The following is an excerpt from the publication “The Intuition Gap” by Kimberly Arnold & David Mayer
The Highly-Trafficked Intersection of Science & Humanity
We eternally find ourselves evaluating opportunities and challenges that stem from the intersection of science and humanity. Still, the very notion that evidence and common sense can compete is an uncomfortable one for some.
Social scientists, psychologists and neurologists have long debated the influences of left-brained vs right-brained tendencies, particularly when it comes to issues like personality, leadership and decision-making. Is it better to be logical, analytical and data-driven? Or creative, free thinking and big-picture oriented? Is either ultimately a reliable predictor or measure of long-term aptitude or potential? And, perhaps more importantly, how are the these answers influenced in the context of computer and machine-based technologies – especially when tools like artificial intelligence and machine learning are advancing at such an unprecedented pace?
Thankfully, this drawn-out debate is coming to a close, primarily because the realities surrounding artificial intelligence, machine learning and other cognitive advancements are answering the logic vs creativity argument for us. Simply stated, the most efficient and transformational advancements require both.
The Intuition Gap Defined
The Intuition Gap is the phrase that we coined to define a void that needs filling – the sought-after equilibrium where technology-enabled cognition and common sense not only co-exist, but foster and enable one another.
The most successful leaders and organizations of the future will be those who recognize The Intuition Gap, apply the most advanced capacities of both technology and the human condition to fill that gap, and utilize those outcomes to differentiate an organization throughout every phase of the life cycle.
At its most basic, we could consider intuition as the very human ability to simultaneously remember and forget. To forget past experiences, competitors’ successes, customer feedback, industry trends, social media firestorms, sales figures, and even shareholder meeting minutes. To remember the lessons of those same experiences and to apply them the next time we have to make a “gut instinct” decision. Intuition tells us that something is the right thing to do, even if every shred of past evidence and data contradicts our thoughts.
The most common response to intuitive thinking from anyone with a “left-brained” mentality is typically, ‘that’s crazy,’ however, it’s impossible to ignore how many innovative solutions the world has benefited from simply because one individual asked what someone considered, at the time, to be a nonsensical question? While, illogical to some, without intuition, the iPad doesn’t exist, nor does Uber, or Spotify, or dozens of other products and services that make our lives more convenient and productive every day.
Conversely, those with characteristics typical of the “right-brained” challenge sole reliance on data in decision-making. These individuals tend to “trust their gut”, and know that outcomes are only as reliable as the core information the analytics are based on. After all, we are still in a preliminary phase of technology innovation where predictive analytics are limited by what Dave refers to as the “tyranny of historical data” (i.e., past performance does not predict future results). Additionally, there are plenty of examples of mass volumes of data being isolated, segmented or manipulated to reach a particular conclusion.
Artificial intelligence and machine learning initiate with human influence. Someone needs to ideate a concept worth exploring and then set the machines on a course of exploration by defining “what is true first”. The outcomes being generated in the cognitive space have a long (long) way to go before they can sense, act, inspire and innovate better than humans do, yet tools like Robotic Process Automation are too efficient NOT to consider. At least for now, The Intuition Gap tells us that our approach needs to lie in the middle so that we can maximize every advantage.
The Power of Collaboration
When chess computers first appeared on the scene in the 1970s and 1980s, they were slow, predictable, and easily beaten by the masters and grandmasters of the day. They slowly improved, until Deep Blue’s defeat of Kasparov in 1997 showed the world that a new era had dawned. Chess computers can today regularly defeat grandmaster-level players, but in the midst of all this technology, a new trend emerged: Computer-human hybrid teams began to defeat chess computers, and on the heels of that revelation, human players coached by computers began to become the new standard in chess playing.
The takeaway? In our race to make a dichotomy out of humans vs computers, we overlooked the awesome potential of them working together…a reinforced exceptionalism that we argue herein is only possible if it stems from the collaboration fostered by The Intuition Gap. When factors like strategy, bias, customer experience, brand capital, social good, public policy and ethics are further factored into the discussion (all focus areas and chapters in this book) – it becomes even more evident that a harmonious juxtaposition between cognition and creativity is the only logical path forward.
