🧂How AI interacts with users

Hi there! Happy Saturday. As I told you before I have been in a group with Silicon Valley product design leads researching UX design trends for Ai products. Here I am slowly sharing some of the insights we learned along the way. This week’s article is brought to you by Icey Zhao, design lead at Airbnb. 

Overview

We looked at 20 AI companies that helps team to be more productive and efficient. They are these 3 categories: Team meeting note taker, generative work deck, XFN-efficiency tools.

Introduction

The evolution of Artificial Intelligence (AI) has significantly transformed how users interact with technology. From the early days of communication to modern digital collaboration and the burgeoning future of AI-human interaction, the journey is both fascinating and complex.

This report explores the historical context, the current state, and future directions of AI interactions with users, offering an in-depth analysis that provides valuable insights for professionals in the field.

Early Days of Collaboration

The Telegraph and Telephone Revolution

The development of the telegraph in the 19th century marked a pivotal moment in communication, enabling near-instantaneous long-distance messaging. This innovation laid the groundwork for more sophisticated forms of communication and collaboration.

The invention of the telephone further revolutionized real-time voice communication, allowing conversations across vast distances and significantly improving coordination and collaboration. These advancements set the stage for the future integration of AI into communication systems, providing a foundation of instantaneous and reliable interaction.

Pre-AI Digital Collaboration

Technological Advancements and Persistent Challenges

Despite significant technological advancements, challenges in digital collaboration persist. The advent of computers and the internet revolutionized communication, enabling unprecedented levels of connectivity and information sharing. Cloud computing and tools like Google Docs, Slack, and Trello have facilitated seamless real-time collaboration, yet inefficiencies remain. According to Vantage Circle, 75% of cross-functional teams encounter decreased productivity due to misaligned communication and coordination issues.

The Dynamics of Team Collaboration

In the tech industry, product managers, designers, and engineers often work on the same project but follow different timelines, necessitating frequent meetings for synchronization. This results in excessive meetings, leaving insufficient time for actual work. Offline collaboration is hampered by difficulties in booking spaces and coordinating schedules, while online collaboration, though removing space and time constraints, can complicate tracking all relevant contexts and may not always be efficient.

Impact of AI on the Workspace - Learning

Context ecosystem

Individual level - Contextual Limitations

Current AI working tools, despite their advanced capabilities, still require users to manually input specific information each time a key result needs to be generated. This manual intervention is necessary because these tools lack the ability to autonomously identify the current phase or context of the user's work.

Without this contextual awareness, the AI is unable to proactively generate the content that the user needs at any given moment. As a result, users are often burdened with the repetitive task of feeding the AI with the necessary data and instructions, which not only slows down the workflow but also limits the potential for truly automated and intelligent assistance.

To overcome this limitation, there is a growing need for AI systems that can track the progression of tasks, understand the context in which they are being used, and independently produce the desired outputs without requiring constant human guidance.

For instance, generative slide tools like Pop AI, Gamma, and Tome offer the capability for users to upload a file to assist in creating presentations. However, these tools are currently limited by allowing the upload of only a single file at a time. This restriction means that they are unable to process multiple documents simultaneously, especially when these documents come in different formats, such as PDFs, Word documents, and spreadsheets.

This limitation forces users to manually consolidate information from various sources before uploading, which can be time-consuming and inefficient. The inability to handle and integrate multiple documents at once significantly hampers the potential of these tools to streamline the presentation creation process, particularly in complex projects where information is scattered across various types of files.

Individual level - Personalization

The trend toward personalization is rapidly transforming the way AI products and services are designed and delivered. In today’s AI products, like Taskade, users can customize their AI agents into different roles to better satisfy their specific needs. This level of personalization not only enhances user satisfaction but also ensures that the outputs are more relevant and effective in meeting business goals.

