In 1952, William Edmund Hick and Ray Hyman, a pair of British and American psychologists, set out to examine the relationship between the number of stimuli present and an individual’s reaction time to any given stimulus. As you probably guessed, the more stimuli a participant had to choose from, the longer it took them to decide which one to interact with. This was coined Hick’s Law and modern-day technology designers and marketers know it well. Less is more, and a failure to adhere to this maxim often results in “analysis paralysis” and failure on the user’s part to make any decision at all.
Drawing from this research, psychologists have applied it to both information theory and human information processing, which models how people absorb new information. In 1988, educational psychologist John Sweller formulated the theory of “cognitive load,” which refers to the used amount of working memory resources. Sweller found that there comes a critical point when the stimuli or information overwhelms this process, impeding both learning and decision making.
It doesn’t matter if you are the CEO of a Fortune 500 company or the primary caregiver for your family, if you are human and awake in 2021, you know what cognitive load feels like, just as you know the old adage less is more.
Whether you believe we’ve reached “Peak Information” or are moving into the “Post Information” age, we can all agree that we’re drenched in data. In business, this deluge is often likened to oil, or the hot commodity every enterprise is after–the more businesses can drill for it, the “richer” they’ll become. But is this really the case?
Every day, 2.5 quintillion bytes of data are created. If it’s too difficult to wrap your brain around 18 zeroes, look to the night sky instead. Over a year ago, pre-pandemic, there was 40 times more data found in the digital realm, than there are observable stars in the universe. Though 2021 estimates have yet to be released, the pandemic, the greatest accelerator of digital life the world has ever seen, ensures the latest figures will be equally, if not exponentially, mind-boggling.
But more data doesn’t mean better decisions, in business or in life. As Hick and Sweller discovered decades before, a surplus of information leads to less efficiency and poorer outcomes.
Think about this in your own life; how many times have you spent an hour clicking through hundreds of movies on the handful of streaming services you subscribe to, only to conclude that it’s too late to watch something? How many different news outlets are carrying the “same but different” take on the stock market? When you invest, do you do the work to figure out where to put your money, or do you rely on an “expert” to guide you, based on what matters to you, at this moment, with this set of goals? There are millions of apps available to you, but how many are on your phone right now? Of those, how many do you use every day? I’m betting very, very few.
The problem is “too much is too much.” We’re drowning in data, but know nothing. I could say the basic principles of economics are that “more of anything, means less of everything.” Data is no different. It’s only as valuable as the decisions it enables, and strategic decisions are a product of knowledge, rather than data. The wisest business leaders draw from the data to create information (think organized, structured or processed data), and then apply their own experiences and insights to find the “signal in the noise,” and plan for tomorrow. That’s literally the life of any executive in today’s modern enterprise.
If organizational wisdom is the goal, data is the raw material, and information is the result of processing all that data. The only way these drowning executives will stay afloat is by ignoring all that information and paying attention to what really matters, right now.
Line-of-business executives are demanding operational outcomes. Whether it’s to reduce costs, mitigate risk, or increase customer satisfaction, they are living in a hyper-competitive environment. Large enterprises are being squeezed at the top by peers with more aggressive digital adoption strategies while venture-backed startups are taking increasingly larger bites out of their ankles.
The race to digital transformation has only been accelerated by the pandemic, and large-scale investments in people and product have led to a mish mash of bespoke implementations that neither scale nor support the endless changes these executives must drive. It’s akin to having a Lego set emptied on your desk and no instruction manual in sight. Sure, the pieces will fit together to build something, but is it the thing you need at the time you need it?
In the AI space, no industry has been better at this then the Robotic Process Automation market. Ten thousand companies have purchased every “Lego piece” in hopes of getting to that place where the automation of manual, mundane, and messy internal work would result in something they can use.
But automated noise, is still noise.
The process of refining and extracting knowledge from that noise, will be the holy grail of the future-ready organization. It is the next phase of growth in the digital transformation journey all enterprises must take to remain relevant.
As Forbes, CIO.com and VentureBeat have clearly articulated, the failure of RPA is leading to an evolved view on the future of work. Rather than build science projects, we need to deploy science-backed products that map to specific business outcomes.
Instead of leading with technology, companies need to work backwards to implement the right tool for the right problem at the right time, in a form that solves for today and prepares organizations for tomorrow.
Having come through the trough of disillusionment, I am optimistic that the modern enterprise will embrace a new way of deploying technology, in artificial intelligence and data science specifically. So, no, the robots won’t be taking over. The next phase of growth in AI will be defined by software designed to demote the tech, promote outcomes, and preserve and codify organizational knowledge that leads to true transformation.