As enterprise continues the process of digital transformation, it’s becoming clearer than ever that data itself has become a form of currency.
What do I mean by that?
Take a look. Everything generates data these days and that data has intrinsic values attached to it. I see data as the common language of humanity itself. We make decisions based on data. And we create data (our digital footprint) with everything that we do. As businesses are better able to leverage all this data, it becomes quite valuable, and that’s why I see it as a new type of currency.
The good news is this data we compile is much more stable than any other form of virtual currency. In almost all cases it’s reliable and trustworthy. The bad news is, effectively integrating digital driven data into our business processes still seems to elude most companies.
It begs the question, what good is data as currency if none of us know how to extract value from it?
Up until recently, most enterprises have expressed frustration that AI has yet to prove itself in a practical manner. In many ways, AI has been its own worst enemy. Operating under a mysterious set of rules only its engineers understand, it’s failed to convert all this data into gold.
In fact, Gartner predicts that through 2022, 85 percent of AI projects will deliver “erroneous” outcomes “due to bias in data, algorithms or the teams responsible for managing them.” While that number appears extreme, it points to the real struggle enterprise is having navigating the unchartered waters of AI integration into the larger business strategy. It’s like having a huge bank account of data available to you, but no ATM by which to dispense that data as cold-cash.
The secret to success may lie in bringing a human element to the marriage of AI and data. What experience has shown us is that when we only try to solve business problems from a purely engineering or technological perspective, we generally fail to render a practical solution. In essence, we are trying to force round technological pegs into square strategy holes.
But by working in a reverse path from the wants and needs of the business, back down through method an AI platform could service those needs, we come closer to finding a solution with real adoption potential.
The key to this new, mindful form of AI, is to focus on being aware and purposeful of the intentions and emotions we hope to evoke through any given artificial intelligent experience. The goal is to identify and articulate the core pain-points to solve – and the positive value the mitigation of those pain points would drive.
An organization struggling to build meaningful consumer engagements, for instance, could identify the core issues that are driving the state of stasis and then decide how AI could use data points to alleviate the problem.
In a nutshell, applying a human-centric, mindful approach to AI and how it uses “data currency” comes down to this: Identify and focus on the needs of people first, so that AI applications serve human needs. This is how data, intelligence, and experience will work together to enhance the human potential and unlock the value of our new data currency.