News and media
5 May 2026
Artificial intelligence is already revolutionizing market intelligence practices. After transforming information gathering, it is now preparing to revolutionize how information is qualified, disseminated and even consumed within organizations.
For a long time, business intelligence systems have been designed around a relatively simple logic: collect as much relevant information as possible, filter it and then share it in the form of newsletters, press reviews or reports intended for a group of decision-makers.
This model remains relevant, but it is evolving rapidly. The introduction of generative AI now makes it possible to automate tasks that until recently were considered highly labor-intensive: reading, summarizing, categorizing, enriching or even formatting content.
Faced with these changes, a question arises: how can we effectively organize our market intelligence and its deliverables in a world where artificial intelligence is becoming a valuable collaborator?
For years, business intelligence professionals have devoted a significant portion of their time to selecting the most relevant information from among thousands of available content sources.
This curation step was essential. It made it possible to transform a mass of heterogeneous information into directly actionable knowledge.
Today, artificial intelligence models have reached a level of maturity that allows us to delegate much of this work with a growing degree of confidence.
AI is now capable of:
This does not mean that human expertise is disappearing. Quite the contrary.
The role of the analyst is gradually shifting from selection to orchestration, quality control and analysis. The challenge is no longer to read every piece of information but to ensure that the system produces the right information at the right time.
Value is thus shifting from production to supervision of collective intelligence.
Automating curation isn’t simply a matter of asking an AI model to summarize an article. Achieving reliable qualification requires a much more sophisticated approach.
At Cikisi, we have developed a qualification system based on several successive layers of analysis. This approach enables us to achieve uniform qualification on a large scale while maintaining the accuracy expected in market intelligence and competitive intelligence projects.
The challenge is not simply to automate the reading of a document, but to replicate the analytical steps that a monitoring expert would naturally take before sharing information with a decision-maker.
Most current monitoring systems still rely on a mass-distribution model: the same newsletter is sent to dozens, or even hundreds, of employees.
However, the needs of a sales director, a marketing manager, a buyer or an executive are rarely the same.
AI is paving the way for a new generation of hyper-personalized deliverables.
In the future, a single competitive event could be presented from different angles depending on the recipient’s profile:
Each user will receive a summary tailored to their responsibilities, industry, areas of interest and even their browsing habits.
This evolution likely represents one of the most profound transformations in business intelligence since the advent of search engines. The challenge will no longer be merely to produce relevant content, but to provide each user with exactly the information they need to make a decision.
With this in mind, business intelligence platforms will need to combine powerful data collection capabilities, industry expertise and large-scale personalization.
One of the most frequently asked questions since the advent of AI is about the future of Boolean queries.
For decades, they have served as the universal language of business intelligence. Operators such as AND, OR, NOT, along with parentheses and proximity operators, have enabled impressive levels of precision in information retrieval.
Should we consider this approach a part of the past? Probably not.
Boolean queries remain one of the best ways to precisely control the scope of monitoring, particularly when it comes to evaluating results and preparing the monitoring process. However, their use may gradually become concentrated in the hands of monitoring specialists.
AI already allows non-expert users to express their needs in natural language and let the system automatically build the most appropriate search strategies.
In the future, Boolean queries could play a role comparable to that of SQL in databases: a language that remains essential but that is primarily used by experts to refine, control and optimize results.
Ultimately, this development is excellent news. It democratizes access to market intelligence while preserving the advanced investigative capabilities that organizations need.
While AI is transforming the way information is collected and disseminated, another challenge is taking center stage: interoperability.
Companies already use a wide range of tools to manage their operations: CRM systems, collaborative platforms, BI tools, knowledge management solutions, enterprise applications or conversational AI assistants.
In this context, the business intelligence platform can no longer operate as a standalone system.
Strategic information must be able to flow freely to the tools where employees actually work.
A competitive signal detected today must be able to automatically enrich a CRM system. Market information must be able to feed into a business intelligence platform. A monitoring report must be able to be integrated into an internal AI assistant or a collaborative environment.
The future belongs to systems capable of connecting the right information to the right business process.
This integration capability is even becoming a more important selection criterion than the simple collection of content.
Organizations are no longer simply seeking access to information. They want to leverage it immediately in their daily workflows.
That is why new-generation monitoring platforms will need to combine three fundamental qualities:
Artificial intelligence does not spell the end of business intelligence. On the contrary, it marks its entry into a new phase of maturity.
The organizations that will succeed tomorrow will be those that know how to combine the power of automation with the methodological rigor of professional market intelligence.
The quality of sources, the management of monitoring scopes, the expertise of human analysts and the governance of information will remain essential elements.
But the way strategic information is produced, disseminated and consumed is already changing.
At Cikisi, we are convinced that the future of market intelligence rests on this combination of comprehensive data collection, trustworthy artificial intelligence and open interoperability. The goal is not to replace human expertise, but to enable experts to focus on what creates the most value: understanding, anticipating and deciding.
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