Business level - Consistency and cohesion

Despite advancements in AI tools, there is still a notable lack of consistency when it comes to generating content in a work setting. It’s similar to how a design system functions for designers; we rely on it to easily pull components that ensure our designs systematically align with the brand, providing a seamless experience for users who can instantly recognize the brand, even when new products are introduced.

Similarly, products like Jasper, which focus on AI-generated marketing content, offer features that allow users to input materials to help generate a consistent brand voice, ensuring that the AI-generated content aligns with the company’s branding. However, current AI tools struggle to achieve the same level of brand consistency and cohesiveness across a project.

At the company level, a more consistent and process-oriented approach, focusing on the project as a whole rather than individual tasks, is essential. Unfortunately, AI tools currently fall short, requiring users to manually reset and ensure both consistency and cohesion, which remains challenging.

This ongoing need for manual adjustments highlights the gap between AI's potential and its current limitations in maintaining a cohesive brand identity and project alignment across various elements.

Information security

During meeting

AI tools have dramatically transformed the working experience by making processes more efficient and productive. During meetings, AI can transcribe conversations and generate notes, summaries, and highlights, ensuring that those who miss the meeting can easily catch up. This capability enhances productivity and ensures continuity in collaborative efforts.

After meeting

AI tools facilitate better collaboration by managing and organizing information more effectively. They can automate routine tasks, allowing employees to focus on more strategic activities. The revolution of AI in the workspace has streamlined many processes, making remote work more effective and reducing barriers to collaboration. As AI continues to evolve, it will undoubtedly bring even more significant changes to how we work, collaborate, and innovate.

For future work efficiency tools, consider integrating AI to analyze data, identify potential bottlenecks in project planning, and assign action items to specific team members.

Is the current AI interaction ideal?

AI tools facilitate better collaboration by managing and organizing information more effectively. They can automate routine tasks, allowing employees to focus on more strategic activities. The revolution of AI in the workspace has streamlined many processes, making remote work more effective and reducing barriers to collaboration. As AI continues to evolve, it will undoubtedly bring even more significant changes to how we work, collaborate, and innovate.

For future work efficiency tools, consider integrating AI to analyze data, identify potential bottlenecks in project planning, and assign action items to specific team members.

Future AI Interactions

AI Thinking Like a Human

Future AI should emulate human interaction patterns, enhancing its ability to interact and understand the world more effectively. As humans, we learn, observe, understand, interact with the environment, and make decisions. Ideally, AI should develop neural networks to actively engage in these processes, enabling it to know us better than we know ourselves.

Achieving Ideal Interaction

Mapping AI evolution as milestones, we can identify several stages of development:

Current Technologies:

Human input drives AI data collection through text interaction (chatbots, online customer service), voice interaction (smart assistants like Siri, Alexa, and Google Assistant), biometric interaction (facial and fingerprint recognition for security), and wearable smart devices (real-time interaction and behavior tracking).

Advanced AI Systems:

Future AI platforms could support multiple file types, collaborate with meeting tools, learn user habits, create smarter layouts, and provide feedback on presentations. AI could also interact with the audience during virtual meetings, capturing engagement patterns for future improvements.

Brain-Computer Interface:

The ultimate stage involves a brain-computer interface, enabling interaction with neural signals and collaboration with robots. Early prototypes like Neuralink illustrate the potential of this technology.

Designing for the Future

Designers should focus on creating intuitive and trustworthy AI experiences. Intuitive design ensures that new technology is accessible and easy to use, while trustworthiness is crucial for user acceptance. For example, users need to feel confident in the safety and reliability of AI-driven systems like Tesla's Full Self-Driving (FSD) feature. Educating users on how AI operates and ensuring transparency can build trust and enhance the user experience.

Summary

That’s it! AI tools have reshaped productivity, but many still lack the ability to understand user context, juggle multiple formats, or ensure brand consistency. As AI evolves, these gaps hold promise for smarter, more seamless interactions. I hope this newsletter gave you fresh insights—let me know what topics you’d like to see next!

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