Director of Solutions Consulting, India,
New Relic
Technology leaders understand the importance of AI, with the TCS AI for Business Study finding that more than half of its survey respondents are excited and optimistic about AI’s potential impact. While the importance is understood, and businesses are in the process of implementing it, only 20% of business leaders say they are well-positioned to leverage AI to their strategic advantage.
Observability is a key component to understanding and monitoring AI applications, and is crucial to ensuring organisations can instantly visualise their entire AI stack, from apps to the engines powering them.
Achieving cost efficiency
One of the biggest challenges to AI adoption is cost. One IDC study found that 70% of respondents across the Asia-Pacific region said generative AI was a dominant factor in driving up IT spending, due to issues such as the high cost of computing resources and addressing skill gaps in prompt engineering. AI monitoring solutions offer greater visibility into KPIs and the ability to determine success, which helps businesses understand the true cost of AI and enables them to implement cost-effective measures for optimising the AI toolchain.
By using observability to monitor AI, easy comparisons can also be made across different time periods. This allows businesses to see any changes in costs and how things change before and after deploying an AI model.
Dynamic resource allocation based on real-time performance metrics helps scale AI usage and makes it cost effective. This ensures that the necessary resources are tapped into, without incurring additional expenses during low-demand periods. Such comprehensive cost management empowers organisations to maximise ROI on AI initiatives.
Improve the performance of AI
With AI monitoring, businesses can see data specific to the AI-layer and help answer critical questions about AI app performance, such as: are end users waiting too long for a response? Is there a recent spike in token usage? Are there patterns of negative user feedback?
AI monitoring offers a broad spectrum of data about AI models — from performance, model inventory to comparisons. Businesses can use it to analyse the performance of AI models and AI apps. It visualises data on which models take the most time to process a request or create a response, and tracks longer request or response times. AI monitoring helps locate outliers across AI models, and determines whether a specific model produces more errors on average, or if one specific error occurs across all models. Using this information, businesses can determine where to make improvements in their AI models.
Detailed metrics and comparison capabilities of AI monitoring and intelligent observability provide valuable insights into the performance of different model versions, guiding data scientists towards optimal deployment choices.
Managing costs associated with AI applications can be challenging. By providing detailed insights into resource and token consumption, businesses can identify which areas to optimise. Shared observability platforms allow teams from different domains to effectively collaborate on AI initiatives. Real-time data insights from observability solutions empower a culture of continuous learning, ensuring continuous improvement to AI systems.
Adopting AI is a multifaceted journey, requiring technological investment and strategic foresight. Intelligent observability is a crucial enabler in this journey, offering real-time insights to deliver consistent performance, experiences, and efficiency at scale. By integrating observability into AI applications, businesses can become AI-ready, ensuring success and delivering a competitive advantage.