The Role of AI, ML and Automation in Organizational Evolution
What role do these technologies – automation, machine learning, and AI – play in the core life cycles or phases of organizational evolution?
As entities launch or initiate growth, significant risk and uncertainty are likely to define the environment. Variables are introductory and there is less internal history for the organization to use as the foundation for predictive analytics. In this framework, leaders and entrepreneurs tend to rely on independent human intuition for decision-making.
Scaling, by definition, shifts an organization’s priorities. Replication, streamlining and volume serve as the foundation for improving efficiency, which (in theory) should progress outputs, revenues, and profits. Accordingly, if outputs are increasing, so is the corresponding data, which means automation tools related to collection, research and analytics can be exceptionally valuable in achieving these aims.
Long-term viable entities work to leverage the advantages of both scenarios. One division or department may be innovating while another is an established market leader. Uncertainty and complexity have likely evolved to overlapping intricacies, with more divergent variables and stakeholders demanding influence. In this environment, augmentation – the balance and counter-leveraging of automation, artificial intelligence, machine learning, collaboration and creativity – is the best path to sustainability.
Potential & Outcomes
Technology, artificial intelligence, machine learning and other cognitive/logic applications are integral to enhancing every one of these phases, which inherently means that they have the potential to improve Strategy itself. The top priority for any enterprise is to continuously increase value for its stakeholders and customers. Accordingly, then, any decision that allows an entity to better its products/services or distinguish itself in the marketplace should be open to the insights that artificial intelligence and machine learning can bring. This can be witnessed in a variety of forms – the revelation of unexplored opportunities, improved efficiencies, key performance indicators (KPIs) that better measure objectives, the enablement of scaling, and more. If the utilization of artificial intelligence and/or machine learning can help foster these aims, why would any leader not want to invest (at least on a basic level) to learn how how?
Individuals who simply think of artificial intelligence and machine learning as sections that need to be incorporated into an organization’s next IT Plan are not only missing the point…but also the potential. One time (and one time only), a strategic decision needs to be made to embrace tools like artificial intelligence and machine learning. After that, the wheels are put into motion and cognitive influence is a reality. The decision then shifts as to how we, as leaders, manipulate the ratios or percentages between creativity, augmentation and automation to further and improve the strategy and long-term performance of our organization.
Our upcoming book is not about cultivating intuition, the neuroscience of intuitive decision-making, or even why some intuition works and some doesn’t. Those topics have been explored (to death) in countless other business books, blogs and websites. Our focus is how the age of artificial intelligence, machine learning, and cognitive automation allows any firm able to unlock their full decision-making potential…and slingshot past their competitors in the process.
When we achieve the right balance between the influences (the formula isn’t the same for every entity), transformational change is indeed possible. That is how we bridge The Intuition Gap.
About the Authors
Kimberly Arnold is the CEO and Founder of Escalate Solutions, a specialized consultancy focused on strategy, growth, scaling and long-term sustainability of organizations. A three-time business owner, she offers more than two decades of experience supporting the marketing, operations and infrastructure of rapidly expanding and diversifying business environments. A published author, sought-after board member and compensated speaker, she frequently shares her expertise on such diverse topics as entrepreneurship, operational efficiency and corporate/community collaboration. Kimberly graduated summa cum laude from Colorado State University with a BA in Political Science & International Relations, studied Arabic at the University of Virginia, and recently earned Executive Certificates from the Cornell University SC Johnson Graduate School of Business in Data Analytics, Financial Management and Measuring & Improving Business Performance.
David Mayer has over twenty-five years of experience in strategic marketing, data sciences, brand management, corporate finance, and management consulting. He currently serves as Principal Analyst for Digital Transformation Services at NelsonHall Research, including AI, blockchain, and cognitive automation, after heading up global operations for Insights and MarTech Analytics at Wipro Digital. Previously, he served as EVP of Specialized Expertise and CDO at Aristeia, and prior to that, Senior Partner and Director of the Business Strategy Practice at Extelligent. Mr. Mayer has extensive working experience in international business, with previous consulting engagements in 14 countries on four continents. A published author in the field of marketing theory, Mr. Mayer holds a BS in Finance from Colorado State University, as well as an MS in Marketing and an MA in Anthropology from the University of Colorado.