The Role of Assumptions in Design Processes

Assumptions are useful, non-neutral and abundant

Students and practitioners of design processes repeatedly come across assumption-making. As Oleson et al. (2023) point out, designers often use assumptions in making design decisions because they can never have perfect information about users or contexts. This article is a short introduction to assumptions for both reader groups, addressing the potential harm of making unchecked assumptions towards design process. Rather than advising students and practitioners to avoid assumptions, we argue that, in our experience, students and practitioners are rarely taught how to work with assumptions effectively, and that the way they do so may be misguided. In the following, we introduce the concept of assumptions as something inherently human, and as something that is part of organizational behaviour and especially a part of any design process. Although assumptions can help us in making decisions, they can also potentially cause harm. Because of this, recognizing assumptions and their usefulness, is paramount to a design process.

Making assumptions is a strategy for survival

An assumption is something we take to be true without having complete evidence for it (Cambridge Dictionary, 2022). In everyday life, this allows us to act quickly and make decisions despite uncertainty. In design and product development, assumptions influence products and experiences that affect other people, so they are not just personal or neutral. In this kind of context, assumptions shape how problems are defined, whose needs are prioritized, and what kinds of solutions are created. Some assumptions can serve as useful starting points, but others may limit thinking or introduce bias. For this reason, assumptions should not be treated as facts, but as ideas that need to be made visible and that must be tested and challenged.

Making decisions based on pre-existing information is something that happens within all kinds of systems. Gestalt theory is interested in investigating whether something we see in the world is interpreted through something that already exists in our mind or is perceived as “true” (Lehar 2002, p. 14). In a design context, this means considering the ways we humans interpret visual world and how we use applications and websites, for example. Gestalt theory gives some insight into looking at the outside world through investigation of situations related to the use of design objects. For example, this can mean considering how the user might understand how to operate a control to adjust an automobile seat. In this case, the user can rightly assume that the way the controls are shaped and grouped represents accurate relationships between the controls and functions in relation to the seat in question (Norman 2013, p. 39). Here, the assumption is verified instantly: pushing the button either triggers wanted action or not. Being able to act based on such an assumption is one of the human abilities that makes a decision-making process much faster.Kahneman (2011, p. 21) explains that there are two interconnected systems in our brains that make this possible. In layman’s terms, there is a fast system which first categorizes the situation, dominating the initial decision-making process in a sense that it gives a kind of baseline that further reactions follow. In addition to this, there is also a slower system that seeks to find deeper analysis. In everyday life, we might greet a person in the street and only then realise they were not our friend after all. We cannot turn this off when working. We carry this way of being into the workplace and arguably also into the behaviour and decision-making processes of our organisations. It is just that we cannot be in the state of flight or fight all the time, or make decisions based solely on “gut feeling” (Dieing et al., 2023, p. 75). As Dieing is here referring to teaching design, we argue this applies also more broadly to design thinking processes, which we have observed from experiences in teaching design-based research. Partly this behaviour of relying on the fast systems of our brain changes through individual experience, so that it is possible to learn not to rely on our brains’ first take on the situation so much that it leads us to wrong conclusions.

Overall, making assumptions is part of us and something that we cannot escape even in our role as solution designers. Sometimes this can be a good thing and sometimes it can lead into a catastrophe. While fast, progressive, and intuitive thinking is evolutionarily efficient, for the purpose of design and product development intuition must be slowed down, questioned, and externalized. In short, the same facility that helps us survive daily life can undermine responsible product decisions if left unexamined.

Assumptions by individuals get carried to organizations

If we accept that we as humans inherently rely heavily on assumptions in our everyday lives, it can be argued that we carry the same traits into organizations in which we work. Organizational strategies, much like individual persons, may rely on already existing experiences. The situation of competition, the behaviour of customers, and the application of new technologies may all be seen as a logical continuation of something that already exists. This can be referred to as “business as usual strategy” behind which lies an assumption that, based on earlier experiences, situations are not likely to change, and not enough organizational changes are made (Bloodgood & Salisbury, 2001, pp. 57–58). While this might hold for a long time, sometimes situations can change quickly, and leave us incapable of responding to the new situation.

On the other hand, future-oriented decision makers within companies can strive to build resilience through scenario thinking. This helps to plan for responding to alternate future outcomes, in case they should materialize. Overall, it is important to remember that it is not organizations but people that make assumptions. Once embedded in processes, roadmaps and Key Performance Indicators, over time the individual assumptions tend to solidify into organizational “truths.” For example, in the case of Covid-19, different organizations were being warned by researchers beforehand about the possibility of a world-wide pandemic, but there was no clear strategy at hand to mitigate the situation when the disease finally broke out. Because of this, it can be argued that even without solely acting on instincts, strategies can fail if they do not consider the world as a hard-to-predict composition of possibilities. Hence, even though company strategies are not always only instinctive guesses by individuals, in hindsight it may become apparent that some company strategies were based on assumptions that may not be as well-informed than we might like to think.

