Monitoring and AI: can the robot replace the monitoring analyst?

For several months, artificial intelligence (AI) has been at the center of attention for businesses. Help with writing, summarizing text corpora, automating tasks… Promises which suggest that certain professions are threatened by artificial intelligence. While new developments are often welcomed with enthusiasm, they can sometimes cause concern among teams. What about the arrival of AI for the job of monitoring analyst? Should the monitoring manager be afraid of being replaced by artificial intelligence?

 AI affects the entire sleep cycle

With the exponential progress linked to AI, and more particularly to the launch of Catgut, we can see what these technologies are capable of bringing to the day before. We will discover, these tools can create real added value to the monitoring cycle.

1. Expression of needs

This involves understanding and delineating the client’s information needs.

Artificial intelligence will be used here to become familiar with:

  • the sector in which the client operates
  • know all the ins and outs
  • list potential competitors, or even assess the risks and opportunities associated with them.

For example, it is possible to ask Catgut to produce PESTEL and SWOT matrices, then from the information collected, to create a monitoring plan which will be able to list the different competitors, sources to monitor (general and specialized), Key Opinions Leaders or even keywords.

The results can be general or very advanced depending on the level of precision of the prompt made by the AI. Note also that artificial intelligence can be used to understand a subject that the analyst does not know or, on the contrary, to explore a subject that he or she masters. The more we immerse ourselves in the subject, the more we enter into an interesting conversation over time with the tool. By having precise knowledge of a topic, the analyst is able to ask pointed and detailed questions to ChatGPT, local seo services which enriches the conversation. The monitoring manager is also able to bring his own nuances and thus ask more complex questions to the tool, forcing it to deepen its answers.

2.  Sourcing and gathering information

Social Listening consists of identifying keywords and sources useful for the analyst’s research work. AI will be used in this step to identify these sources, but not for their monitoring. In fact, tools like ChatGPT only have access to a corpus of text up to September 2021. It is therefore incapable of monitoring sources and reporting information that would be interesting for the watcher in 2023.

We can ask ChatGPT to list the lexical field of our client’s sector as well as the synonyms. Some tools are capable of identifying Key Opinion Leaders to monitor as well as their websites. Other AI, like Perplexity, can generate RSS feeds on specific themes. For competitive monitoring, it is possible to ask this tool to list the different sources to be monitored such as competitors’ sites, book marketing agency  specialized sources in the sector or even the various social networks of competitors.

3. Use of information 

This phase of the monitoring cycle consists of the transposition of raw data into value-added information. It is broken down into 5 phases:

  • Assessment
  • Treatment
  • Analysis
  • Synthesis
  • Interpretation

Evaluating the quality of sources is a phase that cannot ignore human intelligence. Only the monitoring analyst is able to assess the quality and reliability of a source of information. AIs are not able to determine the credibility of a source. A tool like ChatGPT makes many factual errors.

On the information processing side, artificial intelligence can make it possible to reformat data for greater readability. Some AI can extract named entities in a document, which will make it easier to classify information into tags. More and more tools make it possible to extract information directly from a corpus of texts such as ChatPDF or Klavier.

The analysis phase is the phase of interpretation of the information collected. The analyst will interpret this information, extract the useful elements, then group and compare them to obtain intelligible and meaningful data. During this step, we can certainly ask ChatGPT to play a role in assisting us. Example of prompt: “As a monitoring analyst, what questions would you ask yourself to understand the issues in the oil sector linked to climate change?”

In the synthesis phase, the same problem arises. To summarize his analyses, the monitoring manager will use a specific angle. AIs are incapable of deducing thematic axes adapted to the client’s needs. Indeed, based on these needs, the monitor can determine the most relevant themes to respond to his client’s problems. This involves prioritizing certain themes based on their importance. Consequently, the summary must highlight the most relevant information and analyzes possible.

Finally, the interpretation of the results puts the newly synthesized knowledge into perspective to make it operational. Which involves the construction of hypotheses to propose to decision-makers. AI tools are thus able to suggest avenues to explore. However, this is a prediction and should therefore be taken with a step back, because they do not take into account the vagaries of the market and are, like human intelligence, incapable of predicting the future.

4. Dissemination of information 

This final phase consists of restoring the information collected throughout the monitoring process.

Here, AI is not of much use. ChatGPT is, for example, not recommended for writing a report. On the other hand, it can be useful for synthesizing or reformulating information.

What is the future of the watchman in the face of AI?

Artificial intelligence makes it possible to automate tasks such as data collection and thus save time for the monitoring manager. However, with automation also comes a lack of nuance. Indeed, only humans are capable of showing critical thinking and going beyond the limits of algorithms. For example, while AI can help with sourcing, it is unable to verify the reliability of these sources. The expert is the only one who can validate the quality of the identified sources.

In addition, the analyst must regularly face unpredictable situations, to which artificial intelligence will be incapable of adapting, because it is not programmed to deal with them.

Bringing artificial intelligence into the uses of watchmen

For the moment, artificial intelligence does not represent a threat to monitoring experts.

However, it can be used at all stages of the monitoring cycle. Used carefully, AI can simplify certain repetitive tasks and allow watchers to devote more time to analysis and synthesis work. Ultimately, these tools, as intelligent as they are, are incapable of taking the necessary perspective from the work of a monitoring officer. As Christophe Descamps, an expert in the field of monitoring, told us, “we must take artificial intelligence as what it really is, namely a tool to help with analysis.” Finally, we asked ChatGPT for his thoughts on the future of intelligence analysts as AI expands. Here is his response:

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