Unleashing the True Potential of Your Innovation Process
In Part I of “Navigating Cognitive Biases: Unleashing the True Potential of Your Innovation Process,” (Jestha 2082 – Vol 4 – Issue 8) I discussed five critical biases – Anchoring Bias, Confirmation Bias, Status Quo Bias, Overconfidence Bias, and Not-Invented-Here (NIH) Syndrome; and explored their impact on innovation decision-making processes. Building upon that foundation, this article will address the remaining seven cognitive biases that significantly influence innovation effectiveness: Planning Fallacy, Availability Heuristic, Sunk Cost Fallacy, Groupthink, Framing Bias, Survivorship Bias, and Loss Aversion.
In Part I we explored how critical decision-making moments across the innovation value chain; from ideation to commercialisation, are often undermined not by flawed processes, but by flaws in human cognition. Discussing five key cognitive biases and how they distort perception, restrict creative thinking, and derail objective decision-making the last article highlighted how these invisible, deeply embedded mental shortcuts frequently lead innovators astray, and emphasised that awareness and deliberate mitigation strategies are vital for sustaining innovation momentum. This foundational understanding now sets the stage for our continued exploration in Part II, where we turn our attention to seven additional biases that silently shape innovation decisions, and how to master them.
Innovation thrives in environments that embrace uncertainty and non-routine problem-solving. Unlike structured, routine decisions, innovation-related decisions tend to be synthetic, conceptual, and behavioural, relying substantially on intuition alongside rational analysis. However, this creative process can become compromised by psychological biases that distort perception, judgement, and decision-making. Recognising these biases empowers leaders and innovators to make more objective and productive choices. Let me present the discussions about rest of the seven cognitive biases.
Understanding rest of the seven Cognitive Biases in the Innovation Process…
6. Planning Fallacy
The Planning Fallacy refers to the tendency to underestimate time, costs and risks, while overestimating the benefits associated with a task or project. This cognitive bias arises primarily because individuals and teams often base future projections on overly optimistic scenarios, neglecting historical data or failing to sufficiently account for unforeseen disruptions and complexities. In corporate innovation, such optimism frequently overlooks the actual intricacies of product development, regulatory hurdles, competitive reactions, and market uncertainties. Consequently, it often leads to overly ambitious timelines, underestimated budgets, and inadequate contingency planning, potentially jeopardising strategic innovation objectives.
Examples:
- Startup: A startup founder confidently schedules a complex product launch within three months, neglecting past experiences where similar projects required twice as long.
- Corporate: A corporation sets overly optimistic deadlines for launching an innovative digital service, causing rushed execution and compromised quality.
Mitigation Strategy:
Regularly utilise historical data from previous innovation projects. Adopt agile methods and build buffer times into schedules, consistently updating plans as new information emerges (Glaveski, 2020). To effectively mitigate Planning Fallacy, it is essential for teams to adopt a disciplined approach to project estimation, integrating realistic historical benchmarks and systematically capturing lessons learned from past innovation efforts. Leveraging agile methodologies helps break down complex projects into smaller, manageable stages, allowing continuous evaluation and iterative adjustments. Moreover, proactively incorporating strategic buffer periods and contingency resources helps teams accommodate unforeseen challenges and delays, significantly improving their capacity to deliver successful innovation outcomes within realistic parameters.
7. Availability Heuristic
Availability Heuristic bias involves making judgements based on immediately available information rather than a comprehensive set of data. This cognitive bias often occurs because individuals tend to rely on recent, vivid, or emotionally impactful examples that readily come to mind, assuming these are representative of the broader reality. In corporate innovation contexts, such reliance can distort strategic decisions, as teams disproportionately emphasise recent successes or high-profile failures without adequately considering less visible, yet equally critical data points. Consequently, decision-makers may prioritise resources ineffectively, overlook emerging trends, or misinterpret market dynamics, compromising the organisation’s strategic agility and innovation effectiveness.
Examples:
- Startup: A founder over-invests in social media marketing after observing a viral success, overlooking more effective but less visible marketing strategies.
- Corporate: Executives base innovation investment decisions on recent high-profile competitor successes, neglecting broader industry data that suggest alternate trends.
