How AI is redefining market intelligence — and raising the stakes

Market intelligence has long played an important role in helping companies understand the broader economic and sector landscape, aiding the development of growth strategies. 

Close to half of Singapore respondents in the September 2025 edition of the EY-Parthenon CEO Outlook Pulse survey expect geopolitical and economic uncertainty to persist for more than a year, with just as many planning to increase investments to accelerate portfolio transformation.

In such an environment, timely, data-driven market intelligence becomes even more critical, helping leaders anticipate disruptions, pressure-test strategic assumptions, and identify new pockets of opportunity before competitors do.

As companies lean more on insights when making strategic decisions, market intelligence firms need to evolve. Some firms, notably the larger ones, are already integrating AI to enhance forecasting accuracy and deliver personalised, scenario-based insights. 

Alleviating the data collection process

Understandably, producing market intelligence reports can be laborious. It involves collecting data from various sources, analysing it for insights, and synthesising it into reports. 

The most obvious way that market intelligence firms can tap into AI is to use it to process data and transform it into a format that can be easily analysed. For analysts, this means they are relieved from the mundane tasks of data collection and can focus more on planning intelligence and data models, analysing information, and making sense of trends. 

Importantly, analysts can focus more on the critical tasks of connecting the dots and leveraging human judgment and context to develop a point of view for the report — all of which, at this point, cannot be achieved by AI. 

Going beyond structured data with AI

Even as AI holds the promise of enhancing productivity by automating data collection and filtering the correct data, the reality is that it can do more. 

Research has found that over 400 million terabytes of data are generated every day, with total data generation forecasted to surge to over 180 zettabytes by the end of 2025. Notably, over half of the data generated comes from videos, and this trend is only expected to grow. 

Traditional data collection methods would only cover structured data — defined as organised information formatted predictably, typically in rows and columns, making it easily searchable and analysable by databases and software applications. Videos, social media posts, images, and emails are unstructured data that lack predefined formats or organisation, making them challenging to categorise and analyse. 

With AI, complex data such as unstructured data can be organised into a format that analysts can use for analysis, leading to more robust forecasts. Additionally, with the right model, AI can be programmed to automate data collection more regularly, enabling more frequent analysis and trend forecasting. 

Leveraging AI beyond data collection

Aside from collecting and sorting structured and unstructured data for analysis, another area where analysts can leverage AI is for productivity and efficiency in visualising endless types of scenarios for trend forecasting. 

The conventional approach to scenario visualisation requires analysts to make various assumptions and tweak the data model’s parameters to develop predictions and insights for different scenarios. 

Agentic AI — AI systems that possess the ability to act autonomously and make decisions based on programming and learned experience, performing tasks, adapting to new situations, and interacting with their environment without constant human intervention — has the potential to change the approach to scenario visualisation. 

With agentic AI, thousands of scenarios can be visualised by simultaneously tweaking parameters. Analysts will then need to leverage their human judgment to verify whether the AI agents’ assumptions are correct and whether the forecast is accurate.

For example, different supply chain disruption scenarios, from factory shutdowns to port congestion and new tariffs, can be tested instantly across multiple jurisdictions, allowing companies to stress-test suppliers, pre-empt bottlenecks, and adjust their inventories accordingly.

This also means that market intelligence firms can easily personalise reports for different clients based on their requirements, expanding their market opportunities and outreach. In the consumer products and retail sector, AI can simulate how changes in raw-material costs or shifts in market demand affect product pricing and inventory needs.

Is AI an enabler or competitor?

AI can bring considerable benefits to market intelligence firms, but it also carries risks. While AI accelerates data collection, pattern detection and forecasting, it can also amplify inaccuracies if the underlying data is flawed and generate insights that lack contextual understanding. 

Democratisation of AI tools may also risk turning low-level market analysis into a commodity, putting pressure on firms that rely on standardised reports rather than differentiated expertise. The widespread availability of AI tools also means that companies can increasingly conduct their own trend analysis and forecasting, potentially disrupting traditional business models within the market intelligence sector.

To pivot to the new future and remain competitive, market intelligence firms should view AI as both an enabler and a competitor, and take a moment to rethink their value proposition. This means adopting AI where it improves efficiency and analytical depth, while doubling down on the uniquely human elements that differentiate their work: contextual interpretation, industry intuition, and ethical judgement. 

Analysts need to gain data literacy and critical thinking skills to ensure AI enhances rather than replaces human judgment. In essence, striking this balance requires continuous upskilling, workflows that pair human expertise with algorithmic output, and clear responsible AI frameworks.

Firms that embrace AI early, while safeguarding human authenticity in their methodologies, will be best positioned to drive innovation, maintain client trust, and compete long-term in an increasingly complex and competitive environment.

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organisation or its member firms.