The use of agent-based AI is a double-edged sword for your business and carries certain risks

Artificial Intelligence (AI) has become a cornerstone of digital transformation for businesses. With the development of agent-based artificial intelligence systems, it is taking a new direction: these intelligent systems are capable of making autonomous decisions and managing very complex workflows.

Agent-based AI thus offers great potential for improving business operations by increasing efficiency, productivity, and overall value.

However, as with any technological revolution, the rapid rise of agent-based AI raises growing concerns among businesses and indeed carries certain risks.

In this article, discover the main business risks associated with the use of agent-based AI and how to prevent them.

Understanding Agent-Based AI

Agent-based AI is revolutionizing the field of artificial intelligence by introducing a groundbreaking concept: autonomy. Unlike traditional AI systems, AI agents do not simply execute predefined tasks.

  • They are capable of perceiving their environment, analyzing complex situations, making real-time decisions, and acting proactively to achieve their objectives.
  • This ability to act autonomously allows AI agents to operate in dynamic and uncertain environments, continuously adapting to new information and learning from their experiences.

Agent-based AI represents a new era of artificial intelligence, where machines are no longer mere tools but true autonomous agents capable of interacting with the world in an intelligent and flexible manner.

Read our article » What is Agent-Based AI?

What are the Main Business Risks of Agent-Based AI?

Despite its promises, agent-based AI raises legitimate concerns: what are the risks for companies that adopt it?

Operational Risks

Errors in judgment by AI agents can lead to costly malfunctions or inappropriate decisions, impacting productivity and service quality.

  • For example, in a supply chain management context, a poor interpretation of market data by an AI agent could lead to overstocking or stockouts, directly affecting financial performance.

Strategic Risks

Excessive reliance on AI can lead to a loss of control over the company’s strategic directions. The inherent biases in algorithms can sometimes distort analyses and forecasts, compromising long-term decision-making.

  • This situation can be particularly problematic in rapidly evolving sectors, where flexibility and human intuition remain essential for navigating uncertain environments.

💡 In 2018, a major British supermarket chain, Ocado, heavily invested in an AI system to manage its supply chain and predict consumer demand. The system, although effective under normal conditions, failed to anticipate abrupt changes in consumer behavior during the COVID-19 pandemic in 2020.

  • The AI, trained on historical data, continued to recommend supplies based on past trends, failing to account for the sudden demand for certain products (such as toilet paper or disinfectants) and the decline in demand for others. This rigidity led to massive stockouts of essential products and significant surpluses of others.

Ethical and Reputational Challenges

The use of agent-based AI can raise questions of transparency and fairness, particularly in interactions with customers or employees. Perceived unethical use can severely damage a company’s image.

  • Well-publicized cases of algorithmic discrimination or lack of transparency in automated decisions have already led to significant public backlashes for some companies.

💡 In 2018, Amazon had to abandon an AI recruitment tool after discovering it discriminated against female applicants. The algorithm, trained on predominantly male historical data, had learned to favor male profiles. This revelation not only forced Amazon to revise its recruitment process but also sparked a public debate on biases in AI, temporarily affecting the company’s reputation for equal opportunities.

Data Security and Confidentiality Risks

Agent-based AI systems, handling large volumes of sensitive data, become prime targets for cyberattacks, threatening the integrity and confidentiality of the company’s and stakeholders’ information.

  • The complexity of these systems can also create unexpected vulnerabilities, making data protection even more critical and complex.

💡 In 2024, Viamedis and Almerys, subcontractors managing third-party payments for many health insurance companies using AI for personalized patient care management, were victims of a sophisticated cyberattack. Hackers exploited a vulnerability in the interface between the AI system and the patient database. They managed to extract confidential medical information from millions of patients, including diagnoses, treatments, and personal data.

How to Reduce Business Risks Associated with Agent-Based AI?

Ensure Transparency of Agent-Based AI Systems

Transparency is crucial to establish trust in agent-based AI systems. It allows stakeholders to understand how decisions are made and identify potential biases.

The explainability of decisions is at the heart of this transparency. Companies must strive to develop AI models whose decision-making processes can be traced and explained in human-understandable terms.

Implementing rigorous documentation processes is essential. Every step of the development and deployment of agent-based AI must be meticulously documented, creating a verifiable history.

