Shaping Organizational Communication: The Impact of AI and Language through Weick’s Sensemaking Theory

Sensemaking Processes and Phenomenological Analysis in Organizations

American social psychologist Karl Weick, together with his theory of meaning in organizations, has contributed to the acceptance of the concept of social structure in organization theory. According to the theory of meaning, organizations exist as lived experiences in the minds of their members. K. Weick (1979) argues that sense-making is not about discovering organizational reality but about organizing experiences to make organizational life meaningful. In his book Making Meaning in Organizations, K. E. Weick (1995) defines sense-making as a process based on identity formation, retrospection, evolution, social interaction, continuously acquired cues, and a reliance on reliability rather than accuracy.

In essence, sense-making refers to a person’s ability to make sense of the fundamental elements of their experiences during interactions with their environment. The study of basic sense-making falls into the fields of in situ cognition (Suchman, 1987) and embodied cognition (Pfeifer & Bongard, 2006), which examine cognition through an individual’s interactional experience. Assessing a person’s current sense-making abilities is different from the challenge of designing an agent with sense-making abilities. To address this problem, it is necessary to recognize that sense-making is a property that emerges as a result of an agent’s interaction with its environment (Georgeon & Marshall, 2013).

Sense-making is the process by which individuals interpret new, uncertain, confusing events or events that violate expectations, and it is the essence of organizing activity (Maitlis & Christianson, 2014). Czarniawska-Joerges (1992) argues that sense-making in organizational life is unique because “the work itself is much more neglected than in organizational life.” When faced with uncertainty, organizational members attempt to understand what is happening in their environment by gathering and interpreting a wide range of cues. Sense-making at the organizational level is a social process that enables organizational members to interpret their environment, construct perceptual frameworks, and take collective action through their interactions with one another (Maitlis, 2005).

According to the theory of meaning, organizational members interpret, interpret, and give meaning to their environment through communication with others. By consensus, organizational members construct explanations that enable them to understand the world they live in and their actions. Sense-making involves generating open questions and providing clear answers to those questions. According to sense-making theory, organizational reality is a continuum of mutual and collective efforts by organizational members to understand “what happened” after the fact (K. E. Weick, 1993).

Cognition is an organismic activity that evolves into complex and dynamic processes at different levels and on different time scales. These processes are part of the dynamic participation or response of the whole organism in living and interacting with structured environments through material interaction. The way environments shape minds and minds shape environments involves metaplasticity that goes both ways. Brain activity not only enables changes in the brain in response to changes in environmental, social, and cultural environments, but also triggers these changes (Malafouris, 2013). This self-organizing brain-body-environment system can be intervened at any point and change the entire system (Ceci & Roazzi, 1994).

Cognitive frameworks are the result of past socialization experiences. Signs are environmental information encountered in present experience. Signs initiate the process of sense-making. Cognitive frameworks are elements of knowledge that contain rules and values   and serve as a reference and guide for understanding meaning. When people associate structures and signs, they create meaning. Meaning is derived from the categories of past experiences, the signs and labels of current events, and the connections between structure and sign. Not only does the cognitive framework or sign bring meaning (Miles, 2012), and therefore the activity of making meaning is a phenomenological process. Phenomenology is based on a perspective that emphasizes how social phenomena occur and how they are perceived as independent research subjects.

Phenomenology first emerged with Husserl’s claim to end metaphysics, return to experience from a primary perspective, and thus make a new beginning to stereotypical scientific approaches. Phenomenology focuses not on how phenomena exist independently, but on how people who encounter and experience these phenomena perceive them. Social phenomena are shaped by people’s consciousness, not as they are in the outside world. For this reason, it tries to examine social phenomena not only with their external appearances, but also with how they are experienced in life (Keskin et al., 2016). Beyond being just a philosophical approach, phenomenology differs from other approaches with the method it uses. The phenomenological method is based on the principle of correlation. Social facts are portrayed by social actors, objects or events in the external world by establishing a connection with them. This picture determines how social facts are perceived by actors. Social actors base their behavior on their own goals and meanings (Holt & Cornelissen, 2014).

