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Generative AI and Data Capture

Generative AI is the new thing now. Everyone are talking about the new AI applications ChatGPT, BARD, Google against Microsoft and vice versa.

These recent months have been utterly overwhelming. The quantity, quality and ease of access of so many generative AI tools, available for everyone is leading us to believe that we’re embarking a new era.

As always, in such steep or hype tech trends times, the effect is immediately discussed. When it comes to AI, questions and discussions are debating on issues like: which jobs will be replaced by Generative AI tech, will Generative AI get uncontrolled, what about security, data integrity, interoperability, creativity, and so on.

Generative AI comes in many forms – from the overwhelming visual Generative AI or text to image (Midjourney, Lexica, DALL-E, Stable Diffusion, and so on), where one of the first public encounters was with “Théâtre D’opéra Spatial” which won an Art Prize – this image was created with #midjourney , #dalle and #stablediffusion. And following are the various additional use cases for text to text (#chatgpt , You.com, etc.) and even text to presentation (ChatBCG) or text to music (Soundraw, MucisLM and many more). The applications are exciting and the number of possibilities seems to be ever expending.



All the tools mentioned above and others are at their core a great evolution of the NLP models now employing LLM (Large Language Model), and the artificial intelligence / machine learning technology which allows analyzing huge amount of data to generate the text/image/music/ANYTHING from a text prompt.

Now, while these tools provide significant benefits, it's important to remember that they cannot serve as a substitute for the essential process of data collection. For instance, when a sales representative leave a client's site, there is no generative AI tool available that can automatically generate a report on their behalf (I guess we will have to wait for Elon Musk’s Neuralink to be operational…🙄). Therefore, it's crucial that the individual who attended the meeting captures the relevant data; otherwise, it's of no value, and the organization won't be able to utilize the meeting to advance its relationship with the customer and provide better service.

Indeed, human-based data capture is imperative to maintaining the overall integrity and scope of the data entered. While AI tools are incredibly helpful in generating reports, identifying patterns, and making recommendations, they are limited by the quality and quantity of the data that they are provided. That leads to too many inaccurate data generated (link).

People, on the other hand, have the ability to collect a wider range of data, including subtle nuances, body language, and tone of voice. These details are often missed by AI tools but can be critical in providing insights and building strong relationships with customers.

Moreover, people-based data capture is necessary to ensure that the collected data is accurate and relevant. When relying solely on AI tools, there is a risk of introducing biases, errors, or irrelevant data into the system, which can have detrimental consequences for decision-making processes.


In conclusion, while AI tools are a valuable asset to organizations, they cannot replace the importance of human-based data capture. To ensure that the data being collected is accurate, relevant, and comprehensive, it's crucial to rely on both human and AI-based data capture processes. This way, organizations can maximize the benefits of both types of data capture and make well-informed decisions based on a holistic view of the data.

TalkSense helps with accurately data capture and updating of your CRM (e.g. #salesforce) and other platforms, by talking intuitively to your mobile app, while on the go.

Take it to a test drive:

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