Data & AI: 10 Trends to follow in 2025

2025 looks to be a decisive year for companies seeking to fully leverage the power of their data and AI. In a context where digital transformation is everywhere, the ability to innovate, automate processes and anticipate customer needs is crucial to stand out in an increasingly competitive market.

Additionally, adapting employees’ skill sets to new technologies and digital tools will be a key success factor.

At ACT-ON GROUP, especially within our data business line, we analyzed the 10 major trends set to drive organizational and technological evolution in the coming months. These issues - spanning cybersecurity, data management and user experience - are shaping how strategic decisions will be made.

1. THE EVOLUTION OF GENERATIVE AI MODELS

This year, generative models like ChatGPT will take a new step in corporate adoption. These tools won’t just generate text content but will play a key role in advanced data analysis.

However, these future smart agents will reshape work by letting employees focus on higher-value tasks while AI handles automated analytical processes and reporting.

Companies will harness these algorithms to produce dynamic dashboards, craft complex forecasts and automate report generation. This deeper integration will enhance analytics capabilities while simplifying decision-making. It represents a major shift in how organizations approach data management. [1]

2. THE RISE OF DATA MESH

Centralizing data is gradually giving way to the Data Mesh concept, granting greater control to business teams and encouraging broader collaboration and faster decision-making. [2]

Each team can now focus on tasks tied to its particular expertise, while following unified governance rules.

HR, marketing or finance departments will have direct access to relevant information.

This distributed model meets the need for agility and speed, transforming data management so each player is both a user and a contributor.

3. GREATER FOCUS ON RESPONSIBLE DATA

In a world where cybersecurity and data privacy are critical, companies must prove they use data and technology ethically and transparently.

In 2025, “data ethics” labels will be a new mark of trust! [3]

Those who ignore these issues will lose the trust of customers and partners. Fighting algorithmic bias and complying with GDPR will be essential. Responsible data management becomes imperative in an ever more regulated environment!

4. AUTOMATING DATA GOVERNANCE

Faced with ballooning data volumes and complexity, organizations are investing in automated solutions to ensure compliance.

In data management, lineage tools, automated catalogs, and smart classification systems will track data flows in real time and cut down on human error. This approach transforms how data resources are used, ensuring safer, more effective utilization.

5. A “DATA-FIRST CULTURE” IN DAILY OPERATIONS

Data will no longer be reserved for analysts or IT experts.

First, low-code and no-code tools will let non-technical staff handle complex information, generating strategic insights. [5]

Second, this democratization fosters a data-first culture, empowering every employee to drive innovation and decision-making!

If adopted, this approach will boost business competitiveness by speeding up and refining decision-making.

6. THE EXPLOSION OF REAL-TIME DATA PLATFORMS

Demand is mounting for technologies that can process real-time data streams.

Companies are deploying solutions to gather, analyze and utilize data instantly. These platforms are vital to industries like e-commerce, logistics or finance, improving reactivity and user experience! [6]

Such tools directly address rapidly changing markets, where every second counts in decision-making.

7. MORE EFFICIENT HYBRID PREDICTIVE MODELS

By combining classic algorithms with deep-learning techniques, hybrid models deliver more accurate results in areas like inventory management and sales forecasting.

This tech evolution lowers costs while raising operational efficiency. As a result, such models will become indispensable for users tackling intensifying market demands. [7]

8. DATA SOVEREIGNTY AS A STRATEGIC LEVER

Data location and management remain top priorities, especially in the face of ever-stricter regulations. Companies must keep data in environments compliant with local laws and maintain strong cybersecurity.

This push toward greater sovereignty unfolds in a context where global relations directly influence how organizations implement digital technologies. [8]

9. STRONGER HUMAN-AI COLLABORATION

AI doesn’t aim to replace humans but to expand their expertise.

This year, data teams will work closely with algorithms to interpret complex data and solve strategic issues.

Such teamwork blends AI’s analytical power with human creativity, unlocking groundbreaking project-management outcomes. [9]

10. FOCUS ON DATA SUSTAINABILITY

Sustainability goes beyond products to also include data infrastructure.

Companies will seek to reduce environmental impact by optimizing data centers and implementing eco-friendly cloud storage practices.

This approach meets user expectations and mirrors greater global awareness of environmental challenges. [10]

This year stands as a major turning point for businesses. Those able to harness these data trends and translate them into strategic implementations will remain competitive and innovative.

All in the midst of a tense macroeconomic climate with tough competition—both commercially and in recruiting top talent.

That’s why we recommend embracing these new practices and solutions now to gain a competitive edge aligned with market and user needs. Companies that seize this transformation will gain a head start, as data-driven innovation will underpin strategic decisions.

So, are you ready to turn data into a competitive advantage? HR, Comp & Ben, executives… start getting the most out of your data now with our DATA for people solution.

Discover ACT-ON DATA. A project? Contact us.

^{[1] ProactiveAcademy, ChatGPT and its business impact}

^{[2] IBM, “What is Data Mesh?”}

^{[3] OpenDataSoft, Data Ethics}

^{[4] JDN, Data Lineage: Definition, Principles and Tools}

^{[5] AWS, What Is Low-Code?}

^{[6] Databricks, Real-Time Analytics Solutions}

^{[7] IBM, Supply Chain Optimization with AI}

^{[8] CNIL, GDPR: What Does It Mean?}

^{[9] Keyrus, AI and Human Interaction}

^{[10] Hellocarbo, Data Center Energy Optimization}