How social phenomena are interpreted is related to the history of the phenomenon in consciousness, how it is positioned and what it is associated with. Social phenomena are in a semantic relationship that social actors and individual consciousness are in, beyond the relationships that material objects in the physical world are connected to (Holt & Cornelisse, 2014). Phenomenology tries to discover the deep meanings of realities in organizational life by examining the experiences of organizational members, without being affected by external ideas and theories. This in-depth analysis allows us to see how organizational actors make sense of their organizations. Although sense-making begins at the individual level, these understandings spread to a social level as actors share their understandings in the organization (McAuley et al., 2007). The sense-making of relationships, objects, social phenomena, or each other among actors in the organization will be incomplete and largely meaningless without a phenomenological perspective, that is, without perceptions, senses, environmental and cultural context.

Phenomenology and the Place of the Body in Linguistic Communication

Organizational communication is not only the transfer of information but also a fundamental element of organization (Ashcraft et al., 2009; Cooren & Fairhurst, 2004). This approach argues that organizations are shaped through communication. Language and communication determine the organizational structure and meanings are created in this process (Putnam & Maydan Nicotera, 2010). Communication occurs through the transfer of messages between the source and the receiver; ideally, the meaning interpreted by the receiver coincides with the source’s intention (S. McShane & Von Glinow, 2009). In this process, the material and symbolic elements of communication are considered in a dynamic integrity (Crossley, 1997).

Communication is a dynamic and flexible process shaped by individuals’ experiences, symbols and cultural elements (Keskin et al., 2013). Language is not only a reflection of perception, but also a fundamental element that transforms, structures and integrates it (Pietersma, 1989). In this context, the determining role of language and communication on organizational and individual identity is of critical importance in terms of both cognitive processes and social interactions.

Dilthey and Gadamer emphasize that social reality is interpreted through cultural values, social interaction and language and expressed through subjective symbols (Kouzmin & Dixon, 2006). The symbolic relationship between meaning and artificial representations is shaped by the meaning assigned to them by representations such as written and spoken language, sign language, tone of voice, culture, and narratives. This approach helps to understand the multidimensional and dynamic interaction of materiality and symbolism. In line with the “corporeal turn” in social and organizational sciences, advanced phenomenology offers opportunities to develop a bodily understanding of organizational and communication practice (Küpers et al., 2012).

This approach aims to explore the connections and intentions between participants (subjects) and phenomena (objects) while investigating how participants describe a particular phenomenon. Husserl sought to understand human beliefs and knowledge by considering how objects are “presented” to human consciousness (Brummans et al., 2024). The concept of intentionality is at the core of Husserl’s philosophy. Therefore, the study of intentionality in phenomenology focuses on how people experience and relate to phenomena, emphasizing subjective experience rather than conscious decision-making (Vagle, 2014). With this human-centered approach, phenomenology attempts to understand the construction of knowledge among individuals.

Husserl (1939) defines the phenomenology of language as placing existing languages within a universal and unchanging framework of consciousness of all possible languages. Thinking of language as a reality with fixed rules and absolutely unchangeable ignores the living productive nature of language. From a phenomenological perspective, for a person who uses his language as a means of communication with a society, language regains its coherence and integrity. This is the result of a system in which the emphasis is not on the complex histories of independent linguistic phenomena, but on things that are present or future-oriented and governed by current logic (Bernet et al., 2023; Halák, 2022).

The act of expression is not limited to using the expressive power of language alone, but is also recreated by humanity’s ability to move from signs to meaning. Expression is therefore more than just realizing a grammatical sequence, but rather about the formation of relations between signs according to an order that transcends language. In Merleau-Ponty’s words, what we want to communicate is “more of our experience than what has been said before” (Merleau-Ponty et al., 2013). Merleau-Ponty’s phenomenology allows for a radical interpretation of corporeality as a constitutive element of communication and organization. Since communication occurs at a co-oriented boundary between the material embodied world and the subjective, socio-symbolic world of meaning-making, phenomenology offers a means of navigating this landscape (Küpers et al., 2012). For Husserl and Merleau-Ponty, language is not more fundamental than perceptual experiences; rather, it is the result of an interaction with a pre-linguistic and non-propositional world. From this perspective, they reject the idea that meaning can only be expressed through propositions. Separating perception and meaning severs the link between the perception of an object and its expression, rendering language meaningless. Language is a product of our direct perceptual interaction with the world, and this interaction determines the meaning and use of language (Husserl, 1997; Merleau-Ponty & Smith, 1962).

