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The Multiplier Effect: AI, Effective Communication, & Remote Work
Part 3 of 14 in our series on generative AI and organizational dynamics
Learning theory, with its rich insights into how humans acquire, process, and retain information, offers a powerful framework for enhancing effective communication. By understanding the mechanisms behind learning, communicators can tailor their strategies to better align with the cognitive and behavioral patterns of their audience, ensuring messages are not only delivered but also understood and remembered. Whether through the modeling of positive behaviors, the strategic organization of information, or the facilitation of engaging, experiential interactions, learning theories provide a roadmap for crafting communication that resonates deeply and fosters meaningful connections. This approach not only enhances the clarity and impact of the message but also empowers the audience to actively engage with and reflect on the content, leading to a more profound and lasting understanding (Bandura, 1977; Bruner, 1960; Kolb, 1984; Mezirow, 1991; Piaget, 1952; Skinner, 1953; Vygotsky, 1978).
The challenge, of course, is incorporating these strategies (or even pieces of them) into myriad communications in which we, as humans, engage at nearly every turn in life. Further, some people may respond best to visual information, while others might find auditory or text-based communication more effective (Fleming, N., & Baume, D., 2006). Person-to-person, or person-to-small group communications perhaps offer the best opportunity to apply such techniques. Direct interaction provides immediate feedback, allowing communicators to adjust their message, tone, and delivery method in real-time to suit the listener's preferences and responses.
The dynamics change quite substantially, however, in larger group communications. Attempting to apply multimodal communication strategies in larger settings is often impractical at scale. One person cannot feasibly adjust messages to suit the unique preferences of thousands of employees or listeners, for instance. Other challenges include, integrating feedback, getting the balance of information right so it’s not overwhelming for larger audiences, and ensuring that communications are ethical, inclusive, and take into consideration the cultural and social diversity of the audience. This leads us to explore ways that artificial intelligence and other advances in information and communications technology (ICF) are helping to mitigate such challenges, while also creating new, previously unimagined means of communicating.
The Barsch Learning Style Inventory, developed by Jeffrey Barsch in the late 20th century, is a tool designed to help individuals identify their preferred learning styles. The inventory categorizes learners into three primary types based on the sensory modalities that most effectively support their learning processes: auditory, visual, and kinesthetic. Understanding these learning styles can help educators tailor their instructional methods to better suit the needs of their students and can also enable learners to adopt study strategies that align with their natural preferences. Here's an overview of each learning style according to the Barsch Learning Style Inventory:
Auditory Learners
Visual Learners
Kinesthetic Learners
Application and Limitations
The Barsch Learning Style Inventory has been used widely in educational settings to enhance teaching and learning strategies. However, it's important to note that recent research has critiqued the notion of rigid learning styles. Critics argue that categorizing learners too strictly into one style can oversimplify the complexity of human learning and potentially limit learners' flexibility and adaptability. Modern educational research suggests that while individuals may exhibit preferences for certain types of information processing, effective learning often involves integrating multiple modalities and strategies depending on the context, content, and learning objectives.
Despite these critiques, the Barsch Learning Style Inventory remains a useful tool for raising awareness about different approaches to learning and encouraging educators and learners alike to consider a variety of strategies to enhance the learning process (Barsch, 1996; Howard-Jones, 2014; Pashler, McDaniel, Rohrer, & Bjork, 2008; Riener & Willingham, 2010; Willingham, Hughes, & Dobolyi, 2015; conversation with OpenAI’s ChatGPT Feb, 26, 2024).
A free version of the Barsch Learning Styles Inventory is available at: http://faculty.valenciacollege.edu/koverhiser/Learningstyles.htm
Improving Communication At Scale with AI
Artificial Intelligence offers promising solutions to the general challenges referenced above, enabling scalable, personalized, and effective communication strategies within large organizations. By leveraging AI technologies, companies can analyze large datasets to identify patterns and preferences in employee engagement, tailoring communication methods accordingly. AI-driven platforms can automate the customization of messages, ensuring that they match individual preferences for visual, auditory, or kinesthetic information, thereby enhancing accessibility and engagement across diverse audiences.
Furthermore, AI can streamline the integration of feedback by quickly processing responses from large numbers of employees, identifying key themes and areas for improvement. This allows for a dynamic adjustment of communication strategies, ensuring that the content is neither too sparse nor overwhelming. Additionally, AI tools equipped with natural language processing and sentiment analysis can help ensure that communications are culturally sensitive and inclusive, automatically flagging potential biases or culturally insensitive language before messages are disseminated. In this way, AI not only addresses the practical limitations of human capabilities in large-scale communication efforts but also promotes a more inclusive, effective, and ethically conscious approach to organizational communication.
