Artificial Intelligence Serves Competitive Intelligence

At the 5th Forum of African Economic Intelligence Associations (Dakhla, Morocco) Prof. Alexander Sokolov, Deputy Director of Institute for Statistical Studies and Economics of Knowledge  spoke how AI can accelerate search methods of futures studies and enhance market and competitor analysis. 

AI generated picture

Alexander Sokolov explained that Foresight and competitive intelligence often use the same methods.

Unlike foresight, which forms a strategic view, competitive intelligence focuses on the tactical aspects of foresight – competitors’ actions monitoring, searching for promising scientific research, and identifying areas of likely technological breakthroughs. Short time horizon of foresight turns competitive intelligence into “early warning system” for potential threats and opportunities.

Competitive intelligence is a system of methods for collecting and analyzing open data on competitors, trends and new technologies. It is used to gain competitive advantages obtaining and making informed management decisions. Unlike industrial espionage, this tool is ethical and uses only legal sources.

According to Russian researcher, competitive intelligence faces challenges common for all types of futures studies: overabundance of irrelevant information and change of expert knowledge social role. Companies and government agencies, on one hand, are drowning in ocean of open data — news flows, patents, scientific publications, social media — where it is difficult to separate meaningful “weak signals” from information noise. On the other hand, expertise traditionally used to distil useful information from data sets is a subject of experts' cognitive distortions and depends on assessments shift due to their narrow specialization.

"This is where artificial intelligence tools, initially tested in futurological research, come to the fore," the expert believes.

Alexander Sokolov listed several key areas in which AI enriches foresight and competitive intelligence methods.

1. Automated Big Data Analysis

Machine learning algorithms and NLP (natural language processing) are capable scanning thousands of sources in real time, identifying emerging technological trends, tracking competitors' R&D activity (through the analysis of patents and scientific articles), and capturing the first, still weak signals of changing consumer preferences or new business models emergence.

2. Scenario modeling and roadmapping

AI can process multiple variables and model complex cause-and-effect relationships. This allows for not just extrapolating current trends, but also creating multiple, well-developed scenarios for the future development of market, technology, or regulatory environment. For competitive intelligence, this means moving from a reactive "what did competitor do?" to a proactive "what will happen to market if...?"

3. Overcoming expert bias

Integrated methods such as the modified Delphi method, where AI aggregates, anonymizes, and structures expert opinions, help move beyond groupthink and produce a more balanced and objective picture of the future.

Prof. Alexander Sokolov is making presentation

Alexander Sokolov

AI integration with foresight and competitive intelligence methods marks qualitative leap from collecting information on state-of-the-arts to creating a dynamic map of the future that identifies both potential threats and hidden opportunities. This is especially relevant for fast-growing markets such as those in Africa, where new approaches are required to close the technological gap.

According to the scientist, in transforming long-term forecasting from an academic exercise into a practical decision-making tool, researchers must overcome the resistance of inertial management models, institutional barriers, and entrenched biases against third-world countries. Augmenting foresight methods with AI tools could be a solution to this "strategic blindness," the professor believes.

Alexander Sokolov's report was part of a large-scale discussion on the role of knowledge and technology in ensuring co-emergence and sustainable development in Africa, confirming the leading role of Russian scientific school in futures studies globally.