Clear communication with all stakeholders is indispensable. This involves regularly informing employees, customers, and partners about the functioning, capabilities, and limitations of the AI systems used.

Strengthen System Security

Implementing rigorous security protocols is crucial to protect agent-based AI systems from intrusions and cyberattacks.

This involves defining strict authentication and authorization procedures to limit system access, implementing measures to protect against malware and denial-of-service attacks, and encrypting sensitive data.

Regular security audits, continuous monitoring of suspicious activities, and cybersecurity training for staff are also essential. Adopting secure development practices, such as “security by design,” and regularly updating systems are crucial to maintaining a high level of protection against evolving threats.

Clearly Define Roles and Responsibilities

A precise definition of roles between humans and AI agents is fundamental for the successful integration of agent-based AI.

The distribution of tasks must be carefully studied. It is necessary to determine which activities can be entrusted to AI agents and which require human supervision or intervention.

Establishing clear chains of responsibility is crucial. It is important to define who is responsible for decisions and actions taken by AI systems, especially in case of problems.

Training and awareness of teams are essential. Employees must understand how to interact with AI agents, their limitations, and how to intervene if necessary.

Assess the Real Capabilities of AI Agents

A rigorous assessment of the capabilities of AI agents is necessary to ensure their reliability and effectiveness.

Implementing rigorous tests is paramount. These tests must cover a wide range of scenarios, including exceptional or critical situations.

Defining clear performance metrics allows for objectively measuring the effectiveness of AI agents. These metrics must be aligned with the company’s business objectives.

Balance Decision-Making Autonomy

Finding the right balance between the autonomy of AI agents and human control is a major challenge.

Defining clear limits for the autonomy of agents is crucial. It is necessary to determine precisely in which areas and to what extent AI agents can act autonomously.

Implementing human control mechanisms is necessary. This can include human validation points for critical decisions or alert systems in case of unexpected behaviors.

Adapting the level of autonomy according to contexts is important. The autonomy granted to AI agents can vary depending on the criticality of the task or the level of established trust.

“Rather than relying exclusively on agent-based AI, companies should consider hybrid solutions where critical decisions are validated by humans. This approach reduces the risk of total technological failure and allows for better management of unforeseen situations.” - Matthew Thompson, CEO of Agentia.

Define Clear Ethical Principles

Agent-based AI raises important ethical questions, as these systems are capable of making autonomous decisions that can significantly impact people’s lives. It is therefore essential to develop clear ethical guidelines that will guide the development and use of these technologies.

These guidelines must be based on principles such as respect for privacy, non-discrimination, fairness, and accountability. They must also define clear processes for ethical decision-making, conflict resolution, and ethical risk management.

Train and Raise Awareness Among Employees About Agent-Based AI Risks

All employees who will interact with agent-based AI systems must be trained and made aware of the issues and risks associated with this technology.

This training should enable them to understand how AI agents work, the limitations of these systems, and the ethical principles that should guide their use.

It is also important to raise employees’ awareness of potential biases and discrimination risks in AI systems, and to provide them with the tools needed to identify and report these issues.

“Only 9% of companies believe they are ready to manage the risks associated with the use of generative AI in their organization. Additionally, only 17% of risk and compliance officers have formally raised awareness or trained their organization on the risks associated with the use of generative AI.” - Riskonnect

Conclusion: Proactive Management of Agent-Based AI Risks

Integrating agent-based AI into the business world represents an unprecedented opportunity for companies, but it comes with complex challenges that cannot be ignored.

  • Proactively managing the risks associated with this technology is essential to fully exploit its potential while preserving the integrity and reputation of the company.
  • By focusing on transparency, clearly defining roles, rigorously assessing capabilities, balancing decision-making autonomy, and adopting ethical strategies, companies can create an environment conducive to responsible and effective use of agent-based AI.

It is crucial to understand that implementing agent-based AI is not just a technical challenge, but a process that affects all aspects of the organization.

This holistic approach not only minimizes risks but also maximizes the benefits of this revolutionary technology.

As we move towards a future where agent-based AI will play an increasingly important role, the companies that successfully navigate this complex environment will be those that adopt a balanced, ethical, and responsible approach. Agent-based AI is not just a tool but a partner in the growth and innovation of businesses.

By approaching it with caution and vision, companies can pave the way for a new era of productivity, efficiency, and creativity.

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