Today, it is difficult to imagine a business strategy that does not rely, directly or indirectly, on business intelligence. Long confined to producing dashboards for finance or IT departments, business intelligence has changed. In 2026, it will be at the heart of operational decisions. It will speak to marketing teams, fuel commercial thinking, and even save time for human resources. This shift is due to several developments: more accessible tools, better use of data, and above all, a strong expectation from business lines to have concrete answers without waiting days for an analyst to provide them. Let's not forgetartificial intelligence, which suggests, predicts, recommends, and, when integrated with business intelligence, changes the way we make decisions on a daily basis. What are the trends shaping this transformation? How is business intelligence adapting to new uses and challenges? Here are some answers.
Towards more accessible and personalized business intelligence
Business intelligence is no longer reserved for technical profiles. This shift is not new, but it has gained particular momentum in recent months. Marketing, sales, human resources, and even senior management teams now want to be able to access information themselves, without intermediaries. They are no longer satisfied with figures; they expect clear answers to their operational challenges.
59% of executives now consider data to be an essential strategic lever for the development of their business.
This need for autonomy is fueling the rise of self-service tools. These platforms enable users to create dashboards, cross-reference data from multiple sources, and even query a dataset via a natural language interface. The goal is simple: to empower non-technical users without sacrificing the quality or reliability of the analyses produced.
At the same time, the trend toward personalization is intensifying. Gone are the days of identical KPIs for everyone; each team now expects indicators tailored to its context, priorities, and vocabulary. To meet this demand, publishers are revamping their interfaces:
- recommendation engines;
- dynamic scenarios;
- smart displays...
In short, everything is converging towards business intelligence that is more dynamic, more tailored, and more directly useful.
This evolution is also transforming everyday practices. Business intelligence no longer exists outside of business tools. It is increasingly integrated into work environments, CRM systems, management tools, and collaborative platforms. We no longer "consult" data from time to time. Instead, we work with it continuously, in a shared decision-making process.
In 2026, the data market in France is expected to exceed €3 billion, representing an annual growth rate of 4% since 2023.
67% of companies plan to increase their data budget, and 85% of CIOs plan to train their teams in data analysis by 2027.
The rise of space data
Another significant development in business intelligence is the integration of geographic data into analytical tools. Information is no longer just read in columns and graphs, but is also displayed on maps, with spatial interpretation adding a new dimension to decision-making.
This breakthrough completely transforms the way companies read and interpret their business. Maps not only illustrate a report, but become a full-fledged analysis tool, capable of shedding new light on performance, opportunities, or risks.
This type of analysis, which was still marginal a few years ago, is becoming commonplace in B2B sectors where location is important:
- commerce;
- transport;
- services;
- real estate;
- networks.
Spatial data can be used in a variety of ways, including:
- compare performance by area;
- identify underutilized territories;
- cross-reference the positions of customers, competitors, and points of contact.
What used to be the domain of geomatics is gradually becoming an operational tool accessible to business teams. The location of points of contact, customers, and competitors is becoming strategic data.
That said, to take advantage of these analyses, the data must be properly structured. Many companies realize that they have spatial data but do not exploit it. However, a customer address, a service area, and a physical location are elements that can become sources of analysis, provided they are properly identified, formatted, and linked to other data sets.
Those who manage to take this step gain a real advantage. The detailed use of spatial data provides a better understanding of local dynamics, enabling companies to tailor their offerings to the context and prioritize areas with potential. It is an interpretation of the terrain that informs strategy in real time. With the widespread use of geomarketing, companies that master this type of data will gain a competitive edge in their territorial coverage.
Artificial intelligence as a co-pilot for business intelligence
Artificial intelligence (AI) has gradually but surely become established in business intelligence tools. Today, it is no longer limited to a few automatic functions. It actively participates in analysis, helping users navigate their data, detect weak signals, and generate insights more quickly.
What we are seeing is a change in expectations. Teams are no longer just looking for readable dashboards; they want concrete suggestions, relevant alerts, and hypotheses to test. AI meets this need by going beyond visualization. It provides insights, offers leads, and suggests interpretations.
These uses are becoming widespread. Certain algorithms automatically identify trend breaks, anticipate a decline in performance, or recommend targeted action for a specific customer segment. What used to be specialized solutions are now available on platforms accessible to business users.
The goal is not to replace analysts, but to assist them. AI does not make decisions for them; it illuminates blind spots, saves time, and helps them ask the right questions sooner. This co-analysis approach is gaining ground, particularly in SaaS business tools that now natively integrate analytics.
“75% of organizations use data to improve management and optimize decision-making, and 40% use it to organize departmental tasks and facilitate internal coordination.”
Business intelligence for marketing and B2B commerce
It is estimated that 80% of marketing and sales departments use data to enhance their internal tools and structure their databases, as well as to refine their sales and marketing strategies.
Marketing and sales departments no longer expect business intelligence to simply provide them with occasional reports. They see it as a fully-fledged analysis tool, capable of making often scattered, heterogeneous, and sometimes dormant data usable.
This shift is partly driven by the evolution of the tools themselves. More flexible, more visual, and often enhanced by artificial intelligence, they enable teams to navigate their data with much greater agility. As a result, we no longer simply analyze past performance. We identify weak signals, refine segmentations, and model trends to adapt strategy in real time.
On the marketing side, business intelligence is primarily used to make complex customer datamore readable: purchasing history, digital behavior, multichannel interactions, etc. Once structured, all these signals enablea deeper understanding of customers. Segmentation becomes more accurate, indicators are adjusted to business realities, and campaigns become more targeted (and therefore more effective).
For sales teams, the added value is just as tangible. Analyzing performance by territory, channel, or product makes it possible to adjust field activities, anticipate fluctuations in demand, or better distribute efforts according to areas with potential. Geomarketing, in particular, is playing an increasingly important role in prospecting and appointment optimization plans.
Finally, thanks to Business Intelligence tools, both teams (sales and marketing) share a common understanding of results, priorities, and objectives. Indicators are developed together, decisions are based on shared data, and the sales strategy is more consistent as a result.
Business intelligence continues to evolve. It is becoming more open, more integrated, and more responsive. It is no longer limited to well-presented dashboards. It is becoming a living tool, connected to business practices, enriched by artificial intelligence, and capable of exploiting new dimensions such as spatial analysis.
What is changing profoundly is not so much the technology itself as the ambition we place on it. Companies that manage to make business intelligence part of their teams' daily routine, with the right tools, the right reflexes, and solid governance, will have a head start. Not because they will have "more data," but because they will know how to make better use of it.
