Chatbots and AI-agents have become widespread in customer service and in applications like knowledge management, recommender systems, and help desks. Businesses increasingly want to benefit from the capabilities of large language models like OpenAI's GPT-4 and applications powered by such models. Nevertheless, the use of generative AI by companies has been seriously slowed down by concerns about data protection and by the fact that generative AI is known to sometimes make things up - create "hallucinations" as it is often called. Even if an answer does not contain hallucinated information, it may still suffer from incompleteness or misleadingly connected pieces of information. However, companies that want to use AI-agents in non-trivial circumstances need to be able to control them, in particular in customer-facing applications. It would be very unfortunate if it misinforms customers about the company's products or prices. It should also stick very closely to the intended marketing messages. While there is a lot of discussion about "safe AI", "reliable AI", "trustworthy AI", "explainable AI" (XAI) etc., the question of "controllable AI" is rarely discussed. However, as stated above, it is very often crucial that enterprises cannot just rely on, but are in fact able to control an AI system (more precisely, exercise control at design time how the system will behave at runtime).

New compliance technology such as AI and intelligent tagging has the power to change compliance. Our webinar which brought together in-house experts and external subject matter specialists has shed light on the latest cutting-edge technologies and how they can help solve the many day-to-day challenges faced by compliance professionals across the globe. In today's rapidly changing regulatory landscape, it is critically important for banks and financial institutions to respond to new regulations with agility, while ensuring that the customer experience does not suffer. These dual demands put pressure on compliance departments. Compliance technology in the form of end-to-end controls capable of mitigating a multitude of financial crime risks can help to alleviate this pressure.

A few weeks ago, Jochen Leidner, Director of Research at Thomson Reuters, discussed and dissected artificial intelligence (AI). Leidner offered some case studies, conducted by his team, to demonstrate successful ways of building AI products. However, before getting into specifics, Leidner gave a very brief explanation of what AI is and what it does. "It automates things that we deemed, maybe, not automatable a couple of years ago – if it works, or to an extent." Dr. Leidner, who is also the Royal Academy of Engineering Visiting Professor of Data Analytics at the University of Sheffield, made the point very early on that AI is a tool to be utilized with other forms of intelligence.