In recent months, I have been experimenting with various AI tools to enhance the efficiency of my work, particularly tools that make mundane tasks (that generally take a fair amount of my time) at work easier. More specifically, I was trying out tools that record meetings, summarize their content, and automatically create action points. After experimenting with various tools, Bluedot and Google Gemini stood out from the meeting assistants. Both tools have very similar functionalities and can be embedded into the search browser. However, what I found out is that their effectiveness is very different.
From the very beginning, Bluedot was proving to be much more accurate and effective than Google Gemini. It did not simply transcribe the meetings; it appeared to understand the point of them. Even if there were multiple participants or a lot of jargon (such as technical or business vocabulary), Bluedot was still able to refine concepts and translate them into precise transcripts, notes and recordings. The interface is user-friendly, and the AI-generated notes are organized so well that if I had to, I could directly share them with the participants. It creates complete sentences, logical sequences, and nicely ordered lists of action items. Bluedot also excels at separating speakers and retaining continuity across relevant points of discussion.
In contrast, Google Gemini seemed promising with the fact that it is embedded in my Google Workspace, but it was not the most consistent tool. While it performed fairly well with simple discussions, it frequently misheard or misinterpreted critical signals or phrases when participants spoke fast or when strong accents were present. As a consequence, the generated notes missed the primary intention of the meeting or summarized irrelevant references rather than the important ones. Moreover, Gemini’s summaries were far more generic than the Bluedots’, as they were missing a level of nuance and prioritized perspective that made Bluedots’ notes really valuable. However, Google Gemini does have an advanced function where it sends the meeting notes and action points to participants right away, which Bluedot does not have.
My experiments revealed that accuracy in language understanding is a critical challenge for generative AI. Even the most sophisticated large language models can fail when it involves context, accents, or technical vocabularies. For generative AI to be useful, tools such as Gemini could implement domain-adaptive fine-tuning (i.e., learning from a user’s recurring topics or project vocabulary) and include real-time feedback loops to provide corrections on summaries to adapt to the learning process.
Ultimately, my experience confirmed that while generative AI tools can be very compelling, their value is dependent on comprehension and trust, something Bluedot certainly does much better than Gemini in the context of productivity in meetings. I am interested to hear, have you tried any AI tools that enhance the productivity of your work?
