AI Agents: Sectoral Uses, Opportunities, and Risks to Manage
Since 2023, AI agents have transitioned from experimental stages to operational uses across various sectors including finance, healthcare, industry, human resources, and public services. Capable of autonomous or semi-autonomous actions, these agents promise significant gains in productivity and performance.
However, this increased autonomy brings along legal, ethical, operational, and cybersecurity risks that necessitate a structured approach to their governance and management.
1. What is an AI Agent? Definition and Recent Evolution
AI agents refer to software systems with specific characteristics:
- They rely on an AI model pursuing a defined or undefined goal, without significant additional development or modification.
- They are accessible via a studio where users can edit their parameters.
- They are configured to automate complex tasks, make decisions, and execute actions without necessarily requiring human intervention.
AI agents embody the concept of agentivity, which is the capacity of a system to:
- Act autonomously,
- Initiate actions,
- Plan sequences,
- Adapt to changing contexts,
- Pursue high-level objectives without continuous human supervision.
For example, an AI agent can be an automated assistant tasked with sorting incoming emails. It analyzes each message, identifies its category (commercial, support, urgent), and applies the appropriate action, such as archiving or creating a ticket, thus fulfilling a precise, pre-defined role without exceeding its boundaries.
The emergence of frameworks like AutoGPT (2023) and LangGraph (2024), along with agents integrated into cloud suites (e.g., Microsoft Copilot, Google Agentspace), has accelerated the adoption of AI agents in real professional environments.
2. Uses of AI Agents by Sector
2.1 Finance and Insurance
The financial sector is among the first to integrate AI agents due to the increasing complexity of operations, rising data volumes, and multiplying compliance requirements.
Use Cases for AI Agents:
- Risk Analysis Agents: Continuously evaluate portfolios, detect anomalies, and adjust risk scores based on internal and external data.
- Compliance Agents: Provide ongoing transaction monitoring (AML/KYC), prioritize alerts, and prepare compliance documentation for human validation.
- Autonomous Algorithmic Trading: Some agents execute orders automatically according to predefined strategies based on market conditions and risk constraints.
Associated Risks and Specific Challenges:
- Lack of Decision Explainability: Decisions made or recommended by AI agents can be opaque, complicating compliance with regulatory requirements (auditability, traceability, justification).
- Bias and Indirect Discrimination: Underlying models may reproduce or amplify biases present in historical data, leading to unfair risk assessments for certain customer profiles.
- Legal and Financial Liability: In cases of financial loss, undetected fraudulent transactions, or erroneous decisions made by an autonomous agent, the question of human, organizational, or technological responsibility remains complex and requires a clear supervisory framework.
2.2 Healthcare and Life Sciences
The healthcare and life sciences sectors present significant potential for AI agent usage, given the complexity of medical data, pressure on healthcare systems, and increasing needs for clinical decision support.
These agents should be designed as assistance tools, without replacing healthcare professionals.
Use Cases for AI Agents:
- Diagnostic Assistance Agents: Analyze medical records, biological results, and imaging to identify clinical signals and suggest diagnostic pathways, often by comparison with similar patient cohorts.
- Care Coordination Agents: Automate appointment scheduling, tests, and follow-ups, contributing to smoother care management and optimizing hospital resource utilization.
- Clinical Research: In life sciences, AI agents explore scientific literature and clinical trial data to identify correlations, formulate hypotheses, and accelerate biomedical research.
Associated Risks and Specific Challenges:
- Protection of Health Data: AI agents handle sensitive medical information, increasing the risks of privacy violations in cases of security breaches, poor access governance, or uncontrolled data usage.
- Risk of Medical Errors: Misinterpretation of data, biases in models, or incomplete clinical information may lead to inaccurate recommendations, potentially impacting care quality and patient safety.
- Excessive Dependence on Algorithmic Recommendations: Increased reliance on AI agents can weaken clinical judgment if not properly managed, making the establishment of human supervision, explainability, and clearly defined responsibility essential.
2.3 Human Resources and Talent Management
Human resources functions are a prime application area for AI agents, especially in a context marked by growing applicant numbers, rapid skill evolution, and the need to better anticipate talent needs.
Use Cases for AI Agents:
- Candidate Pre-screening Agents: Analyze resumes, cover letters, and professional profiles to identify the best matches for defined criteria, prioritizing profiles for recruiters to review.
- Automated Onboarding Agents: Assist new employees during their integration by automating administrative steps, disseminating personalized content, and facilitating job commencement.
- Skills Management and Internal Mobility Agents: Cross-reference HR data, internal evaluations, and business needs to identify skill gaps, recommend training, and propose mobility opportunities.
Associated Risks and Specific Challenges:
- Risk of Indirect Discrimination: Models may replicate or amplify biases related to age, gender, or origin due to non-neutral historical data.
- Protection of Personal Data and Regulatory Compliance: AI agents handle sensitive data subject to GDPR, imposing high transparency, data minimization, and individual information requirements.
