Actions and decisions, taken thousands of times every day by everyone in every matter – largely determine our degree of success and failure. The context and impact of everyday decisions are often perceived as somewhat limited, due to a short-term outlook and delayed gratification. However, the effects of decisions compounding over time are often neglected. This is true in many aspects of our lives, from a business marginally increasing its expenses, to deciding upon what to eat. Decisions have a long-term impact on your life, on other people’s lives or on the outcome of a project.

Nowadays, the complexity levels of decisions making have risen owing to the current era of information affluence. Adding in confusion and making evaluating information essential to differentiate evidence/facts and opinions and rumours. 

Why is human decision making flawed? 

As defined by William Starbuck, “Decision implies the end of deliberation and the beginning of the action.”. The deliberation process by which each of us goes through is crucial to the decision itself. This process of weighing the different options is relying on our ability to make a judgement, and the capacity to look past our biases. It isn’t as intuitive as we may think. Our decisions process is foggy and filled with biases and other thinking errors. Pick up the book The Art of Thinking Clearly – by Rolf Dobelli to read about 99 different biases and thinking errors. The following three bias are good examples of everyday decisions and business decisions:

  • Confirmation bias – finding evidence that supports what we already believe.
  • Dunning-Kruger effect – thinking you know more than you do. 
  • Cognitive dissonance – mental discomfort experienced by a person who simultaneously holds two or more contradictory beliefs, ideas, or values.

I am sure we can all think of times when we or others have taken this path. If you want to learn more about the academic view on decision-making and bias, here is a link.  

Can machines make better decisions? 

Theoretically, machines have no guilty conscience or moral compass. Also, machines don’t exhaust besides mechanical breakdown or having “bugs” in their implementation.
Machines do a better job when the decision depends on predefined characteristics e.g. in production lines if a product is outside the defined metric, it is classified as not suitable. On the other hand, machines such as AI are only as good as the training data we give it, making them dependent on us. The World Economic Forum’s publication “AI isn’t dangerous, but human bias is”, emphasizes on proactively identifying and managing any bias present in the training data used. Otherwise “the system could pick up bias and amplify it – or at very least, perpetuate it”; “algorithms don’t become biased on their own – they learn that from us.

There would not be anything to fear from AI and robotics if humans were “perfect”. 

How you can make better decisions 

From our own experience, it is worth addressing biases in decisions making. We think that with a few simple steps noticeable improvements can be made. Let us take a good example from the TED talk by J. Marshall Shepherd: “3 kinds of bias that shape your worldview”. In short, here we present three points on how to make better decisions: 

  1. Taking inventory of your own biases and where they come from. 
  2. Then to evaluate your sources, what do you consume your information from?
  3. Speak out, share how you evaluated your biases and evaluated your sources. 

Constantly re-evaluating our perceptions and seeking if we hold a right interpretation of the information obtained is a necessity in business to remain relevant. Unlike science, in business, you find grey areas. Hence, acknowledging the other’s beliefs is essential for effective communication. Foresight and evaluating options will go a long way.

Decision making should be treated as strategic moves - identify and tackle biases are essential.


We suggest a mixed approach “fit for purpose”. Machines should take repetitive decisions, while higher level decisions with a degree of uncertainty remain in the ultimate authority of responsible adults. We can use AI-powered systems to structure data, find patterns and similarity…all adding to an informed decision by the person in charge.

At drkv “Our mission is to enhance individual and organizational health and performance.”. In our incubator, we aim first to understand the current environment so to be able to create viable solutions. We take note of our biases and go through thorough research in the development stage to identify pieces of evidence to build upon, filtering out misinformation.

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