In 2024, the term “technological innovation” encompasses very different realities depending on the sectors. Generative artificial intelligence, unified data architectures, and European regulatory constraints are reshaping companies’ investment priorities. Three structural axes stand out for their maturity and their concrete impact on organizations.
Agentic AI: autonomous agents replacing chatbots in businesses
Most articles on technology trends for 2024 focus on generative AI and language models. However, the most concrete shift lies a step further, in what KPMG identifies as agentic AI in its Global Tech Report.
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The principle: instead of a single chatbot responding to queries, multiple specialized agents collaborate to execute a complete workflow. Claims processing in insurance, automatic invoice reminders, onboarding management for new employees – these processes are orchestrated by autonomous agents with human supervision afterward, not in real-time.
Following the news on Info Tech allows one to gauge how much these multi-agent architectures are gaining ground in the information systems of large French and European companies.
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This shift changes the very nature of artificial intelligence projects: moving from a centralized model (a single LLM queried by all departments) to a constellation of specialized agents that distribute tasks. IT teams must rethink governance, access rights, and the traceability of each automated decision.
Convergence of big data and generative AI on unified platforms
Until now, most companies maintained two separate environments: on one side, analytical data pipelines (data lakes, data warehouses), and on the other, generative models deployed via APIs. This separation creates redundancies, data quality issues, and high infrastructure costs.
The underlying trend in 2024 is to merge analytical data and generative AI into a single platform. Language models access the company’s structured data directly, without an intermediate extraction layer. The result: more reliable responses, grounded in the actual information of the information system.
For technical departments, this convergence imposes several concrete trade-offs:
- Choosing between a sector-specific cloud platform (suitable for a specific profession) or a general solution that will need customization, with very different deployment timelines.
- Defining who has access to training data and how to trace the origin of each piece of information used by the model, a prerequisite for regulatory compliance.
- Measuring the real cost of inference (each query to the generative model consumes computing resources) and comparing it to the productivity gains measured in business processes.
Companies that delay this unification find themselves with incompatible data silos and generative models that “hallucinate” due to lack of access to the right internal sources.
European regulation and the carbon footprint of AI models
The regulatory angle is notably absent from traditional technology trend lists, even though it directly conditions architectural choices. European ESG and CSRD obligations are beginning to incorporate issues related to generative AI and big data.
Capgemini emphasizes that regulatory compliance related to AI is becoming a driver of technological investment, rather than just a legal risk to be managed downstream. Three requirements are emerging simultaneously:
- The traceability of training data: companies must document the origin of the datasets used to train or refine their models, under penalty of non-compliance.
- The measurement of the carbon footprint of models: each training cycle and each inference query generates energy consumption that non-financial reports will need to quantify.
- Ethical governance: systems for managing trust, risk, and AI security (what Gartner calls AI TRiSM) are moving from a recommendation status to a near-obligation for organizations operating in the European market.
This regulatory framework pushes digital departments to integrate legal and CSR teams from the design phase of AI projects, which lengthens development cycles but reduces the risk of having to redo everything after an audit.
Sustainable technologies and GreenTech: a criterion for technological selection
Sustainability is no longer a marketing argument. It is becoming a technical criterion for selecting cloud providers and software solutions. Tenders now include clauses on the energy consumption of data centers, server cooling, and the geographical location of infrastructures.
Sustainable technologies cover a broad spectrum: optimizing algorithms to reduce the number of calculations needed, choosing data centers powered by renewable energy, designing software that is less memory-intensive. For client companies, the challenge is to verify these commitments beyond commercial statements.
What is changing concretely for IT departments
IT departments must arbitrate between raw performance and digital sobriety. A lighter generative AI model, trained on job-specific data, can produce results comparable to a massive model while consuming a fraction of the resources. Optimization replaces the race for model size as the technical priority in mature organizations.
Technological roadmaps for 2024 that ignore the regulatory and environmental dimension start with a structural handicap. The three trends described here (agentic AI, data-AI convergence, ESG constraints) do not operate in silos: it is their interaction that redefines the digital architectures of companies for the coming years.