The technologies that structure the economy in 2026 are no longer just novelties. Artificial intelligence, cybersecurity, and digital sovereignty now function as power infrastructures that reconfigure value chains, business models, and strategic trade-offs for companies.
IT and physical security convergence: a field in full reconfiguration
Physical security (video surveillance, access control, intrusion detection) is shifting towards a software and data-driven model. Genetec documents this evolution for 2026: intelligent video analysis, decision automation, and IT/OT convergence are transforming a historically hardware-focused sector into a “software-defined” domain.
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This convergence means that IT teams and physical security teams now share the same data management platforms. Cameras no longer just record: they feed AI models that detect anomalies in real-time, cross-reference streams with other sensors, and trigger automated processes.
For companies, the change is tangible. Security managers must acquire network and cloud skills, while CIOs integrate physical security into their scope. Data centralization tools become the common foundation, far removed from the siloed systems that prevailed a few years ago. Organizations that regularly publish on these topics, such as the tech category on Bozar, allow for tracking these transformations over time.
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Digital sovereignty and AI ethics: structuring pillars in 2026
VivaTech 2026 positions “Sovereignty & Ethics” and “Cybersecurity & Defense” on par with artificial intelligence and Greentech among its major themes. This programming choice reflects a shift: technological sovereignty is no longer a conference topic; it is an operational decision criterion.
Controlling AI models raises specific questions. Where are the training data hosted? Who audits the biases of a foundation model used in health or care? What jurisdiction applies when a cloud provider operates from another continent?
- The choice of cloud provider determines the location of data and the applicable regulatory framework, which directly impacts compliance in sectors like health or finance.
- The auditability of AI models becomes a prerequisite: companies demand the ability to trace automated decisions, particularly for care or credit management processes.
- Training internal teams in algorithmic ethics is being structured, with dedicated roles (AI manager, ethics committee) that did not exist three years ago.
Digital sovereignty now conditions infrastructure choices, not just institutional discourse. Tender offers include clauses on data localization and algorithmic transparency that would have seemed anecdotal not long ago.
Agentic AI: systems that execute, not just respond
Agentic AI refers to systems capable of planning, deciding, and executing a sequence of actions without human intervention at each step. The difference from a classic chatbot is structural: where a conversational assistant responds to a request, an AI agent breaks down a goal into subtasks, mobilizes external tools, and adjusts its strategy based on intermediate results.
In business, these systems are beginning to be applied to complex processes. An agent can analyze a set of contractual documents, identify non-compliant clauses, draft a summary report, and send it to the legal department, all without an operator restarting each step.
The main challenge remains reliability. An agent that chains multiple actions amplifies errors: a misinterpretation at step two propagates to step five. Companies deploying these systems implement human checkpoints at critical stages, accepting semi-autonomous operation rather than total automation.
Quantum computing: where the sector really stands
Quantum computing remains at a pre-industrial stage, but research investments are progressing. The quantum field is particularly interested in cryptography, logistical optimization, and molecular simulation for the health sector.
The technology relies on qubits, units of information that exploit the properties of quantum superposition and entanglement. Unlike classical bits (0 or 1), a qubit can represent multiple states simultaneously, paving the way for parallel calculations on certain types of problems.
For companies, the short-term challenge is not to buy a quantum computer. Current use cases involve simulators and cloud environments provided by a few specialized players. The concrete priority is to identify business processes that would benefit from quantum computing power and to train teams capable of formulating these problems in the correct mathematical formalism.
- Post-quantum cryptography is undergoing standardization efforts to anticipate the day when a sufficiently powerful quantum computer could break current encryption protocols.
- Quantum molecular simulation could accelerate the discovery of new molecules in health and materials.
- Combinatorial optimization (fleet management, supply chains) is among the most studied use cases by companies testing quantum environments.
The deeptech market, of which quantum is a segment, continues to attract increasing funding according to data compiled by Fortune Business Insights. Quantum computing remains a preparation technology, not yet production, but decisions made today regarding training and data architecture will condition organizations’ ability to leverage this power when the time comes.