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What are the limitations of AI in taking meeting notes

What are the limitations of AI in taking meeting notes

AI struggles with nuances, context, and crosstalk, often misinterpreting sarcasm and requiring high-quality inputs for accuracy, affecting its reliability.

Limitations in AI Meeting Assistants

Artificial Intelligence has profoundly impacted multiple industries within the business, and one of the major areas of development has been the automation of meeting notes. However, there are still several limitations that AI meeting assistants face. In this article, these limitations will be explored, and insights and real-life examples will be provided to demonstrate the current situation.

Issues with Understanding the Context

A major challenge that AI still faces today is understanding human conversations based on all the tones and contexts that shape them. AI is good at transcribing the words but not so at understanding the way a specific phrase should be taken: sarcastically, humorously, or with a specific shade of meaning – subtly. For instance, “Great job on that project” could simply be a comment on a person’s good work but could also be a sarcastic comment on a failed project, and the way the phrase should be taken largely depends on the context. Consequently, meeting summaries produced by AI can be factually correct but, due to a lack of context, they can also be very misleading.

Struggling with Crosstalk and Multiple People Talking

Another current limitation that AI has is the inability to perfectly deal with everyone talking over each other during meetings – crosstalk. It is challenging for AI to recognize speakers and their words, resulting in missing or overlapping speech on the meeting notes. For instance, research has shown that when there is background noise, AI’s voice recognition declines significantly as some algorithms primarily rely on comparing low-frequency audio signals. Speaker recognition is also flawed when multiple people are speaking at once. In some cases, when people talk over each other, two or more conversations tend to blend together because simultaneous talks are too much for the technology.

Technical Limitations and Reliance on Good Input Data

The performance ability of AI is still largely dependent on the quality of the input data. The audio output quality is heavily reliant on the equipment with which it is recorded and on the presence or absence of background noise. It can sometimes be challenging for AI to recognize accents if they are very strong or are not very common for the technology. Additionally, sometimes problems arise that can not be predicted and avoided – equipment failure or other technical difficulties may lead to the AI not penning something in the notes. The only relative solution to a problem that can give more predictable results is ensuring the best possible quality of recording and having specific recording equipment, which is not always an option since many meetings are online or offline with no professional recording equipment.

Identifying and Accessing Past Notes

To begin with, in the context of today’s dynamic business reality, the ability to access past meeting notes in a relatively easy and quick way is an element of strategic importance . However, this task is not entirely without challenges, especially in relation to using AI meeting assistants. The following sections of the paper will aim to explore these matters in more detail and provide some reflections on how to overcome related issues.

Search and Organization

It is crucial to accept that even if an AI tool is capable of transcribing and converting human speech from various types of calls into a textual format, the test is how well it can be found. Apparently, there are decent solutions that ensure very effective searches through lots of written documents; however, they usually rely on proper tagging and unambiguous or canonical keywords. Sometimes, either the user has not added the right tag to the document, or they do not know what the keyword should be . In that case, the chances of finding what one needs are approximately as high as finding a needle in a haystack and feeling pain in doing so.

Integration with Other Systems

Most businesses are multifunctional and use many tools for a variety of tasks that have to be assistantized, from a CRM system and a chat tool to JIRA or RTM. Therefore, an AI meeting assistant has to be able to pass exactly to these systems in order for them to work most effectively, based on the decisions made during and documented after the meeting. Notably, in this respect, some particular challenges may arise because it can be really difficult to ensure perfect compatibility with both Non-AI tools and other tools based on AI. In such a situation, one will generally have to write a special adapter for a custom CRM system or part with time and money and follow a similar trajectory in terms of integration.

The Shortcomings of AI in Efficient Record Keeping

AI has the potential to be a source of numerous brilliant solutions regarding record keeping, but certain shortcomings affect its everyday efficiency and reliance. This section will explore particular cases in detail, relying on examples and data to highlight the numerous ways in which artificial intelligence fails to effectively manage records.

Transcription and Data Entry Accuracy

One of the most important aspects of record keeping is its accuracy, which almost directly determines the system’s effectiveness. However, AI-driven transcription and data entry are far from accurate and relevant in various cases. The primary sources of error include poor audio quality during a meeting, the speaker’s accent, or complex industry-specific words as suggested by Gini. According to another one of Gini’s articles, AI transcription services usually report an error rate between 5% and 15%, depending on audio quality and accent. The existence of such transcription defects forces people to spend more time reviewing and correcting files. AI has become a burden rather than an additional time-saving tool this feature should have been.

Document Formats and Structures

AI tends to struggle with any kind of excessive complexity, which can be present in structure or format. Complex pictures, tables, or graphs misguide the mechanics, resulting in the loss of important information . An AI system is unlikely to manually and accurately extract data from a financial report ready to send to shareholders. It is also rather unlikely to correctly capture data from a technical schematic. Important data will simply be lost due to the limitations that currently plague AI as a whole, preventing companies from keeping proper records.


