is a systematic and analytical thought process that follows a known step-by-step progression similar to a straight line. Linear thinkers view a problem as a process with a set starting point that follows a sequence of connected series, ultimately leading to a solution … thoughts flow in a straightforward, logical way, and progress in a stepwise fashion. Linear thinkers are considered left-brained because the left side of the brain is associated with methodical and analytical thinking.
Note that there are variations on these definitions, including lateral thinking, systems thinking, design thinking and fragmented thinking – among others – but for our purposes here the simple linear versus non-linear distinction is fine.Linear and non-linear thinking can be good or bad, depending on the problem-solving context. Linear thinking is generally “easier” because the starting point is known.
“Linear thinkers’ brains are wired differently than non-linear thinkers. As engineers , information technology specialists, accountants and other research-based professionals, their linear style of thinking generally serves them well in their chosen professions. However, that can change as they move up the chain of command to leadership roles.” It can also change when a new technology arrives on the scene with its own kind of “thinking.
“By combining the efficiency of linear AI with the innovative power of GenAI, you can maximize your return on investment and drive real business transformation.”The investment strategy is clear, but since most managers and leaders are linear thinkers ,Many professionals are comfortable with the predictability and structure of linear models. Shifting to a more fluid, exploratory approach of non-linear thinking can feel uncertain and risky.
Machine learning and generative AI offer enormous opportunities to save money and make money. In order to identify and optimize these opportunities, companies need to search and destroy embedded linear thinking. While this does not mean there’s no payoff to linear problem-solving – which there clearly is – it means that in order fully leverage generative AI new approaches to “thinking” must be adopted.