One way to address the role of assumptions in organizations is through design-oriented approaches to problem solving. If assumptions are an inherent part of human cognition and therefore inevitably present in organizational strategies, the key question becomes how organizations recognize and work with these assumptions. Rather than attempting to eliminate assumptions altogether, there are also approaches that instead aim to acknowledge and test them. Design thinking can be seen as one such approach as it emphasizes empathy with users, iterative experimentation, and continuous reframing of the problem space (Tu et al., 2018). Through these practices, implicit assumptions about customers, technologies, or future conditions can be made visible and challenged. In this sense, design thinking does not remove assumptions from making decisions, but it transforms them into hypotheses that can be explored, validated, or rejected through structured inquiry.

Design thinking and assumptions

Design thinking can be described as a process model for dealing with ill-defined challenges. For example, the Stanford design thinking model separates the problem-solving and design processes into five different blocks, or phases (Tu et al., 2018, p. 3). This type of an approach to a design process accepts that it can (and often should) lead to unexpected directions and outcomes, but at the same time it still aims to verify decisions and form a tested “situation awareness” on which the decisions are based. This might be one of the reasons design thinking processes are often described as hard to grasp and fuzzy, represented by Newman’s squiggle (image 1). Newman’s squiggle (2023) is a visual metaphor for the design thinking process, depicting it as a tangled mess of a wire, looping back and forth, but eventually sorting itself out to a straight line. This means that the design process is almost always iterative and messy, before it finds its path to a solution. This is especially true with students who, without experience, often find design processes very unclear.

Similarly to how humans can be seen as acting based on two seemingly contradicting ways of understanding the world – through an “instinctive” and a “reasoning” layer in their brains – also the design thinking process moves from a limited amount of information, often in great vagueness, towards better-informed situations. What is more, this happens at the same time with trying to create something innovative that is not yet present in the world. Without some level of assumptions, this process cannot even get started. Because of this, when we acknowledge the assumptions that permeate individual behaviours, we find that these tend to get transferred to organizations as well as other social groups. As a result, organizational culture is probably at least in some degree dictated by biases from individuals. Considering in more detail what kinds of biases live and spread in organizations would be a topic for a completely different article. So, for the purpose of our discussion here, it is enough to be aware that such biases exist, and they affect even in the organizational culture.

The role of assumptions in empathetic design and product development

Empathetic design is a key approach in product development that focuses on deeply understanding the stakeholders – especially the end users – by observing and engaging with their real experiences, emotions, and challenges. Rather than relying solely on stated needs or preconceived ideas, empathetic design focuses on seeing the world from the user’s perspective in order to uncover motivations, contextual factors, and unspoken pain points that influence how a product is used (Leonard & Rayport, 2011; Thomas, 2013). By integrating these insights into design decisions, empathetic design aims to create products that feel intuitive, supportive, and meaningful to the users. This means addressing not only the functional requirements but also the emotional and human aspects of user experience.

In educational product development settings, such as multidisciplinary project courses, empathetic design practices are often emphasized as teams develop solutions for real-world challenges that are based on user research. However, our experience from teaching and participating in such projects suggests us that practicing genuine empathy requires significant effort from both students and instructors. The participants in such projects frequently become attached to their own ideas and assumptions due to factors such as time constraints, personal beliefs, fixed mindsets, or a reluctance to critically verify their thinking. As a result, designers may speak about empathy while still protecting their own assumptions. In this sense, true empathetic design does not eliminate assumptions but rather seeks to expose and challenge them. Empathy can fail when designers mistake identification with users for actual understanding.

Assumptions are therefore an inherent and often necessary part of the design process. Designers must frequently rely on preliminary assumptions when identifying stakeholders, defining challenges, envisioning valuable features, or exploring technological possibilities. This is particularly true in the early phases of product development when concrete evidence is limited. In the design thinking processes that are commonly used in product development education at organizations such as the Häme University of Applied Sciences (HAMK), these early assumptions help initiate inquiry and guide the direction of research. For instance, developers may initially assume who their key stakeholders are, what problems are worth solving, or what value a proposed product might provide for users or sponsors. These assumptions function as starting points for investigation, allowing teams to frame research questions and begin exploring potential solutions.

What is more, design doing, where the ideas created in design thinking processes are implemented (Backes, 2022), may even deliberately employ assumptions as tools for exploration. In speculative design, provocative or exaggerated assumptions can be used to stimulate discussion and reflection on possible futures (Dunne & Raby, 2013). Similarly, presumptive design introduces early artifacts based on assumptions that can be tested and discussed with users to generate feedback and insights (Frishberg & Lambdin, 2016). In these cases, assumptions are not treated as facts but as hypotheses that support exploration and learning.

