The Hidden Biases Behind Commercial Decision-Making
Nowadays, most organisations invest heavily in analytical models, forecasts, and business cases to inform their decisions. Yet picture this: you’re surrounded by some of the most brilliant minds in the organisation, making critical decisions based on the data. However, behind the conclusions reached, there is a quiet, invisible force at play that no one is talking about: human judgement.
Bias, pressure, prior experience, and options framing often influence the decision-making process in ways that go unnoticed. Understanding these hidden patterns has the potential to improve the quality, consistency, and robustness of commercial decision-making across a variety of industries.
Why Rational Processes Still Produce Inconsistent Outcomes
We all ‘know and love’ structured governance processes: investment committees, procurement boards, approval gateways, and due diligence processes. Most organisations enforce structured governance frameworks to ensure evidence-based and rigorously evaluated decisions.
Unfortunately, structure does not remove bias – it simply changes where it enters the process.
Bias rarely waits for the final sign-off. In fact, it is introduced much earlier through:
The filtering and shortlisting of options
The skewing of how risks are framed
The unexamined assumptions presented as fact
The dominant narrative that captures the team’s imagination
As a result, two teams can analyse the exact same evidence and reach vastly different conclusions. This is not a failure of logic, it is simply how we navigate complexity and uncertainty.
Four Cognitive Traps Shaping Business Choices
Four well-established behavioural biases consistently influence investment, procurement, and strategic choices:
1. Loss aversion
Decision-makers tend to weigh potential losses more heavily than equivalent gains. This can lead to overly cautious decisions or the rejection of high-potential opportunities. A more drastic effect is observed when individuals settle for the status quo, even when performance is underwhelming. A prime market example is the collapse of Kodak. Despite inventing the digital camera, the company’s leaders feared the ‘loss’ of their existing film revenue. Their solution was to stick to an obsolete status quo rather than embracing the innovation that would have secured their future.
2. Anchoring
Initial figures or ingrained assumptions disproportionately shape later judgment. Early cost estimates, valuation ranges, or supplier benchmarks often remain influential, even when better data becomes available. This pattern is consistently observed in project planning, with empirical studies indicating that once a project manager establishes an initial ‘anchor’ estimate, they tend to under-adjust as new information emerges. This behaviour leads to distortions in subsequent forecasting throughout the project lifecycle [1].
3. Confirmation bias
Teams naturally seek evidence that supports a preferred option, while dismissing contradictory signals. This can weaken the challenge and foster overconfidence in a particular choice [2]. A real-life example is BlackBerry’s downfall. Their leadership believed customers would continue to prioritise physical keyboards. Therefore, they focused on evidence supporting this view and discounted growing demand for touchscreen apps and broader user experience. As a consequence, competitors like Apple and Android took the lead.
4. Sunk-cost fallacy
The more time, effort, or capital is invested in an initiative, the harder it becomes to step away from it. Organisations continue the pursuit of underperforming projects simply because stopping them feels like failure [3]. This can be seen in the ‘Concorde Fallacy’. The British and French governments continued to pour billions into developing the supersonic Concorde aircraft for nearly three decades, long after it was clear that the plane would never turn a profit, simply because they had already invested too much to back down.
On their own, these biases are subtle. However, when combined, they can quietly derail critical decisions.
Structural and Behavioural Drivers of Decision-Making
Decision-making is heavily influenced by how options are structured and presented. Small design choices such as framing, sequencing, and emphasis having a disproportionate impact on outcomes and often steer stakeholders towards safer or more prominent options. This reflects a fundamental aspect of human cognition rather than manipulation, meaning that improving decisions is not just about better data, but about deliberately designing the decision framework to mitigate bias.
In organisational contexts, decisions are rarely made in isolation and are shaped by group dynamics. Factors like seniority bias, dominant narratives, and lack of challenge can suppress critical debate. As a result, apparent alignment may simply reflect passive acceptance. Effective decision-making requires constructive friction to ensure that consensus is genuinely robust.
Reducing Bias in Practice
While bias cannot be fully eliminated, its impact can be reduced through more structured and intentional approaches to decision-making.
Separate evidence from interpretation: Clearly distinguish between data, assumptions, and narrative. Doing so makes it far easier to identify exactly where subjective judgement may influence the final conclusion.
Embed structured challenge: Do not assume constructive scrutiny will happen organically. Independent reviews, alternative scenarios, and formal due diligence are essential to guarantee a balanced evaluation.
Stress-test anchor assumptions: Force early assumptions to justify themselves as new information comes to light. This prevents outdated or biased starting points from influencing the final decision.
Conduct pre-mortems: Before signing off on a project, ask: “If this were to fail, what would be the cause?”. This exercise flushes out hidden risks that standard risk registers often overlook.
Optimise the decision environment: Ensure all options are genuinely comparable and trade-offs are transparent, so that no single perspective dominates purely because it was presented well.
From Behavioural Insight to Better Decisions
The key challenge in commercial decision-making is not a lack of data, but competing interpretations. This is where a structured Research & Strategy approach becomes critical. By combining strong market insight with clear business cases, benchmarking, and disciplined governance, organisations can:
Bring data to life by contextualising it, rather than viewing it in isolation
Mitigate bias by reducing the reliance on individual judgment
Foster consistency in how decisions are made
Instil greater confidence in commercial outcomes
Better Decisions are Designed, Not Assumed
By default, organisations focus their efforts on enhancing the quality of data, analysis and validation. However, another vital area to improve is the decision-making environment itself. Commercial success depends less on what the data shows, but on how that data is scrutinised, interpreted, and put into practice.
Recognising and addressing hidden behavioural biases is no longer optional. It is the foundation of better, more reliable, and entirely defensible decision-making.
References
[1] Lorko, Matej, Maroš Servátka, and Le Zhang. "Anchoring in project duration estimation." Journal of Economic Behavior & Organization 162 (2019): 49-65.
[2] Rau, Devaki, and Philip Bromiley. "A review of cognitive biases in strategic decision making." Long Range Planning 58.3 (2025): 102529.
[3] Roth, Stefan, Thomas Robbert, and Lennart Straus. "On the sunk-cost effect in economic decision-making: a meta-analytic review." Business research 8.1 (2015): 99-138.
Written by Alexandra Bicu
Edited by Kate Randall