This perspective focuses on the relationship between organizations and communication by constructing a meaningful world in which communicators co-create diverse and sometimes ambiguous meanings (Sandberg & Dall’Alba, 2009). Advances in areas such as AI, big data analysis, natural language processing, and learning algorithms allow us to explore how organizations can transform communication and meaning-making processes. The concept of interference refers to any psychological, social, or structural barrier that distorts, distorts, distorts, or renders incomprehensible the source’s message. If any part of the communication process is disrupted or corrupted, it will not be possible for the source and the receiver to have a common understanding of the content of the message (S. McShane & Von Glinow, 2009). Phenomenologically, all those involved in communication are, first and foremost, embodied beings or agents, or are mediated by the lived process of embodiment, perception, and expression (Lanigan, 2007). Therefore, communication is rooted in and processed through lived and meaningful bodies that interact with their world (Zlatev, 2007).

Merleau-Ponty’s approach to perception emphasizes that perception is inherently communicative; it is a subjective form of expression and partnership (Baldwin, 2007). Perception primarily means establishing a non-objective partnership with the world (Crossley, 1997). This process triggers the perceiver to respond and enter into a dialog with the perceived figures. Being perceived by others does not only mean reflecting an “object” but also understanding the meanings of perceived actions and responding to them. Therefore, perception, interaction and communication are intertwined and intertwined (D. Low, 2009). In today’s complex, uncertain and ever-changing business world, corporate communication is becoming increasingly important. The increasing competitive challenges brought about by the global economy, the pace of technological innovations in products and processes, the challenges created by the abundance of information, and the pressures to develop more responsible business practices require organizations and their employees to better understand the value of communication (May & Mumby, 2004). Under these conditions, communication; It plays a critical role in increasing information sharing, improving employee and customer satisfaction, and supporting sustainable business practices. As organizations become increasingly fragmented due to the impact of new technologies and require more flexible coordination methods, communication is recognized as an effective and flexible tool in these new business practices (Corman & Poole, 2000).

The integration of AI into organizations not only accelerates communication processes, but also affects meaning-making processes. According to the phenomenological perspective, meaning is not only shaped by individuals’ perceptions and social interactions; it can also be reshaped by the data and analytical power provided by AI. AI tools can change how meanings are shaped in social contexts by increasing the speed of linguistic interactions. This raises new questions about how individuals and groups create shared meanings. How do AI’s rapid data flows and recommendation systems reshape meaningful relationships between individuals? This plays a critical role not only in the transfer of information, but also in creating new meanings in cultural and social contexts. Understanding the impact of AI on communication requires a more in-depth phenomenological examination of individuals’ perceptions and collective meaning-making processes.

Intersection of Sensemaking With Artificial Intelligence and Phenomenology

Artificial intelligence encompasses efforts to create intelligence in machines and the development of technologies that perform tasks specific to human intelligence (Broussard, 2018; Frankish & Ramsey, 2014). Recent developments have accelerated the integration of artificial intelligence into daily life (Campolo et al., 2017), while interactions with smart devices and the Internet of Things (IoT) are expected to further this process (Rainie & Anderson, 2017). Artificial intelligence not only enhances the interaction capabilities of devices, but also expands the boundaries of human communication (Peter & Kühne, 2018). Human-computer interaction (HCI) studies show that artificial intelligence systems that communicate directly with humans are perceived as independent social actors (Nass et al., 1994). Technologies designed to be particularly sensitive to social cues can establish deeper interactions with people. Although traditionally strong ties in the workplace are thought to be formed through face-to-face interactions, AI-based tools are reshaping access to information, support, and participation. AI systems like ChatGPT can complement leadership skills and transform workplace dynamics by supporting meaning-making processes (Littlejohn & Foss, 2009).

People use their knowledge of human interactions to guide meaning-making processes in their interactions with media (B. Reeves & Nass, 1998). Although it is known that a machine is programed by a human, research shows that people direct their messages directly to the device, not to the programmer (Sundar & Nass, 2000). Although people perceive robots as social communication partners, they do not see them as people and reflect their own thoughts in their interactions with digital assistants (Edwards et al., 2016; Guzman, 2019). Applications of AI in the field of communication include conversational agents, social robots, and automatic writing software, and these technologies have become possible thanks to developments in the fields of Natural Language Processing (NLP) and Natural Language Generation (NLG; Allen, 2003; Dörr, 2016). AI is taking an active role in communication by not only facilitating communication but also automating social processes (Gehl & Bakardjieva, 2017; J. Reeves, 2016). AI-powered technologies, which have historically taken on the role of media as a tool for exchanging messages, are designed to process messages dynamically, unlike previous talking devices (Gunkel, 2012; Peter & Kühne, 2018).

