphrase Roy Amara, we overestimate the impact of a new technology early on. When it falls short of our expectations, our disappointment means we are less willing to adopt it when it is truly ready. The reality is that often it is only when we change how we operate and organize that many new technologies come into their own.
But what happens when we really combine organizational change and technology in the guise of artificial intelligence? Whilst companies are only starting to see what’s truly possible, they are discovering previously unattainable insights into their customers, employees, financials, environment, new products, and more. The accessibility of cloud-based technology and the nexus of engineering, mathematics, and organizational change promise solutions or improvements to our most complex issues ranging from climate change and poverty to criminal exploitation and disease. Many of our current use cases seem mundane in comparison with what the future holds, but new technology is still fueling business value into the trillions of dollars. Whether it’s a restaurant’s menu board customized to your locality, precise metrics about your favorite sports team, customized retail or banking offerings delivered to your phone, or the faster discovery of medical interventions, the promise of applying sophisticated technology to data has the power to amaze us and our customers.
So, if we take a rational, step-by-step approach to AI, where should we start so that we can truly capitalize on its potential? How do we position ourselves to identify and scale opportunities, while preventing ourselves from going down blind alleys? At the risk of sounding repetitive, many of the steps are the same as with any new technology. Let me share some basic steps any company can take—and are probably already taking—to learn how AI can supercharge their business by fully capitalizing on the data they already have.
Educate
History is littered with examples of new technologies that were either treated as a dark art that must be left to the wizard we now call the CIO to figure out, or as a magic spell that with a sprinkling of pixie dust will transform an organization merely by uttering its name. Understanding that AI is an enabler, not an outcome, education from the C-suite down is important. Dispelling myths behind AI and foundational components helps ground everyone in the considerations that are needed when thinking of applying AI to a business problem or opportunity. Don’t take for granted that everyone (or anyone) knows what AI and its sub-disciplines are. For the sake of clarity, AI encompasses the use of systems to perform tasks that usually require human intelligence. This is defined in a very narrow way, unlike what is referred to as “artificial general intelligence,” which aims to replicate human behavior but is just a distant dream. Most AI is based on machine learning to create a model that represents the decision process required often by divining patterns in data.
Continue reading: https://www.forbes.com/sites/amazonwebservices/2021/11/04/making-artificial-intelligence-real/?sh=4f98bef078b1
But what happens when we really combine organizational change and technology in the guise of artificial intelligence? Whilst companies are only starting to see what’s truly possible, they are discovering previously unattainable insights into their customers, employees, financials, environment, new products, and more. The accessibility of cloud-based technology and the nexus of engineering, mathematics, and organizational change promise solutions or improvements to our most complex issues ranging from climate change and poverty to criminal exploitation and disease. Many of our current use cases seem mundane in comparison with what the future holds, but new technology is still fueling business value into the trillions of dollars. Whether it’s a restaurant’s menu board customized to your locality, precise metrics about your favorite sports team, customized retail or banking offerings delivered to your phone, or the faster discovery of medical interventions, the promise of applying sophisticated technology to data has the power to amaze us and our customers.
So, if we take a rational, step-by-step approach to AI, where should we start so that we can truly capitalize on its potential? How do we position ourselves to identify and scale opportunities, while preventing ourselves from going down blind alleys? At the risk of sounding repetitive, many of the steps are the same as with any new technology. Let me share some basic steps any company can take—and are probably already taking—to learn how AI can supercharge their business by fully capitalizing on the data they already have.
Educate
History is littered with examples of new technologies that were either treated as a dark art that must be left to the wizard we now call the CIO to figure out, or as a magic spell that with a sprinkling of pixie dust will transform an organization merely by uttering its name. Understanding that AI is an enabler, not an outcome, education from the C-suite down is important. Dispelling myths behind AI and foundational components helps ground everyone in the considerations that are needed when thinking of applying AI to a business problem or opportunity. Don’t take for granted that everyone (or anyone) knows what AI and its sub-disciplines are. For the sake of clarity, AI encompasses the use of systems to perform tasks that usually require human intelligence. This is defined in a very narrow way, unlike what is referred to as “artificial general intelligence,” which aims to replicate human behavior but is just a distant dream. Most AI is based on machine learning to create a model that represents the decision process required often by divining patterns in data.
Continue reading: https://www.forbes.com/sites/amazonwebservices/2021/11/04/making-artificial-intelligence-real/?sh=4f98bef078b1
