AI is constantly evolving from doing what it is told to doing what it has been trained to do. We can see that leading AI models are moving to being used in multiple areas of public service and enterprise operations across healthcare, shipping, travel, defence, finance, and more domains.
Due to this growth, there must be an answer to the question of who monitors these computer programs after installation.
Going from AI performance to AI behaviour
Normal software monitoring looks only at availability, latencies, errors, and efficiency. However, AI should take a wider approach. An AI system can be working properly, but the results of its work may not correspond to reality. This is especially relevant in the case of Agentic AI that is able to understand information, make decisions, and perform actions without a lot of supervision from people.
Tools of Assistant AI, such as VideoBots, VoiceBots, and ChatBots, can communicate with customers directly, and AI platforms can perform thousands of contacts without a human in the loop. The monitoring should not only check if the program is functioning, but also pay attention to what it is saying, how it behaves, in which way it makes decisions, and whether it is staying within limits.
Keeping a Check on AI in Actual Situation
The application of Artificial intelligence raises various challenges when it is applied in different environments. Domain- or commercial-specific models use technologies like LLMs and SLMs, which are custom-made for the specific needs of businesses.
For instance, it is not necessary for a general-purpose Chatbot to be monitored as carefully as a model made for banking clients. Various commercial uses of the model are Telephony AI, AI Agents for public service applications, and other applications in commercial organizations.
Accountable Human-Centric AI
The main aim of observability is not to create barriers to innovation but rather to make AI more responsible. Human-Centric means defining at what particular time the AIs can take independent decisions and at what moment humans need to act.
For AI to enable improvements in Ease of Living, monitoring should be about more than just technical parameters. Organizations must observe the accuracy of AI, the number of hallucinations, responses, data safety and its use, trustworthiness, and decision-making actions of agents.
AI is becoming more voice-first, thereby integrating into daily life and everyday processes, and hence observability will become part of trust.