However, every early assumption in product development can also be understood as a gamble: the longer an assumption remains untested, the more costly it may become if it proves incorrect. For this reason, assumptions must eventually be examined and validated through user research in order to distinguish evidence from speculation. Without this critical evaluation, assumptions can limit creativity, misdirect development efforts, or reduce the inclusivity and effectiveness of design outcomes. Recognizing how assumptions emerge, how biases influence them, and how they can be systematically tested, is therefore essential for maintaining the quality and integrity of the design process. The following section explores in greater detail why identifying and challenging assumptions is critical for successful design practice.

Assumptions through bias behaviour

There is a variety of bias behaviours that can be recognized to lead to assumptions during different decision-making scenarios. These are present at different levels of actions that can affect the decision-making process and future interactions between designers, stakeholders and clients.

Cognitive biases – such as confirmation bias, anchoring, and framing – can cause individuals to make assumptions that align with their pre-existing professional and personal beliefs and initial information. However, these types of assumptions – such as “we already know the user” – may often ignore contradictory evidence unless emphasis is placed on deeper understanding or research. According to Lord and Taylor (2009), this tendency can be seen in economic decisions, crisis management, and clinical settings, where biases can lead to persistent misjudgements and risk aversion. Such problems could be avoided by examining the resulting challenge scenarios so that these biases can be recognized as something that is pervasive in product development, and by acknowledging that these biases may shape decisions from idea evaluation to market launch if there is no deeper user understanding. As Liedtka (2014, pp. 931–936) points out, actively mitigating these biases is essential for fostering innovation, improving decision quality, developing solutions for actual needs, and maintaining competitiveness in dynamic markets.

However, there is also the phenomenon of biased assimilation which describes how new evidence is interpreted to fit into existing assumptions, making it difficult to overcome initial expectations and leading to entrenched and neglecting viewpoints (Lord & Taylor, 2009). For instance, the attitude that “the first idea is the best idea” is an example of how, in product development, biased assimilation can entrench initial assumptions, skew decision-making, and limit innovation.

Finally, implicit bias is something that operates automatically, influencing behaviour based on social cues, often without conscious awareness. An example of this would be the assumption that “since the user looks like us, we can make a solution for us.” This tendency can be measured experimentally, and it is linked to real-world biased actions (De Houwer, 2019). As this type of bias operates unconsciously, it can significantly influence product development and design thinking, often resulting in exclusion or inequity. Fergus & Bowers (2023) argue that recognizing and actively addressing implicit biases through structured interventions, while inclusive design practices are essential for creating products that serve diverse populations effectively.

Overall, the influence of cognitive, assimilative, and implicit biases demonstrates how deeply ingrained mental shortcuts shape assumptions, decisions, and product outcomes. When left unexamined, these biases can narrow perspectives, reinforce existing beliefs, and limit innovation across contexts ranging from clinical judgement to product design. However, by intentionally identifying and mitigating these biases through structured decision processes, inclusive design practices, and continuous reflection, individuals and teams can make more accurate, equitable, and forward-thinking choices. Ultimately, cultivating awareness of these psychological influences is essential for improving decision quality, fostering inclusive innovation, and ensuring that products and solutions effectively meet the needs of diverse users. In the following section, we further discuss how this can be seen in practise at educational contexts that aim to teach such design processes.

Experiences on design process and biases

As all of the previously discussed types of biases come up in the context of teaching design processes, such as in HAMK Diili and Product Development Project (PdP), this gives examples of how they can affect design actions in various ways (Siintoharju et al, 2024; Jussila et al., 2023). Our overall experience as educators is that while the concept of biases and the role of assumptions may be clear on the surface, it is hard to teach through educational practices. In projects that teach students the basics of this type of work, assumptions work as starting points when doing initial research, and they are often based on a design brief that may be vague. The desktop research that the students engage in is guided, but not dictated, by teachers so that it should not be done solely based on social media and other non-scientific sources but it should also take into account peer-reviewed articles. The amount of information that the students gather at this initial stage is usually large and can be even overwhelming to students who lack the experience to interpret and use it, potentially halting the design process. This phenomenon has been described as the “Death Valley of the Design Process” (Klitsie et al., 2019), referring to a stage where collected information is abundant but difficult to interpret or apply. At this stage, student groups may struggle to make use of the data, as evaluating its relevance and meaning requires experience. Therefore, the next step in the process is to regain orientation and find ways to work with the information. One useful method for this, which we have found from educational practices, is affinity mapping which can enable thematic analysis and helps transform raw data into actionable insights.