Mitigation Strategy:
Promote decision-making based on diverse datasets and thorough market analyses. Encourage teams to seek out balanced perspectives beyond readily available examples (Tovmasyan, 2020). Organisations should systematically ensure comprehensive data collection and validation from multiple, diverse sources rather than defaulting to easily recalled instances or anecdotal experiences. Decision-makers must consciously expand their informational boundaries, seeking historical patterns, quantitative analyses, and insights from less obvious or less immediate sources. Creating structured decision-making protocols that explicitly require consideration of broader datasets and competing viewpoints further helps teams overcome impulsive reliance on readily available but potentially misleading information.
8. Sunk Cost Fallacy
The Sunk Cost Fallacy is the tendency to continue investing in projects due to previously committed resources, regardless of diminishing returns.
We may have poured enormous effort into improving the performance of an employee whom we knew we shouldn’t have hired initially. Similarly, banks often extend additional funding to failing businesses to ‘protect’ earlier investments; known as escalation of commitment. Sunk costs, irrelevant to present decisions, weigh heavily due to ego, fear of criticism, and self-esteem, leading to compounding errors.
Examples:
- Startup: Continuing development on an ineffective product because of substantial initial funding and efforts already expended.
- Corporate: Persistently funding a failing business unit due to previously high investments and perceived reputational risks of closure.
Mitigation Strategy:
Seek unbiased input from uninvolved third parties. Evaluate past decisions objectively, reinforce a culture that values decision quality over outcomes, and mind you, ‘when you find yourself in a hole, stop digging’. It is critical to actively involve impartial advisors who were not associated with previous decisions, ensuring objective evaluation free from emotional entanglements or reputational concerns. Additionally, leaders must openly acknowledge and address psychological factors such as wounded self-esteem, ego protection, and fear of external criticism that can reinforce commitment to failing initiatives. Establishing a corporate culture that rewards high-quality decision-making rather than merely successful outcomes encourages transparency and empowers teams to swiftly pivot away from ineffective endeavours, thus preventing the compounding of errors.
9. Groupthink
Groupthink occurs when the desire for consensus overrides critical evaluation, hindering innovation by suppressing alternative views. This cognitive bias typically emerges in cohesive teams or hierarchical corporate structures where harmony and conformity are implicitly prioritised over rigorous analysis and constructive criticism. As a result, team members may self-censor doubts or differing opinions, depriving the organisation of vital alternative perspectives necessary for robust innovation. Consequently, this suppresses meaningful debate, limits exploration of creative solutions, and increases the likelihood of suboptimal decisions that might have been avoided through more open and critical deliberation.
Examples:
- Startup: A founding team unanimously agrees to pursue a risky market without adequately debating its viability, due to fear of disagreement.
- Corporate: A corporate innovation committee quickly approves a concept (proposed by the CEO) without critical analysis, suppressing concerns about market readiness.
Mitigation Strategy:
Foster psychological safety and encourage diverse opinions. Appoint a devil’s advocate, invite external experts for critique, and normalise respectful dissent (Board of Innovation, 2021). Organisations should deliberately structure meetings and innovation reviews to explicitly solicit and protect independent perspectives, creating an environment where challenging prevailing assumptions is actively rewarded. Introducing practices such as anonymous feedback mechanisms and structured debates can significantly reduce conformity pressure, thus enabling more comprehensive evaluation of innovative ideas. Additionally, engaging with external advisors or subject matter experts regularly can inject fresh viewpoints and unbiased evaluations, helping teams to rigorously assess proposals and mitigate the detrimental impacts of consensus-driven conformity.
10. Framing Bias
Framing Bias refers to decision-making influenced by how information is presented rather than its substantive content. Framing Bias arises when the same information, depending on how it is worded or presented, leads to different decisions – highlighting our sensitivity to context rather than content. In innovation-related decision-making, how a proposal is framed (as a potential gain vs. a potential loss) can drastically sway support or rejection, regardless of the underlying data. For instance, a sales manager may frame a discount as ‘you save 15%’ rather than ‘you’re paying 85%’ – the former evokes a positive emotional response, while the latter emphasises cost, though both describe the same reality. Similarly, in internal budgeting or innovation pitches, how a new initiative is positioned can unconsciously steer stakeholder support.
Examples:
- Startup: Framing potential customer growth optimistically leads to underestimating required investment.
- Corporate: Presenting new initiatives solely in terms of cost savings, neglecting potential quality enhancements.
