AI is dominating the business world, and companies are seeking the best solutions for adoption. A big question is, how can I strike a balance between innovation and control? Leaders want to integrate technology for its ability to promote growth, but must ensure it follows governance best practices.

Agentic AI is particularly problematic. It operates autonomously, making decisions and taking actions without human intervention. Although its power is impressive, it can get out of control without the proper oversight.

CIOs must take a careful approach to ensure they make the most of agentic AI while aligning with compliance guidelines.

What Does Agentic AI Do?

Agentic AI uses sophisticated reasoning and iterative planning to solve complex problems. For example, when used in customer service, it extends beyond simply answering questions. It can recommend products and services and provide balance information using multiple data sources and third-party applications.

Here are some other use cases:

• Content Creation: The technology enables the creation of personalized content that resonates with consumers.
• Software Engineering: Agentic AI can handle repetitive coding tasks, allowing developers to focus on more pressing tasks.
• Healthcare: The technology can analyze vast amounts of data to help patients and doctors make more informed decisions about their healthcare.
• Video Analytics: Agentic AI can scan through visual archives to create alerts, draft incident reports, and enhance quality control.

What are the Risks of Agentic AI?

Although agentic AI offers several advantages, it also presents its share of risks, including the following:

• Biases: Agentic AI collects its information from the internet, which is full of biases. For example, when gathering and assessing financial information, it may over-scrutinize specific customers based on demographics. Biases can also change outcomes in medical and research assessments.
• Transparency and Explainability: The technology provides insight and solves problems, but it doesn’t always reveal where it got its information. Companies that share this information may need to do independent research to back their sources. Otherwise, they lose credibility in their industry.
• Data Privacy and Security: AI scans systems that may contain sensitive information. In doing so, it must comply with data protection regulations. Failure to comply can lead to reputational damage, fines, and penalties for the companies operating these systems.
• Accountability: The decisions made by agentic AI ultimately reflect on the company. Errors can lead to legal and reputational risks.

Ensuring Compliance in Agentic AI

Companies can overcome the risks of agentic AI by implementing the following practices:

• Ethical AI: Organizations can remain compliant by considering ethics at every stage of the AI lifecycle, including data collection, model training, deployment, monitoring, and innovation. Their roadmap must prioritize transparency, fairness, and security.
• Collaborative Approach: A collaborative approach is recommended. Organizations should bring in stakeholders, industry experts, and legal and ethical regulators to ensure their systems remain compliant. Teams can determine the best ways to implement AI fairly, ensure accountability, and maintain compliance.
• Implement Robust Governance Frameworks: Organizations must develop frameworks that adhere to defined guidelines and incorporate effective accountability mechanisms. Frameworks should be reviewed regularly to ensure ongoing compliance in an evolving technological landscape.
• Develop Regulatory Sandboxes: These frameworks enable companies to test new products, services, and business models in a controlled environment before launching them. They allow teams to explore new avenues, encouraging innovation without risk. Once implemented, companies will gain a better understanding of the implications, limits, and possibilities.
• Bias Mitigation: AI models should be designed to promote fairness and minimize biases. This approach typically requires presenting the technology with diverse datasets during training. Techniques such as re-weighting, re-sampling, and enabling explainable AI frameworks are often incorporated.
• Continuous Monitoring and Auditing: Teams must continuously monitor and audit systems to ensure they are producing reliable, unbiased insight and secure processes. Human oversight is necessary.
• Data Privacy and Security: Organizations must ensure data privacy and security by using encryption, access control, and other security measures. They must ensure systems comply with the General Data Protection Regulation (GDPR) and other guidelines that may vary by location and industry.
• Transparency and Explainability: Companies can enhance transparency when explaining AI information by utilizing compliance teams to decipher complex AI models. For example, some teams ensure they use well-thought-out prompts that lead to more reliable output.

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