The effectiveness of an entire AI-based record-keeping system effectively depends on the initial setup and training. An AI ignores the need to understand specific words and rules that always requires an investment of time and money. AI implementation is not a feasible option for too many small businesses or startups. Moreover, every company develops over time, introducing new specific terms, names, and other information. AI record-keeping systems are far from perfect in this regard, as multiple training sessions are necessary.

Data Breaches and Privacy

AI is used to store an increasing amount of data, which adds to the security and privacy issues. Great amounts of data present targets for cyber-attacks, contributing to the rising tendency of data breaches that may become common. Certain data must be also kept in a strict compliance with certain rules such as the GDPR and HIPAA. These issues should be addressed with the adherence to stringent security regulations and constant vigilance.


However innovative an AI record-keeping solution is; its implementation is not fully integrated. Compatibility is a major issue, as AI may cause errors, leads to lost data, and is forced to work in isolation.

Inaccurate Transcriptions and Context Loss

Seamless automation for AI in meeting transcriptions faces formidable challenges such as inaccurate transcriptions and context loss. These challenges do not only impair effective record-keeping but also affect organization decision-making. The identified challenges are:

Inability to Capture Verbatim Dialogue

Besides linguistic discrepancies, inability to capture the verbatim dialogue is a formidable challenge of using AI to transcribe meetings. Previously, significant advancements have been made in the development of speech recognition capabilities for AI. However, they are largely ineffective in acknowledging various accents, dialects, or industry-specific jargons. For example, when a meeting of relevant stakeholders to a medical board uses technical and complex medical terms, the AI transcriber is highly likely to make errors of transcription. Some studies indicate that error rates in transcription are exposed to significant spikes when AI transcribers encounter unfamiliar terms . As such, the not creating viable records for the meeting by producing inaccurate meeting notes.

Misinterpretation of Context and Tone

Use of AI to transcribe meetings cannot be used effectively to interpret context and tone. In normal communications, humans often rely on multiple subtleties of a conversation that include irony, sarcasm, and emotional undertones, which may not be immediately apparent or transferable to written records. As such, the AI is incapable of understanding the context and tone of the meetings, which is problematic in various scenarios. For example, if a decision-maker asked whether the timeline of 12 hours was feasible for the teams and a team member sarcastically expresses his/her agreement and willingness to commit his/her career to meet the deadline, he/she remains wary. Slight incorrect transcriptions of concerns in the context of its delivery are misplaced because the AI transcriber failed to appreciate their delivery was sarcastic. Therefore, it is critical to improve AI to a sufficient level where it can effectively transcribe meetings without such glaring issues.

Inability to Attest Correct Speakers

Another issue is the AIs inability to confirm different speakers, especially in meetings with multiple participants. This problem is obvious in meetings with overlapping dialogues or with speakers with similar vocal characteristics. The failure to attribute specific comments to a particular speaker leaves AI with the challenge of categorizing the comments to make the meaning of the text or the contribution of the speaker unrecognizable or questionable. For example, valuable input from stakeholders suggested by the human team might be misunderstood for a contrary decision and the reason it remains clearly unfamiliar to the meeting audience. Therefore human-based oversight in the transcription of meetings is fundamental based on the significant natural language and contextual nuances challenges facing AI. In the event of human oversight and review, the meeting transcriptions help in making that they are accurate and that sufficient safeguards can be developed around them to make them valuable records for a meeting.

Complications in Locating Specific Meeting Notes

Finding particular notes from meetings in the seas of digital records is one of the facets. Despite machine learning and AI advancements, it can still be difficult and, at time, ineffective, to find the needed information for several reasons.

Insufficient Tagging and Categorization

The main issue for most note-related search problems is inadequacy of tagging or indexing. In other words, if a set of meeting notes was not comprehensively tagged based on the names of the topics, participants, specific terminology, or terms, finding a needed set of notes is a challenge. For example, an executive may need to find notes from a meeting six months ago where a specific product was discussed. However, if the notes were not tagged with the names of the product and specific terms, the system will not find the notes quickly, if at all. One possibility would be to request all meeting notes from the past week and filter the set manually. As a result, the time needed for the search is significantly increased.

Volume and Redundancy

This factor is closely connected to the previous one and often involves similar issues. The point is, the more the notes and the more similar topics are discussed at different meetings, the more redundant and alternative not-sets are created. With those, the search system may be unable to find the particular notes from a specific meeting. As an example, a team may have weekly meetings related to a specific project and accumulate tens of note sets. The system may not distinguish minor variations between similar notes sets, especially if it is being updated rarely.

AI Limitations

While AI search algorithms are powerful, they have certain limitations. The main issue is that AI can search through notes only within the words and phrases from them. If a particular note is titled a one specific term, while it would be more logical for a person to search and remember a meeting under a different one, the search will be ineffective. As an example, there may be a meeting where the team discussed budget readjusting, but AI may vase its search on financial planning term, organizing the notes.

Access Issues

Another challenge may be related to access permissions and availability. If a certain type of notes is stored in the departmental drive for which the particular user does not have access, they are probably unaware of the meeting or ability to easily request the notes. At the same time, giving everyone access to every note is both unfeasible and unsafe in terms of confidential business information. Thus, note access is regulated but may be too restricted.

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