Conversational AI is increasingly central to enterprise applications, spurred by advancements in large language models (LLMs) and generative AI. These technologies enable more natural and open-ended interactions; however, they also bring new risks and regulatory demands that traditional governance approaches are ill-equipped to manage.
The heightened exposure of applications increases the risk of errors, biases, security breaches, and reputational damage. Despite this urgency, many enterprises lack a robust strategy and accountability mechanisms for governing conversational AI. Gartner forecasts that by 2028, 25% of enterprises will face significant disruptions and business losses due to insufficient governance and inadequate guardrail practices.
To effectively mitigate the risks associated with conversational AI, it is critical for enterprise application leaders to establish governance, assign accountability, and utilize proven frameworks to manage and escalate responsibilities for secure and compliant AI.
Building a Cross-Functional Governance Team for Conversational AI
Effective governance begins with forming a cross-functional team that has clearly defined accountability and decision-making authority. Given the unpredictable nature of conversational AI agents and the unique risks they pose, collaboration between business, IT, risk, and compliance teams is vital for establishing, enforcing, and monitoring acceptable AI behaviors.
Once team members are selected, it is essential to outline their mandate and authority, clearly delineating roles, responsibilities, and escalation processes related to conversational AI issues. Appointing a team leader to coordinate activities, along with setting a regular meeting schedule for reviewing new use cases, evaluating incidents, and updating guardrail strategies as risks and regulations evolve, is also crucial.
By establishing a dedicated governance team, enterprises can ensure their conversational AI initiatives align with business objectives, regulatory requirements, and enterprise risk tolerances.
Defining and Communicating Guardrail Policies for Conversational AI
To govern the interactions of conversational AI agents effectively, clear and accessible guardrail policies are necessary. Unlike traditional applications, these systems require frameworks that accommodate open-ended interactions, unpredictable user input, and evolving compliance standards.
According to Gartner, by 2027, 80% of large enterprises are expected to have well-defined and reusable governance and guardrail practices for their conversational AI agents and assistants. Enterprise application leaders must develop policy statements that delineate acceptable and unacceptable behaviors regarding security, privacy, business practices, legal priorities, ethical usage, and regulatory compliance.
These policies should be communicated broadly to internal teams, vendors, and partners involved in the design, deployment, and operation of conversational AI solutions. Ongoing training and timely updates are essential to ensure that all stakeholders comprehend policy expectations and can swiftly adapt to new business, technological, or regulatory changes.
Establishing Operational Responsibility Using an Adaptive Framework
Clear operational responsibility for each conversational AI guardrail is vital for ensuring accountability and enabling rapid responses to emerging issues. An adaptive framework allows enterprise application leaders to assign each guardrail or policy area to its operational owner, whether that is end-users, application administrators, or a dedicated risk team.
It is also imperative for leaders to define escalation procedures and ensure that all team members understand how to initiate escalations when needed. A structured approach to operational assignments and escalations guarantees that no guardrail is overlooked, accountability is maintained, and incidents are managed proficiently.
By adhering to these best practices, enterprise application leaders can position their organizations to meet regulatory requirements, manage evolving risks, and achieve favorable business outcomes with confidence.
The author is Bern Elliot, Distinguished VP Analyst at Gartner.
Disclaimer: The views expressed are solely those of the author, and ETCIO does not necessarily endorse them. ETCIO is not responsible for any direct or indirect damage caused to any person or organization.
This article aligns with the aims of the Making AI Work Summit & Awards, India’s most influential AI summit. It is not merely a discussion; it also recognizes pioneers, developers, and businesses showcased at the AI Summit who are effectively utilizing AI to create meaningful impact, demonstrating that the future of AI is being constructed today.