“Computer Says No”: How a Closed Mindset Stifles Innovation in Decision-Making

In recent years, data-driven decision-making has become the backbone of many organisations. It’s efficient, measurable, and often straightforward. This approach focuses on “number-goes-down/up” thinking, aiming to reduce costs, boost efficiency, or meet other easily quantifiable targets. 

While this can streamline processes, it also risks shutting down creativity and innovation. This is especially true in decision-making projects, where an over-reliance on rigid data can lead to a closed mindset. When we allow only the data to speak, we often ignore the valuable insights that human experiences can bring.

“Computer says no” is a phrase used to describe situations where data-driven systems block alternative approaches. This mindset prioritises fixed singular answers based on numerical outcomes, often at the expense of human input and flexibility. This way of thinking can make it difficult to explore open-ended questions, leading people to focus only on meeting predetermined targets instead of thinking about broader goals.

For instance, if a company aims to cut operational costs, a data-driven approach might lead to layoffs or reductions in resources, as these solutions show immediate, measurable results. However, this limited focus can overlook alternative solutions, like investing in training to improve efficiency, which could have a more sustainable impact in the long run.

In his discussion on high-speed rail projects, Rory Sutherland contrasts data-driven solutions with human-centred approaches by suggesting that Disney, rather than engineers, should handle the project brief. 

While traditional engineers would focus on making the journey faster by shaving off minutes, Disney would seek to make the experience enjoyable and memorable for passengers. Sutherland argues that time and speed, though important, don’t directly align with human preferences. Instead, it’s often about the quality of the experience. He continues to point out that Disney-like approaches are rarely considered because they involve open-ended questions that invite multiple, subjective solutions. One direct quote from the discussion was.

“business, government and politics aren’t looking to solving problems, they are looking to win arguments”

By treating decisions like high school maths problems, businesses and governments tend to seek single, data-backed answers that avoid debate and deflect responsibility. This mindset, “computer says no” thinking, prioritises data as an excuse to avoid making real decisions, ultimately limiting creative problem-solving and overlooking human needs.

When data is treated as the ultimate “truth,” decision-makers can rely on it to support passive, risk-free choices. By following data alone, they avoid creative problem-solving and human-centred design, allowing them to justify their decisions with numbers instead of considering the human experience. This is particularly harmful in projects intended to enhance quality of life—such as transportation, urban planning, or healthcare where the experience and preferences of people are essential.

Sutherland’s perspective shows us that data-driven thinking isn’t enough on its own. Sometimes, the best solutions require a more imaginative and open-ended approach, like Disney’s, that looks beyond just speed or efficiency. The real value lies in balancing data with human insight to make decisions that not only achieve measurable goals but also provide a meaningful impact on people’s lives.

Data is often seen as objective, but data interpretation can easily become biassed. One common example is survivorship bias, a logical error that occurs when we focus only on successful outcomes, ignoring the ones that didn’t. This can lead to skewed conclusions and incomplete solutions. During World War II, engineers studied damaged aircraft that returned from missions. Statistician Abraham Wald pointed out that they were only seeing planes that survived; the planes that were hit in critical areas hadn’t returned. By considering only the visible data on the planes that came back, Engineers risked overlooking the planes that were lost due to hits in areas they hadn’t examined.

Similarly, organisations can interpret quantitative data in ways that favour certain outcomes or ignore critical information. When decision-makers focus only on successful projects or examples that meet targets, they miss the full picture. This is confirmation bias, where people unconsciously search for and interpret data that confirms their pre-existing beliefs. This can lead to cherry-picking data that supports desired conclusions while disregarding data that doesn’t fit the narrative. When combined with “computer says no” thinking, these biases reinforce closed mindsets. 

Decisions become less about finding the best solution and more about justifying predetermined goals, limiting opportunities for innovation.

An open mindset in decision-making allows for human-centred design. Instead of only answering “What do we need to reduce?”, an open mindset encourages questions like “What do people need to do their best work?”. By allowing room for creative problem-solving, organisations can focus on innovative solutions rather than just achieving numbers. This means creating space for exploration and failure—something closed, data-only thinking often shuts down.

In many cases, pilots and prototypes are essential for discovering new approaches. They give teams a safe space to test ideas, fail, and learn. When failure is allowed, teams can take measured risks to explore options that may not have immediate numerical backing. This, in turn, can lead to breakthrough innovations.

While data is essential for decision-making, it should serve as a guideline rather than a dictator. An open, human-centred approach doesn’t ignore data; instead, it uses data to inform deeper questions and refine creative solutions. For instance, instead of asking, “How can we cut costs?”, we might ask, “What do our customers value most, and how can we provide that effectively?”

In this way, data becomes a supporting tool rather than a restriction. It provides insights that can inspire new ideas and make creative solutions more impactful. By balancing data with human intuition and design thinking, organisations can make decisions that drive innovation rather than limit it.

To truly innovate, organisations need to embrace pilots and trials as opportunities to test new approaches. These low-stakes trials give teams the freedom to experiment and learn. A pilot might not produce immediate data-driven success, but it can provide valuable insights for future projects. This process of testing, failing, and adjusting is at the heart of innovation.

For example, a company may test a new service model on a small scale before fully committing. If the pilot doesn’t meet the initial goals, it can be tweaked and improved. Even if a pilot fails, it provides information on what doesn’t work, which is just as valuable as knowing what does.

Then some pilots strike diamonds and could reinvent how you do business.

While data-driven decision-making has many benefits, it should not be the sole driver of choices. An open mindset allows people to ask better questions, explore innovative solutions, and find human-centred approaches that data alone cannot provide. By allowing space for pilots and embracing constructive failure, organisations can move beyond “computer says no” thinking and make room for meaningful innovation.

Ultimately, the key to growth lies in balancing data with creativity. When organisations allow people to define questions and take calculated risks, they empower teams to go beyond numbers and build solutions that truly resonate with human needs.

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