AI Agents: Sectoral Uses, Opportunities, and Risks to Be Managed
Since 2023, AI agents have transitioned from experimental phases to operational uses across various sectors including finance, healthcare, industry, human resources, and public services. Capable of acting autonomously or semi-autonomously, these agents promise significant gains in productivity and performance. However, this increased autonomy introduces legal, ethical, operational, and cybersecurity risks, necessitating a structured approach to their governance and management.
1. What is an AI Agent? Definition and Recent Evolution
AI agents refer to software systems based on an AI model designed to pursue defined or undefined objectives. They can be accessed through a studio where users can edit parameters and are configured to automate complex tasks, make decisions, and execute actions without human intervention.
Key characteristics of AI agents include:
- Ability to act autonomously
- Initiate actions
- Plan sequences
- Adapt to changing contexts
- Pursue high-level objectives without continuous human supervision
For example, an AI agent may function as an automated assistant sorting incoming emails based on predefined criteria, executing tasks without exceeding its designated role.
The emergence of frameworks like AutoGPT (2023) and LangGraph (2024) has accelerated the adoption of AI agents in professional environments.
2. Uses of AI Agents by Sector
2.1 Finance and Insurance
The financial sector has integrated AI agents due to complex operations and increased data volumes. Use cases include:
- Risk analysis agents: Continuously assess portfolios and detect anomalies.
- Compliance agents: Monitor transactions and prepare compliance files for human validation.
- Autonomous algorithmic trading: Execute orders based on predefined strategies.
However, this raises governance challenges such as:
- Lack of decision explainability
- Bias and indirect discrimination
- Legal and financial liability
2.2 Healthcare and Life Sciences
AI agents offer potential in healthcare to support decision-making. Use cases include:
- Diagnostic support agents: Analyze medical records to suggest diagnostic paths.
- Care coordination agents: Automate scheduling of appointments and examinations.
- Clinical research: Explore scientific literature to identify correlations.
Challenges include:
- Protection of health data
- Risk of medical errors
- Excessive reliance on algorithmic recommendations
2.3 Human Resources and Talent Management
In HR, AI agents can streamline processes. Use cases include:
- Candidate pre-screening agents: Analyze CVs and prioritize profiles.
- Automated onboarding agents: Support new employees during integration.
- Skills management agents: Identify skills gaps and recommend training.
Risks involve:
- Risk of indirect discrimination
- Protection of personal data
- Human control over decisions
2.4 Industry, Supply Chain, and Logistics
AI agents are crucial in these sectors. Use cases include:
- Predictive maintenance agents: Anticipate failures and plan interventions.
- Supply chain optimization agents: Adjust flows based on demand and capacities.
- Real-time production planning agents: Adapt schedules based on disruptions.
Challenges include:
- Cascading effects of automated decisions
- Dependence on external data
- Cyber vulnerabilities
2.5 Public Sector and Citizen Services
AI agents can enhance public services. Use cases include:
- User orientation agents: Assist citizens with administrative procedures.
- Administrative decision-support agents: Analyze complex files for public officials.
- Social or tax fraud detection agents: Identify inconsistencies in administrative data.
Risks involve:
- Infringement of fundamental rights
- Opacity of decision criteria
- Insufficient contestability of decisions
3. Major Cross-Cutting Risks of AI Agents
Deployment of AI agents raises common risks across organizations, including:
3.1 Legal and Regulatory Risks
Autonomy of AI agents increases exposure to regulatory compliance risks:
- Non-compliance with GDPR
- Exposure to emerging regulations
- Uncertain legal liability
3.2 Ethical Risks
AI agents introduce ethical concerns, such as:
- Algorithmic bias: Reproducing biases from training data.
- Weakening of human autonomy
- Lack of transparency and explainability
3.3 Operational and Cybersecurity Risks
Operationally, AI agents introduce risks related to:
- Misconfiguration or misuse of agents
- Excessive access to internal systems
- Difficulty of ex post auditing
As AI agents become increasingly powerful, effective management is crucial. Organizations must focus on deploying, supervising, and governing them responsibly.
Naia supports organizations in AI management, from inventory to risk management and regulatory compliance. Explore solutions to oversee your AI agents while anticipating regulatory developments.