One challenge of the process is that the initial desktop research stage may lead to a false assumption that the group has gathered enough relevant and accurate understanding when it moves to the next phase of product development. This next stage is the “define” phase of design thinking that helps to narrow down and conceptualize the problem. At this stage of the work, assumptions and bias work hand in hand. A student group may think that they must have all the information necessary due to the time they have already used in gathering background information, for example, through interviews and desktop research. However, if they do not possess the skills to evaluate their own understanding, this may lead to a classic Dunning-Krueger situation, where people who have low ability or knowledge in a specific domain tend to overestimate their own competence in it, at the same time as people with high ability in the same domain may underestimate their competence (Kruger & Dunning, 1999). In the case of these student projects, this often shows how the students do not understand that they do not know enough about how the information should be gathered and they do not know what information would be needed to successfully complete the task. This applies to other phases of design thinking process.

However, also the teachers may introduce their biases into the process. During these projects, the teachers encourage the students to work with limited data in order to reach a goal, trying to ensure that the process follows the guidelines closely enough but still remains loose enough to not restrict the students’ work too much. This encouragement may further enforce the feeling that the student group is on the right track with the right amount of the right kind of information. This can partly be avoided in sparring sessions, but it seems plausible that relevant information may often also remain undiscovered and unused at the rapidly advancing design process. Something that teachers should also consider when teaching these matters is that the students may find it difficult to challenge assumptions, and they may feel a pressure to perform. This could mean that they would accept things without questioning them enough when they come from the teacher. From the teachers’ perspective, this seems contradictory as teachers want the students to question the presented things, the found information, their own thinking, and so on. At the same time, the students may interpret doing the right thing as doing something without errors, which is counterproductive and can even completely seize this stage of the design process. This is because design and product development are iterative activities where every iteration makes the project or solution better. Indeed, this kind of a process can be described as a succession of making mistakes and learning from them, continuously. In traditional pedagogy, the opposite may often be true as errors in an everyday classroom context may be seen as an unwanted trait, something that should be avoided. For example, in mathematics the students usually strive for the right answer, but in a design process one needs to make mistakes in order to find something that leads to answers, ideas or concepts that are right enough for the purpose.

In the end, student groups may end up in a situation where they want to avoid errors and try to assure themselves and the teacher that their initial assumptions must be right, that they have enough information that is correct, and that the ideas, prototypes and even the final presentation they give are controlled and objective. At the same time, teachers try to embrace “mistakes” and encourage discussions, finding things through trial and error, creativity, and looking at almost every part of the process critically. Of course, it must also be acknowledged that even professional designers cannot completely avoid biases because having a bias is an inherently human thing, and the students are still very much in the process of learning these things. Still, as we teach these types of design processes, we often encounter fairly middle-of the-road ideas that are not scientifically supported but nevertheless presented to clients and teachers.

Conclusion: Assumptions are the starting line, not the end goal

Design will not suffer if we make assumptions, but will it suffer if we pretend that we do not make them, or if we get too attached to different biases or ideals. We have found in practice that designers and developers tend to constantly rely on assumptions, often without acknowledging them. These unexamined beliefs quietly steer how the problem is framed, how the users are defined, and which solution paths are taken. Sometimes they steer the process more powerfully than research itself. Rather than advising students and practitioners to avoid assumptions, then, we argue the opposite: we should not only recognize the assumptions we make but also expose them and put them to work.

In this sense, assumptions functions like hypotheses, similarly to how scientific method relies on testing hypotheses (Tieteen termipankki, 2025). Because of this, design processes should adopt the same discipline. When assumptions are treated as tentative propositions rather than hidden truths, they become testable, revisable, and ultimately productive. What is more, ignoring assumptions will not eliminate bias but rather only conceals it. Assumptions are neither inherently harmful nor inherently useful – they are tools for navigating through uncertainty. If they are left unexamined, they narrow perspectives, reinforce dominant viewpoints, and prematurely close off exploration. However, when made explicit, they mark the beginning of an inquiry where documenting and categorizing assumptions according to desirability, feasibility, and viability helps developers identify which beliefs carry the greatest risk and demand early validation. In our opinion, this practice not only strengthens methodological rigor but can also create psychological safety for providing space for disagreements, alternative interpretations, and collective sense-making.

Importantly, breaking assumptions is not merely corrective but also generative. Many meaningful innovations emerge precisely when a foundational assumption collapses. When designers and developers replace certainty with curiosity, they can create conditions for surprise, such as unexpected user insights, reframed problems, and expanded solution spaces. Hence the challenge for design students and other development practitioners is not to eliminate assumptions but to treat them as the starting line rather than as the end goal. By making assumptions visible, testable, and disposable, designers and developers can build processes that are more transparent, resilient, and capable of producing outcomes that are not only functional but genuinely inclusive and transformative.

Authors