AI Enhanced Augmented Reality and Virtual Reality
Two important tools that are, at once, supporting and profoundly changing the nature of remote work communications are augmented reality (AR) and virtual reality (VR). AR overlays digital information onto the real world, enhancing one’s perception of their surroundings with interactive, computer-generated elements visible through devices like smartphones or AR glasses. VR immerses users in a fully digital environment, creating a simulated experience that can be similar to or completely different from the real world, accessible through VR headsets.
Advances in AI have the potential to significantly enhance AR and VR technologies, especially in the context of remote communication within large global teams. These enhancements can lead to more immersive, efficient, and interactive communication experiences. Here’s a brief look at how AI could amplify AR and VR for remote team collaboration:
Real-time Language Translation and Subtitling: AI-driven natural language processing (NLP) can provide real-time translation and subtitling in AR and VR environments, breaking down language barriers among global team members. This allows for seamless communication and collaboration, regardless of participants' native languages.
Personalized Avatars and Digital Twins: AI can generate realistic and personalized avatars or digital twins of team members in virtual spaces, enhancing the sense of presence and engagement. These avatars could adapt in real-time to mirror the user’s expressions and body language, making interactions more natural.
Contextual and Spatial Awareness: By integrating AI with AR and VR, these technologies can become contextually aware, adapting the information displayed based on the user's environment, task at hand, or focus within the virtual space. This could optimize workflows and enhance decision-making in collaborative projects.
Interactive and Adaptive Learning Environments: For training and development purposes, AI can create dynamic, interactive learning environments within AR and VR that adapt to the user's learning pace, style, and immediate responses. This personalized approach can improve skill acquisition and retention for remote teams.
Enhanced Collaboration Tools: AI can augment AR and VR collaboration tools with features like automatic meeting summaries, action item tracking, and intelligent assistance for brainstorming and problem-solving, making virtual meetings more productive.
Emotion and Sentiment Analysis: Integrating emotion recognition AI with AR and VR could allow the technology to respond to users' emotional cues, adjusting the communication style or providing feedback to team leaders about the group's mood and engagement levels.
Optimized Network Performance: AI algorithms can optimize data transmission and processing for AR and VR applications, reducing latency and improving the smoothness and reliability of remote interactions, even when bandwidth is limited.
Accessibility Features: AR and VR, enhanced by AI, can offer advanced accessibility features, such as real-time sign language interpretation or navigation aids for visually impaired users, making remote communication more inclusive.
Implementation Challenges
While the potential of AI-enhanced AR and VR for remote communication is vast, several challenges remain, including ensuring privacy and data security, managing the computational demands, and providing equitable access to these advanced technologies across different regions and socioeconomic statuses.
That stated, the integration of AI with AR and VR technologies promises to revolutionize remote communication in global teams, offering more immersive, interactive, and inclusive ways to collaborate. As these technologies continue to evolve, they will likely become integral tools for bridging the gap between remote team members, enhancing both productivity and connection.
Re-imagining Remote Work Communications
Taking all of the above considerations into account, what might this portend for future remote working modalities? Perhaps the best way to imagine this, given what learning theory suggests about the power of story telling as an effective tool for human learning and understanding, is by sharing a vignette depicting a potential day in the life of a typical global work team in the not-to-distant future.
To start, let’s imagine a fictitious global urban planning company called UrbanSync Global Innovations (USGI). Let’s further imagine that USGI has recently won a contract, funded by a large international investment firm, to design highly efficient, environmentally friendly “smart cities” around the globe. An overarching goal of the project is to enhance public services, and foster sustainable urban development. The team is composed of a diverse group of global urban planners, software engineers, data scientists, environmental consultants, and public policy experts, collaborating from different parts of the world.
As the early morning light filters through her New York City apartment, Emily, a data scientist deeply involved in the groundbreaking Smart City Integration Platform (SCIP) project, begins her day like any other. With a steaming cup of coffee in hand, she settles into her workspace, an area marked by a blend of comfort and cutting-edge technology. She dons her AR goggles, and instantly, her quiet living room transforms into a dynamic command center for global collaboration.
In her upper left view, a neatly organized menu of asynchronous communications appears, glowing softly against the morning light. These messages, sent while she was asleep, contain updates from Hans, an urban planner from Munich, Germany, and Mikka, an international public policy expert from Osaka, Japan. Their messages, originally in German and Japanese, respectively, are seamlessly translated into English for Emily, complete with cultural cues and considerations to bridge any gaps in understanding.
The messages are also available in a variety of formats including, 3D casted images of Hans and Mikka delivering their messages (again, translated into English), AI-written formats of the messages, 3D images of the latest Smart City designs that can be viewed and edited in real-time, and short video clips of how the design would look in a real-life rendering.