- Human Control of Decisions: Excessive automation may reduce human involvement in critical decisions like recruitment or career progression, necessitating clear mechanisms for supervision and accountability.
2.4 Industry, Supply Chain, and Logistics
The industry, supply chain, and logistics sectors present key application areas for AI agents, driven by the complexity of value chains, multiple stakeholders, and the need for continuous optimization of production and supply flows.
Use Cases for AI Agents:
- Predictive Maintenance Agents: Continuously analyze data from industrial sensors and maintenance histories to anticipate failures, schedule interventions, and reduce unplanned downtime.
- Supply Chain Optimization Agents: Cross-reference demand data, production capacities, stock levels, and logistical constraints to adjust flows, limit shortages, and reduce costs.
- Real-time Production Planning Agents: Adapt production schedules based on operational contingencies, demand variations, or external constraints.
Associated Risks and Specific Challenges:
- Cascade Effects from Automated Decisions: An erroneous configuration or decision can quickly propagate through the entire value chain, affecting production, stock levels, and deliveries.
- Dependence on External Data: The quality of AI agents’ decisions relies on sometimes incomplete or unreliable data, which can jeopardize operational judgments.
- Cyber Vulnerabilities in Industrial Systems: Integrating AI agents into critical industrial environments increases the attack surface and imposes heightened cybersecurity and access control requirements.
2.5 Public Sector and Citizen Services
The public sector and citizen services represent an expanding application field for AI agents, amid rising volumes of administrative requests, the pursuit of improved efficiency, and the need to ensure equitable access to public services while reinforcing fundamental rights.
Use Cases for AI Agents:
- User Guidance Agents: Assist citizens with their administrative processes by directing them to relevant services, automating certain responses, and facilitating access to public information.
- Administrative Decision Support Agents: Analyze complex files to prioritize their processing or formulate recommendations for public agents, particularly in social aid or resource allocation.
- Fraud Detection Agents: Cross-reference large volumes of administrative data to identify inconsistencies or atypical behaviors that may indicate fraudulent situations.
Associated Risks and Specific Challenges:
- Infringement of Fundamental Rights: Poorly framed automated decisions can affect access to rights, social benefits, or essential services.
- Opacity of Decision Criteria: The lack of clear explainability for algorithmic recommendations complicates their understanding by public agents and citizens alike.
- Insufficient Controllability of Decisions: The absence of appeal mechanisms and human oversight may limit users’ ability to contest automated decisions, making procedural guarantees essential.
3. Major Cross-Cutting Risks of AI Agents
Beyond sector-specific challenges, deploying AI agents raises cross-cutting risks common to all organizations. These risks involve legal, ethical, operational, and cybersecurity dimensions, calling for a comprehensive approach to AI governance.
3.1 Legal and Regulatory Risks
The increasing autonomy of AI agents exposes organizations to risks of non-compliance, particularly when these systems participate in decisions with legal or significant effects on individuals.
- Non-compliance with GDPR: Article 22 of the GDPR strictly governs fully automated decisions producing legal effects. Using AI agents without human control mechanisms, informing affected individuals, or providing recourse options can directly violate the European framework.
- Exposure to Emerging Regulations: The gradual implementation of the European AI Act and the adoption of AI framework laws in Asia (South Korea, Taiwan, Japan) impose new obligations regarding risk classification, transparency, and governance.
- Uncertain Legal Liability: In cases of damage caused by autonomous decisions, the distribution of responsibilities among the organization, human teams, technology providers, and the AI agent itself remains legally complex.
3.2 Ethical Risks
AI agents also pose critical ethical challenges related to their capacity to influence, recommend, or automate sensitive decisions.
- Algorithmic Bias: Agents may reproduce or amplify biases present in training data, resulting in indirect discrimination or inequitable treatment.
- Weakening of Human Autonomy: Excessive dependence on AI agents’ recommendations may reduce individuals’ ability to exercise critical judgment, especially in complex decision-making contexts.
- Lack of Transparency and Explainability: The opacity of certain models makes it difficult to understand decision-making logics, undermining user and stakeholder trust.
3.3 Operational and Cybersecurity Risks
From an operational perspective, AI agents introduce new vectors of technical and organizational risks.
- Misconfiguration or Misuse of Agents: Poorly configured or insufficiently controlled agents may yield erroneous decisions or be exploited for malicious purposes.
- Excessive Access to Internal Systems: AI agents often require extensive access to databases or critical systems, increasing the attack surface in case of compromise.
- Difficulties in Post-Hoc Auditing: The autonomous sequence of decisions and actions complicates the traceability and auditing of agents’ behaviors, particularly in incidents or disputes.
As AI agents gain prominence, effective AI management becomes a strategic lever for organizations.
Manage AI Agents with Confidence
AI agents are already transforming business processes. The question is no longer whether they should be used, but how to deploy, supervise, and govern them responsibly.
Explore solutions for better managing and supervising your AI agents today, and anticipate upcoming regulatory developments.
