AI algorithms have become foundational in operational activities for many organizations. The broadened scope of AI and ML algorithms and their usage allows businesses to unlock operational efficiencies, strategy planning, and new boundaries for customer experience. While there is so much about using these algorithms, ineffective management can lead to operational, financial, and regulatory risks.
Customer interactions with chatbots, fraud detection, or patient symptom checker with AI still involve human supervision to resolve possible biases and refine the algorithm further. Organizations need AI Assurance services beyond simple algorithmic control assessments.
Human supervision is still crucial in assessing the readiness for deploying AI systems.
Significant time and effort in establishing deployment-ready models.
Lack of trust and questions about performance for Unsupervised AI models.
Expensive data science talent required to maintain Supervised AI models.
AI models age faster than traditional software. Concept drift makes data models obsolete.
We at Saxon apply a holistic assurance approach to assess the elements in AI algorithms’ lifecycle, validating the complex AI ecosystem. How do we address this? Our experts will initiate with the algorithm validation and enhance trust with our custom AI Assurance framework.
Will the current AI system design match according to the output from the system?
Assessing the confidence in the data quality and integration methods used for training the AI models.
Defining and articulating the output of each AI system.
Interpreting the performance metrics to the AI algorithms and design output.
Interaction of AI systems with the market consistently, planned vs. deviations.
As we continue to leverage AI for internal business operations, the quality of AI systems will be a key differentiator in building a data-driven enterprise. Our experts at Saxon can help you with the AI assurance framework alongside algorithm validation for consistent performance and adaption to dynamic scenarios.