More dashboards are no longer enough to support better decisions. With dormant data, scattered information and an overload of indicators, business leaders still struggle to turn information into action. Drawing on the findings of Coface’s 2026 Data & AI Barometer, discover 3 strategies to speed up your analysis, anticipate risks more effectively and make decisions with greater confidence.
The gap between available data and actionable insight remains vast
In an economic environment that is as fragmented as it is uncertain, executive, finance and risk teams have never had access to so much information. Internal reporting, market data and customer histories give companies a considerable volume of data with which to steer their business, yet they still struggle to extract its full value.
A clear conclusion emerges from the latest edition of Coface’s Data & AI Barometer, produced in France with Les Echos Etudes and based on a survey of 900 business decision-makers: in 2026, adoption is growing, but it is not yet fully structured. The gap between available data and actionable insight remains vast: only 17% of respondents currently consider themselves truly advanced in using data to inform strategic decisions.
As data and AI become increasingly embedded in decision-making processes, the challenge for companies is now to establish the right governance and safeguards so that they can realise their full potential.
At Coface, we transform millions of data points into actionable information every day to assess risk, anticipate developments and support decision-making.
Our conviction is clear: value in trade credit risk management decisions comes from combining high-quality data, the power of artificial intelligence and human expertise
explains Nesrin Gonin, Business Information Director of Coface France and Western Europe.
1 – Identify sooner what could influence a decision
Balance sheets, income statements and payment histories remain essential for assessing a company’s trajectory. However, they only provide a snapshot of the situation at a particular point in time. In the meantime, a customer’s financial health may weaken, a supplier may run into difficulties, or the sector and geopolitical environment in a market may deteriorate. The freshness of information is now just as important as its reliability. In 2026, 68% of decision-makers believe that their data is sufficiently up to date, compared with 49% in 2025.
This is encouraging progress, but companies still need to identify which changing information could genuinely affect their decisions. It is therefore no coincidence that 42% of respondents rank reliable, real-time indicators among their top expectations for data and AI. Priorities also vary significantly by area of responsibility: finance teams will monitor payment behaviour and early signs of financial strain more closely, while procurement teams will focus more on supplier resilience, sector developments and risks that could disrupt supply chains.
Strategy to implement: for every key decision, such as granting payment terms, approving a supplier or entering a market, identify which data needs to be updated, how frequently and which signals should trigger a more in-depth analysis. The aim is not to monitor everything constantly, but to detect soon enough anything that could change the decision.
2 – An isolated data point proves nothing: context makes all the difference
A single figure rarely tells the whole story. A financial indicator may appear reassuring at first, but take on a very different meaning when considered alongside a late payment, a deterioration in sector conditions, a country’s economic climate or information the company already holds.
However, companies should avoid a relentless race to collect more data. Multiplying indicators without a clearly defined hierarchy is not necessarily the most appropriate response. It is better to select robust, complementary sources:
- internal data provides detailed knowledge of the portfolio, business partners and operations;
- external information broadens the analysis to include the economic, sector and geographical environment;
- business expertise remains essential for interpreting these signals and assessing their true significance.
The main obstacle is therefore not always a lack of information, but more often scattered and heterogeneous data, insufficient links between different sources, and limited sharing across teams within the same organisation.
Cross-referencing datasets brings together several perspectives and turns raw data into qualified, enriched information, providing a much more complete and realistic view of the situation of your business partner, whether a customer or supplier.
At biopharmaceutical group Ipsen, Coface data helps speed up straightforward decisions that require little analysis, allowing teams to focus their expertise on more complex situations.
The objective is therefore not to replace human analysis, but to apply it where it adds the most value,
says Estelle Gazin, VP Treasury, Financing and Credit Management, Ipsen.
Strategy to implement: for each important indicator, look for the information that will confirm, qualify or explain it. The question is no longer simply, “What data do we have?”, but “What does it tell us when we bring it together?”
3 – Collect less, decide better: start with the business need
Faced with the abundance of available data, it can be tempting to keep collecting every possible piece of information before beginning a trade credit risk analysis of a customer or supplier. Yet this approach can slow action and divert attention from the most important issues.
Whether the decision involves granting payment terms to a new customer, selecting a strategic supplier, adjusting an exposure or assessing a new market, each situation calls for its own analytical framework and therefore requires neither the same data nor the same level of analysis. The reverse approach is far more effective: start with the decision to be made, then work back to the information needed to inform it. Before adopting new tools or producing new indicators, it is therefore useful to define:
- the question that needs to be decided;
- the main risk factors to examine;
- the information required to reach a decision.
Strategy to implement: first express the final business need as a precise question, then retain only the information that can genuinely help answer it. Technology can speed up analysis, but it replaces neither contextual knowledge nor the judgement required when making a decision.
Discover how to turn your raw data into actionable insights for your strategic decisions:
> Explore the Coface ecosystem of solutions for managing trade credit risks :Trade credit insurance, Business Information services, Debt collection and more ;
>Contact our experts in your country today for a personalized demo.