Mitigation Strategy:
Don’t automatically accept the initial frame; deliberately reframe problems multiple ways. Seek neutrality, and constantly reassess how changing frames might alter your decisions. Challenge others to articulate their perspectives differently. Leaders should develop the habit of routinely reframing problems from different angles; such as viewing them through the lens of cost vs. value, short-term vs. long-term impact, or customer-centric vs. operational perspectives. Encourage teams to pose problems in a neutral or redundant way that combines both gains and losses to reveal hidden biases in interpretation. When evaluating proposals or recommendations, ask how the conclusion might differ if the situation were framed differently. Importantly, avoid framing the problem too early, especially in high-stakes innovation decisions; and be cautious not to unintentionally anchor your team or advisors with your own assumptions, as these may echo back as confirmation rather than critique.
11. Survivorship Bias
Survivorship Bias occurs when successful cases overshadow unsuccessful ones, distorting perceptions of reality. This bias occurs when organisations focus disproportionately on visible success stories; startups that scaled, products that succeeded, or strategies that paid off, while ignoring the often larger pool of failures that followed the same path. This creates a skewed perception of what drives success, leading to flawed benchmarking and overconfidence in imitating winning models. In innovation strategy, this bias can result in copying superficial tactics without understanding the deeper, less-visible factors or the contextual reasons behind both success and failure.
Examples:
- Startup: Entrepreneurs emulate only successful startups without studying failures, resulting in misguided strategies.
- Corporate: A corporation benchmarks innovation strategies solely against industry successes, ignoring valuable lessons from companies that failed.
Mitigation Strategy:
Actively analyse both successful and unsuccessful cases. Ensure balanced benchmarking and comprehensive case studies to provide realistic and actionable insights (Decision Lab, 2022). Organisations should build mechanisms to capture lessons not just from wins but also from failed projects, competitor collapses, or initiatives that never reached the market. Conducting post-mortems on failed innovations alongside success case reviews ensures more balanced learning and realistic strategic planning. Encourage teams to look beyond best practices and explore ‘missed practices’ that reveal what not to replicate; and why.
12. Loss Aversion
Loss Aversion involves a preference for avoiding losses over achieving equivalent gains, discouraging risk-taking. This bias stems from the psychological insight that losses feel nearly twice as painful as gains feel rewarding. In corporate innovation, this can manifest as excessive caution – where decision-makers avoid bold initiatives not because they lack merit, but because the fear of potential failure outweighs the prospect of breakthrough success. As a result, companies may cling to incremental improvements while missing transformative opportunities that could redefine their competitive position.
Examples:
- Startup: Founders avoid pivoting a failing strategy, fearing potential losses rather than potential opportunities.
- Corporate: Companies avoid innovative projects with high potential but uncertain outcomes, prioritising predictable incremental improvements.
Mitigation Strategy:
Frame innovation as a necessary exploration rather than potential loss. Structure innovation initiatives with controlled risks and encourage small, iterative experiments to minimise fear of failure (Fung, 2019). Leaders should redefine innovation not as a binary win-or-lose gamble, but as a learning-driven process where every outcome contributes to strategic insight. By designing innovation portfolios with a mix of low-risk experiments and high-reward opportunities, organisations can buffer potential setbacks while still fostering ambition. Celebrating learning from failed pilots – as long as they were well-reasoned – helps normalise intelligent risk-taking and gradually shift the organisational mindset from fear of loss to pursuit of progress.
Building a Bias-Resilient Innovation Culture
Integrating our discussions from both parts, it’s evident that navigating cognitive biases effectively requires systemic awareness and deliberate practice. Organisations must champion psychological safety, continuous learning, and structured decision-making frameworks that embed bias awareness deeply into their innovation DNA. Leaders who master cognitive biases not only foster cultures of sustained innovation but significantly elevate their organisational competitiveness.
Remember, biases will always exist, but their control lies in your proactive response. Strive not to eliminate biases but to master them, turning potential hindrances into stepping stones toward innovation excellence.
Reflect upon these biases within your organisations. What biases have you encountered or fallen prey to, and how did they shape your innovation outcomes? Share your experiences and strategies; let’s collectively master the art of bias-resilient innovation.
References
- Glaveski, S. (2020). 36 Cognitive Biases that Inhibit Innovation.
- The Decision Lab. (2022). How Biases Can Color Entrepreneurial Decision-Making.
- Board of Innovation. (2021). Cognitive Biases in Innovative Thinking.
- Tovmasyan, G. (2020). How Do Psychological Factors, Cognitive Biases and Cognitive Dissonance Affect Work Performance and Decision Making?
- Fung, K. S. N. (2019). How Cognitive Biases Can Help Explain the Imbalance of Exploration and Exploitation in New Product Development.
(Khatri is Management Consultant and Educator)