Curious and eager to dive into the details, Emily opts to explore all available communication modalities. She selects the 3D updates first, and the room around her comes alive with holographic projections of Hans and Mikka. They stand in their mutual "work space," a virtual environment designed to foster collaboration across continents. As they discuss their overnight progress, their holograms are vivid, making Emily feel as though her colleagues are right there with her, despite the miles separating them.
Next, Emily explores the AI-generated written summaries of Hans and Mikka's updates. The AI's ability to condense their detailed reports into concise, comprehensible text impresses her, providing a quick yet thorough understanding of their progress and insights.
But Emily's exploration doesn't stop there. She's particularly interested in the latest design updates, so she navigates to the 3D image of the enhanced urban layout. The model, sophisticated and detailed, offers her a bird's eye view of their collective vision coming to life, block by block, in a simulated environment that's as close to reality as possible.
Craving a deeper dive, she selects the option to view a short video clip. This AI-generated simulation showcases the design's potential impact on real-world urban living, complete with bustling streets, green spaces, and efficient transport systems. It's a glimpse into the future, one that Emily and her team are working tirelessly to realize.
After absorbing the wealth of information, Emily formulates questions and feedback for Hans and Mikka. She records her thoughts and uploads them to the Open Space platform, choosing an asynchronous format to respect their off-work hours, a reminder of the platform's sensitivity to cultural norms and time zone differences.
With Hans and Mikka's updates fully reviewed, Emily turns her attention to Javier, an environmental engineer based in Santiago, Chile. She shares some environmental concerns about the current design and wishes to discuss them directly. The Open Space platform, recognizing the minor time difference between New York and Santiago, suggests an optimal time for a synchronous conversation later in the day. Emily books the meeting with a sense of anticipation, eager to bring Javier's insights into the fold.
This "day in the life" scenario not only showcases the potential of AI-enhanced communication in global projects but also highlights the importance of flexibility, cultural sensitivity, and the seamless integration of various communication styles to foster collaboration across borders. As Emily navigates through her day, she exemplifies how future technologies could redefine the boundaries of teamwork, innovation, and global project management.
NOTE: The above vignette was produced by ChatGPT based on a very detailed 500-word prompt outlining the characters, the technology components, the time zone, language and cultural differences, as well as key elements of the fictitious company, the project, etc. Quite clearly, a skilled human writer could produce a vastly more compelling depiction of a day in the life of a future remote working experience. However, the author elected to leave the story intact, as created by the platform (with only minor edits) to further illustrate ChatGPT’s current capabilities (and deficits) with respect to novel content generation.
The above scenario, while fictitious and purely speculative, nevertheless demonstrates how AI-enhanced communication platforms could potentially overcome barriers of time, distance, language, and culture, enabling a global team to collaboratively innovate and implement cutting-edge solutions for complex remote work communications. Only time will reveal how close to the mark this scenario actually comes, but even with current state technology it’s not implausible to imagine such a world in the very near future - large components of this vision are firmly in place already.
How might Future Point Digital help your organization reimagine the art of the possible with respect to new ways of working, doing, thinking, and communicating via emerging technology? Follow us at: www.futurepointdigital.com
About the Author: David Ragland is a former senior technology executive and an adjunct professor of management. He serves as a partner at FuturePoint Digital, a research-based technology consultancy specializing in strategy, advisory, and educational services for global clients. David earned his Doctorate in Business Administration from IE University in Madrid, Spain, and a Master of Science in Information and Telecommunications Systems from Johns Hopkins University. He also holds an undergraduate degree in Psychology from James Madison University and completed a certificate in Artificial Intelligence and Business Strategy at MIT. His research focuses on the intersection of emerging technology with organizational and societal dynamics.
References
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Bruner, J. S. (1960). The process of education. Harvard University Press.
Fleming, N., & Baume, D. (2006). Learning styles again: VARKing up the right tree! Educational Developments, SEDA Ltd.
Howard-Jones, P. A. (2014). Neuroscience and education: Myths and messages. Nature Reviews Neuroscience, 15(12), 817-824.
Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice-Hall.
Mezirow, J. (1991). Transformative dimensions of adult learning. Jossey-Bass.
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105-119.
Piaget, J. (1952). The origins of intelligence in children. International Universities Press.
Riener, C., & Willingham, D. (2010). The myth of learning styles. Change: The Magazine of Higher Learning, 42(5), 32-35.
Skinner, B. F. (1953). Science and human behavior. Macmillan.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Willingham, D. T., Hughes, E. M., & Dobolyi, D. G. (2015). The scientific status of learning styles theories. Teaching of Psychology, 42(3), 266-271.