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Have you ever felt overwhelmed by documents, chats and meetings? In the knowledge economy, keeping on top of the vast amount of information discussed in meetings and stored in a number of tools can be overwhelming, finding the crucial information needed for the current situation can be tedious and time-consuming.

This information overload and the resulting difficulty in retrieval causes project teams in companies to experience a lack of project knowledge and information sharing, leading to inefficiencies in team decision-making and learning (Lacy & Le Roux, 2020).

Meetings are the lifeblood of collaboration, but they generate a huge amount of unstructured data which often disappears the moment they end. Traditional note-taking is a compromise: either you half-listen while typing, or you fully engage with no record to show for it. RobertAI was built to solve this problem. 

RobertAI is an AI-powered meeting intelligence platform that serves as the central hub for your projects. It captures, analyses and organises all project related data into permanent, searchable and actionable knowledge.

RobertAI’s strength lies in its ability to intelligently comprehend a wide range of content. Users can record real-time conversations, which the program will then comprehend and transcribe automatically, extracting important conclusions and useful information. Furthermore, users can submit pre-existing transcripts and sophisticated documents such as Word and PDF files. The text is then intelligently extracted by the AI, converting structured and unstructured data into an asset that can be analysed. This intelligence is founded on a strong knowledge base for every project. Teams can upload and manage any relevant information, including design mock-ups, spreadsheets and initial briefs, using this centralised knowledge hub.

Another noteworthy feature is the ability to take notes directly within the programme, which provides a simple way to record spontaneous ideas. Thanks to the combination of structured files and unstructured notes, all project-related data is kept in one easily accessible location, eliminating information asymmetry and ensuring that everyone involved is kept informed about ongoing matters.

Finally, the Chat-Feature is particularly useful when questions arise or things in the documentation remain unclear. Users can discuss their projects in natural language via the chat interface and find whatever they need by simply asking questions. For example, users can ask specific queries such as ‘Summarise the client’s comments from last week’s call based on the transcript and the attached PDF brief’. Users can even narrow in the scope of their enquiries by selecting specific meetings and publications from the Knowledge Hub to specify where RobertAI should gather the answers from.

Designed to be the hyper-intelligent participant in every meeting, RobertAI ensures that your team’s collective intelligence isn’t overlooked. It eliminates the administrative burden of post-meeting tasks, allowing everybody to focus on delivering value and doing their work rather than being stuck in administrative tasks. Say goodbye to meeting amnesia and hello to a smarter, more informed workflow. It’s time for RobertAI.

Team 42: Juul van Gurp (546066jg), Evie Burghouts (581928eb), Palle de Veer (761154pv), Johannes Erath (785513je)

Reference

Lacy, A., & Le Roux, S. (2020). A review of group memory enhancing learning and knowledge practices in team meetings. ResearchGate. https://www.researchgate.net/publication/344149069_A_review_of_group_memory_enhancing_learning_and_knowledge_practices_in_team_meetings/citation/download

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The use of generative AI by students has increased significantly in recent years, but the question is: how useful are these tools in practice? I have mixed feelings: sometimes brilliant, but sometimes frustrating. During my thesis research, I used ChatGPT for coding. The tool seemed to understand how to code and provided me with long code. I quickly realized this wasn’t efficient; the code was often unnecessarily long, increasing the risk of errors and making it difficult to understand. My next step was watching YouTube coding tutorials. The combination of these tutorials and using ChatGPT proved effective. The generative AI provided direction and ideas, while the tutorials helped with practical implementation. The question is whether generative AI will ever be able to code without human correction.

For my thesis research, I also used the DALL·E program. I’m not a creative person myself, but using this tool suddenly gave me a graphic designer at my disposal. I created my cover page and a few other images with it in fifteen minutes.

The NotebookLM program also proved very useful. When writing a thesis, you’re constantly looking for sources that support your research or show the downsides. These articles are often long and complex. Generative AI can be very useful here; you send the article as a PDF, and the program starts writing a concise summary, which is very useful. At the same time, I wondered: will I miss the nuance and details if I rely too much on these summaries? In practice, it sometimes turned out that important details were missing.

In my opinion, generative AI should be seen as a sparring partner rather than a replacement. What do you think: should we view AI primarily as a tool, or should we accept that it will eventually take over some of our thinking?

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During last week’s lecture, the use of augmented reality (AR) in aircraft inspections was discussed. Professor Ting Li’s article discusses how China Southern Airlines uses AR glasses to support maintenance engineers. The AR glasses have multiple functions: they recognize parts, display step-by-step instructions, and make it easy to remotely call in experts. The article demonstrates that AR glasses make the maintenance process more efficient and safer. The engineers’ human knowledge is enhanced by technology (Li, Wang & Wu, 2022).

I recently came across an interesting article about safety inspections at Schiphol Airport. For the first time, KLM conducted a technical inspection outside the maintenance hangar using drones (Dronewatch, 2024). Previously, aircraft were first moved to a maintenance hangar to conduct the inspection there. The drones fly around the aircraft in the open air to record potential damage from hail or lightning. The drone films everything, and the footage is subsequently analyzed with software. The advantage of this method is that it saves time, thus reducing the risk of delays.

These two cases demonstrate different perspectives. With AR, the technology augments and enriches the human, increasing the engineer’s capacity. Conversely, the drone inspection performs the inspection independently, focusing primarily on speed and efficiency. In my opinion, combining the two technologies creates a smart, fast, and safe maintenance service: drones for rapid problem detection, AR glasses for more complex repairs. However, several questions remain about these technologies. It’s not yet clear how reliable drones are in severe weather. I also wonder what role maintenance personnel will play in the future. What do you think: will drones take over the role of engineers with AR glasses, or will future inspections be performed using a combination of the two technologies?

References

De Jager, W., & De Jager, W. (2024, August 6). Eerste KLM-vliegtuiginspectie buiten hangar met drone op Schiphol | Dronewatch. Dronewatch | Serieus Over Drones. https://www.dronewatch.nl/2024/08/06/eerste-klm-vliegtuiginspectie-buiten-hangar-met-drone-op-schiphol/

Li, T., Wang, J., & Wu, F. (2022, April 5). How one airline is using AR to improve operations. Harvard Business Review. https://hbr.org/2022/04/how-one-airline-is-using-ar-to-improve-operations