Today, AI is perhaps the first thing that comes to mind when we think about modern-day innovations. Emerging at the forefront of this technology is agentic AI — a new frontier that has the potential to revolutionise how we interact with technology. Agentic AI refers to autonomous systems that can sense and act upon their environment to achieve specific goals.
Imagine having a group of highly intelligent robot helpers — AI agents — who communicate with each other to accomplish tasks for you. For instance, suppose you’re planning a birthday party. Agent 1 identifies the key tasks: finding a venue, sending invitations, ordering a cake, and buying decorations. Agent 2 finds a cool party location by researching nearby venues and comparing prices. Agent 3 sends fun invitations to your friends using your contact list. Agent 4 orders a chocolate cake by communicating with bakeries and selecting the best option. Agent 5 buys balloons, streamers, and party hats from online stores. Finally, Agent 1 consolidates the work, presenting you with a detailed plan.
In the business context, this approach translates to streamlining complex operations. Agentic AI is designed to enhance efficiency, often mimicking human intelligence in decision-making. However, the autonomy risks that come with it are something enterprises must consider carefully before deploying AI agents.
Although in its nascent stage, agentic AI can impact enterprise operations. These include unintended decisions, lack of explainability, data vulnerabilities, ethical concerns, and over-reliance on automation. Among these, three critical risks stand out for organisations to consider.
Data vulnerabilities pose a significant threat as agentic AI relies heavily on datasets for training and operation. Poor data governance or reliance on biased datasets can lead to skewed outcomes, while data breaches can result in financial and legal repercussions.
Another concern is that overly relying on automation can lead to operational risks, particularly during unforeseen events. For instance, automated supply chain decisions without human oversight might amplify disruptions caused by natural disasters or geopolitical crises.
Apart from these, human resources is another area of concern. Often perceived as a competitor to human workers, agentic AI can lead to diminished morale and reduced employee participation if its implementation isn’t handled sensitively.
Addressing these risks requires a comprehensive strategy with proactive approaches. Establishing governance frameworks is crucial for defining accountability and ensuring compliance with ethical and operational standards. Ensuring the quality and diversity of data used for AI training while continuously monitoring for biases or vulnerabilities can help mitigate skewed outcomes.
Finally, investing in human oversight ensures a balanced collaboration between humans and AI. By creating mechanisms for human intervention in critical processes, enterprises can maintain control, enhance accountability, and build confidence in AI systems.
The writer is founder & CEO, Findability Sciences
