In an office setting, meetings are the lifeblood that keeps projects on track and teams in sync; however, they can often become cumbersome and unproductive. Long in duration with repetitive occurrences, remote participants, multiple simultaneous topics of discussion, incoherent verbiage, etc. are only some of the challenges we face in team meetings.
Enter Generative Artificial Intelligence (GenAI) – a game-changer in objectively evaluating and enhancing their effectiveness.
GenAI leverages advanced algorithms for analysis and can function as a virtual assistant, providing valuable insights and recommendations to improve future meetings.
Comprehensive Engagement Analysis
One of the key advantages of leveraging GenAI to evaluate meetings is the ability to track and analyze participants’ engagement levels, including real-time, from the vast quantities of un-structured data. Through video/image-based algorithms, GenAI can monitor participants’ facial expressions, body language, and speech patterns comprehensively.
Present day technology (e.g., Microsoft Teams ASR, OpenAI Whisper) can generate accurate written transcript from spoken words. GenAI can analyze this meeting content through Natural Language Processing (NLP), breaking down the conversation and identifying key themes, the distribution of speaking time among participants, and even the sentiment behind specific statements. This results in a thorough examination of whether the meeting stayed on topic, if all voices were heard, and if the desired outcomes were discussed.
A common pain point in meetings is time management. How often do meetings run longer than planned, causing scheduling conflicts and general frustration? GenAI can track the actual duration of the meeting against the planned schedule and evaluate adherence to the agenda. By identifying which segments of the meeting consumed more time than allocated, actionable insights can be generated on improving time management.
Participation and Effectiveness Scoring
GenAI can aggregate various metrics – such as participant engagement, content relevance, and agenda adherence – into a comprehensive effectiveness score. This score provides a quantitative measure of the meeting’s success, serving as an invaluable tool for reflecting on the meeting’s overall impact.
The scoring can be done at individual-level and improvements captured, where needed.
For instance, if a participant consistently scores low on relevance or clarity, providing constructive feedback and offering training or resources can help them improve their communication skills.
The automated results of GenAI can be easily verified e.g., manual means using surveys and/or via automated NLP utilities in Python programming language (e.g., syuzhet, topicmodels, lda, etc.).
Balancing Technology with Human Judgment
GenAI generates data-driven insights to help tailor meetings for every participant.
It does have a few intrinsic limitations e.g., it responds differently for the same input. Here, it is necessary to regularly fine-tune and validate the AI algorithms via prompts to help minimize discrepancies and improve their accuracy.
Further establishing clear evaluation criteria and benchmarks will ensure consistency in the assessment process.
While GenAI is impressive, it is essential to remember that it should complement, and not replace, human judgment. The rich, qualitative nuances that come from human experiences and interactions are elements that AI cannot fully replicate still. Therefore, the ideal approach is to integrate GenAI with the instinctual and experiential knowledge of human professionals and derive better results.
