The next significant development in artificial intelligence (AI), referred to as “agentic AI,” represents a crucial evolutionary step for technology within the finance sector and beyond.
According to Saifr, these artificial intelligence systems are distinguished by their ability to act autonomously and make independent decisions in dynamic environments. Unlike robotic process automations which perform repetitive tasks, agentic AI understands and decides the necessity of actions before performing them, continually learning from its experiences.
McKinsey & Company provides an insightful explanation of this transition, describing artificil intelligence’s shift from conceptualisation to execution. This evolution involves AI utilising advanced techniques like machine learning, large language models, and natural language processing to perform complex decision-making autonomously, thereby streamlining a wide range of tasks and operations.
Such capabilities are paving the way for virtual assistants to evolve into highly efficient aids capable of managing intricate tasks such as planning travel itineraries across multiple platforms.
In the financial services industry, agentic AI’s potential is particularly significant. It can analyse vast quantities of data to tailor financial advice, investment strategies, and savings plans to current market conditions and individual preferences.
This continual adaptation could lead to substantial efficiency improvements within the industry. However, while the deployment of fully autonomous systems may not be immediate, several practical applications of agentic AI are expected to emerge shortly.
These applications include automating complex regulatory reporting, aiding risk and compliance assessments, managing due diligence workflows, and enhancing client services. Such advancements promise not only operational efficiency but also a higher degree of personalisation in customer interactions.
However, the integration of agentic AI comes with its challenges, particularly in highly regulated sectors like finance. Ethical and regulatory concerns must be addressed, ensuring that AI systems operate under strict human oversight to avoid biases and uphold ethical standards.
Furthermore, the success of agentic AI depends significantly on the availability and quality of data. Financial leaders must ensure robust data governance to facilitate the effective operation of AI systems.
To prepare for the advent of agentic AI, organisations should focus on documenting business processes and workflows to identify operational gaps and opportunities for AI integration. Additionally, organising data for AI use and establishing internal guidelines for AI training and testing are crucial steps towards harnessing the potential of agentic AI to transform service delivery, operations, and compliance in the financial services sector.
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