This is an example of confirmation bias.

Leaning toward information that supports your beliefs can lead to selective, flawed decision-making. In the tech industry, this often causes teams to ignore critical feedback, resulting in delays and budget overruns. Research shows that 58% of tech teams have done this, and executives are 70% more likely to approve projects when data aligns with their views, despite counter-evidence. Recognizing this bias can help you make more balanced, data-driven decisions, saving time and resources.

Most people still favour information that confirms their beliefs, even if they think they don't.

A survey found over 60% of business leaders place more emphasis on supportive data, despite opposing viewpoints. In tech, this can mean sticking with a preferred solution even when clear data shows it underperforms. Being aware of this bias is crucial, as it's linked to 65% of failed tech projects. Addressing it leads to more strategic, objective decisions.

This is an example of overconfidence bias.

Overconfidence leads to overestimating knowledge, abilities, or predictions, often resulting in risky decisions. In the tech industry, this can mean underestimating project complexities, leading to missed deadlines and cost overruns. Research shows that overconfident executives are 65% more likely to approve projects with unrealistic timelines, with 70% of these projects facing delays. Recognizing this bias helps ensure more realistic planning, better risk management, and successful outcomes.

Even if you feel cautious, overconfidence can still subtly influence decisions.

Data reveals that over 60% of executives underestimate project timelines, believing challenges can be managed without delay. In tech, this might mean underestimating the resources needed to launch a new platform, leading to rushed development and unexpected issues. Addressing overconfidence helps leaders plan with more accuracy and build reliable strategies that support project success.

This is an example of the sunk cost fallacy.

Sometimes called “throwing good money after bad,” this bias causes people to keep investing in failing projects because of previous investments. In the tech industry, 57% of companies continue to fund projects that are clearly failing due to past spending, leading to wasted resources and missed opportunities. Addressing this bias helps leaders make decisions based on future potential, not past costs, enabling more strategic pivots and resource allocation.

Be mindful—the sunk cost fallacy can affect anyone, often without realizing it.

Research indicates that over 70% of executives stick with projects longer than they should because of previous investments. In tech, this might mean continuing to invest in outdated platforms despite better alternatives. Companies can lose up to 40% more than planned budgets on projects that should have been halted earlier. Overcoming this bias can lead to more agile, cost-effective decisions.

This is an example of the planning fallacy.

The planning fallacy leads people to underestimate the time, effort, and resources needed to complete tasks, resulting in unrealistic deadlines and budgets. In the tech industry, this bias is common, with 75% of tech projects running over their original timelines due to unforeseen complexities. Addressing this bias enables more accurate forecasting, better resource management, and fewer last-minute crises, saving companies time and money.

Even if you plan carefully, the planning fallacy can still subtly affect your timelines.

Research shows that over 60% of professionals, including experienced project managers, tend to set overly optimistic deadlines, leading to delays and cost overruns. In tech, this often means projects take 30-50% longer than expected due to overlooked complexities. Being aware of this bias helps leaders build realistic timelines and set reliable expectations, improving project efficiency and resource allocation.

This is an example of anchoring bias.

Anchoring bias occurs when the first piece of information you receive becomes a reference point that heavily influences subsequent decisions. In the tech industry, this can lead to skewed project budgets, unrealistic timelines, or inflated expectations. Research shows that initial figures in negotiations can sway outcomes by up to 20%, even if they are arbitrary. Addressing this bias ensures more balanced evaluations, better budgeting, and effective negotiations.

Even if you believe the first offer doesn’t affect you, anchoring can be subtle and pervasive.

Studies indicate that over 50% of business negotiations are influenced by initial figures, even when there is no logical basis for them. In the tech sector, this can lead to inaccurate project estimates and cost overruns due to initial anchors. Being aware of this bias helps leaders approach negotiations and budgeting with a more critical eye, leading to better outcomes and strategic decision-making.

This is an example of groupthink.

Groupthink occurs when teams prioritize consensus over critical thinking, leading to a lack of diverse perspectives and missed opportunities. In the tech industry, this can cause teams to stick with familiar ideas and avoid exploring innovative solutions, even when fresh approaches could be more effective. Research shows that 58% of tech teams have made suboptimal decisions because dissenting opinions were not voiced or explored. Addressing this bias encourages open dialogue, critical evaluation, and creative problem-solving, leading to stronger, more strategic outcomes.

Even if you think your team values diverse perspectives, groupthink can subtly influence decision-making.

Studies indicate that over 60% of professionals have, at times, withheld dissenting opinions in team settings to avoid conflict or disruption, leading to groupthink. In tech, this might mean approving a project direction without fully discussing potential risks or alternatives. Recognizing this bias helps leaders foster a culture of open dialogue and healthy debate, ensuring that all ideas are considered and that the best solutions emerge through collaborative thinking.

This is an example of the illusion of control.

The illusion of control leads people to overestimate their ability to influence outcomes, even when success is largely dependent on external factors. In the tech industry, this can cause risky decisions, such as pushing through a product launch despite unpredictable market conditions or unforeseen technical challenges. Research shows that 55% of tech executives have taken on projects believing they could control outcomes, only to face delays and failures due to factors beyond their control. Addressing this bias helps leaders set realistic expectations, identify external risks, and plan more effectively.

Even if you feel cautious, the illusion of control can still subtly affect decision-making.

Data reveals that over 60% of executives tend to overestimate their influence on complex projects, leading to miscalculations and resource misallocation. In the tech sector, this might mean assuming teams can manage variables like customer adoption rates or third-party dependencies, only to find these elements are beyond direct control. Recognizing this bias enables leaders to develop more robust contingency plans and make balanced, informed decisions.

This is an example of status quo bias.

Status quo bias leads people to prefer maintaining the current situation rather than making changes, even when better alternatives are available. In the tech industry, this can cause teams to hold onto outdated systems, tools, or methods, missing out on opportunities for innovation and improvement. Research shows that 65% of tech companies admit to delaying updates or changes due to comfort with the existing setup, resulting in inefficiencies and higher costs over time. Addressing this bias encourages teams to be open to change, leading to more agile, forward-thinking decisions.

Even if you think you’re open to change, status quo bias can subtly influence your choices.

Studies reveal that professionals often stick with existing methods out of habit or comfort, even when newer solutions could save time and resources. In tech, this might mean continuing to use outdated software or processes instead of exploring modern, more efficient alternatives. Recognizing this bias helps leaders and teams remain adaptable, enabling them to embrace new technologies and strategies that can drive greater success and efficiency.

This is an example of authority bias.

Authority bias occurs when people defer to the opinions or recommendations of perceived experts or leaders, even if their insights aren't directly relevant. In the tech industry, this can lead to costly mistakes, like adopting strategies or technologies endorsed by industry figures without thorough evaluation. Research shows that 62% of tech executives admit to following advice from influential figures, even when internal data suggests otherwise. Addressing this bias encourages data-driven decisions, ensuring that strategic choices are based on relevance and evidence, not just reputation.

Even if you believe you’re objective, authority bias can still subtly influence your decision-making.

Studies reveal that over 65% of professionals are more likely to agree with recommendations if they come from someone with high status, even if the advice isn’t directly applicable. In tech, this might mean prioritizing suggestions from a well-known executive over input from technical teams who have deeper insights. Recognizing this bias encourages leaders to validate advice against data, leading to more reliable, strategic decisions that drive success.