As organizations interact with their employees and customers through voice or text-based AI-powered tools, new challenges arise in managing human-AI interactions. In particular, striking a balance between the mechanical and human elements is critical. Organizations are designing communication technologies that incorporate human-specific elements such as personality traits and speech patterns to make the customer experience both efficient and satisfying. Information technology research has examined how digital systems (e.g., virtual environments, online shopping platforms, and decision support mechanisms) are perceived by humans (Lankton et al., 2015; Riedl et al., 2014), but conversational AI systems differ from previous technologies because they offer real-time and personalized interactions (Diederich et al., 2020; Pfeuffer et al., 2019). Research shows that elements that increase the sense of social presence in chatbots (e.g., dialog delays that mimic natural speech rhythms or use of humor) can increase conversion rates (Schanke et al., 2021). However, these elements do not always produce positive results; an overly personalized or human-like experience can, in some cases, negatively affect users’ expectations and create dissatisfaction (Hill et al., 2015).

Zheng and Jarvenpaa (2021) argue that users project their own beliefs, expectations, and emotions onto digital systems when interacting with them, which can lead to misunderstandings. When interacting with an AI-powered tool, users can sometimes see it as a human and sometimes as a mechanical system (Epley et al., 2007). Brahnam (2009) emphasizes that users sometimes unconsciously evaluate AI tools as human, while sometimes questioning the limitations of these tools. The variability between these two perceptions can lead to negative consequences, especially when the AI tool is emphasized as having human-like characteristics. Therefore, organizations should carefully balance the context of these systems and the level of human elements when evaluating how chatbots are perceived (Schuetzler et al., 2020).

AI can mimic human-like perception and meaning-making processes by creating digital representations of phenomenological experiences, and even add new dimensions. The technological reshaping of the way people perceive the world makes it possible to reproduce the human-centered understanding offered by phenomenology in a digital environment. The fact that AI’s decision-making and learning algorithms analyze and respond to the meaningful relationships that individuals establish with their environment further expands the phenomenological perspective. In this context, the intersection of AI and phenomenology examines how individuals’ internal experiences are reflected in the external world and how they can be reproduced in a digital environment (Dastin, 2022; Graham et al., 2020). AI applications that model perceptual processes have the potential to embody phenomenological concepts as a digital reflection (Dastin, 2022).

In response to the work of Ricoeur and Gadamer and to post-phenomenology’s views on how technologies mediate human-world relations, a hermeneutic approach to digital technologies has been proposed (Ihde, 1998; Rosenberger & Verbeek, 2015). Ricoeur argues that experience is shaped through language and narrative, emphasizing that digital technologies change the world and construct our narratives (Reijers & Coeckelbergh, 2020; Romele, 2020). Technology is intertwined with human life and affects meaning-making processes. Artificial intelligence can allow us to recognize our biases and integrate them into our lives (Coeckelbergh, 2023).

While the phenomenological framework emphasizes subjective experiences and perceptions, AI works with data-driven algorithms (Husserl, 1931; Russell & Norvig, 2016). While phenomenology foregrounds the subjectivity of individual experiences and the role of sensory input in creating meaning, AI is based on rational calculations and excludes the subjective aspects of sensory experiences (Dreyfus, 1992). This leads to a divergence between AI and phenomenological approaches (Merleau-Ponty & Soysal, 2012; Russell & Norvig, 2016). While Descartes’ Cartesian dualism separates the mind and body, AI performs mental operations with algorithms, which contradicts Husserl’s approach (Husserl, 1913). However, Merleau-Ponty’s perspective on the body-consciousness relationship may offer a new perspective on AI systems. The body emphasizes the interaction with the experiential world (Merleau-Ponty, 1945). Artificial intelligence systems that take into account the body-consciousness relationship can establish more natural interactions with people and become compatible with human experience (Gallagher, 2017).

According to Merleau-Ponty, perception and language are intertwined and interrelated elements that affect each other, like matter and mind; the meaning of language is a product of how we perceive the world. Speech requires the interaction of bodily, perceptual, and cognitive abilities (Macke, 2007). Increasing complexity can increase uncertainty because more elements affect each other (Huber & Daft, 1987). This has a significant impact on the adoption of artificial intelligence in organizations. Increasing complexity and dependency in AI systems bring uncertainty and meaning-making challenges. Perrow’s research on complex interactive systems provides an important example in this context. It is important to consider the dynamics of complexity and manage uncertainties in the development of AI systems.