2025 is the year where AI hype will run into reality. Companies will need to show that their pilot projects can be transformed into profitable enterprise-scale applications. With this in mind, we offer five predictions for the AI market:
- Companies will need to graduate from running LLM pilots to deploying LLM-based applications in production. To do so successfully, they will need AI Observability tools to help them to guarantee model accuracy, prevent hallucinations, and ensure availability and speed.
- While LLM-based models will capture all the attention, companies that employ effective governance and observability for machine-learning based models will produce the lion’s share of AI value created in 2025.
- As companies move LLM models from pilot projects to fully deployed production systems, they will need AI Observability tools that can address not just model quality issues (like model drift, data quality, and hallucination identification) but also can manage the health and performance of the underlying infrastructure systems that decide the performance and availability of those models.
- AI Observability tools that can deliver rapid, real time identification of anomalies and root cause analysis across both models and underlying infrastructure systems will be essential to an organization’s ability to capitalize on the promise of enterprise-scale AI.
- Model data quality will emerge as an important element of model performance. The ability to ensure the overall quality of the data feeding LLM and ML models will be critical to their successful enterprise-scale deployment and operation.
To hear more, listen to InsightFinder AI’s CEO Helen Gu’s recent conversation with The Bloor Group’s Eric Kavanagh and their discussion of Predictions for the AI Era.
Or, read the white paper to learn more about how InsightFinder’s AI Observability can help turn AI pilots into AI performance.
