
Welcome to the Parallaxis Blog
Explore our latest thoughts on all things related to AI, Machine Learning, and the importance of data .
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5 Key Indicators That AI Isn't The Answer
This post outlines five key considerations for deciding whether to pursue AI: the existence of clear rules, data volume and variability, significant regulatory requirements, static environments, and unclear ROI. AI should be a multiplier for disciplined thinking and well-designed processes, not a replacement. Focusing on data quality and automation maturity should precede any AI adoption.

Compliance, Coffee, and Machine Learning
Large Language Models (LLMs) can transform compliance document review from a slow, error-prone and manual process into a scalable, efficient operation. Unlike traditional automation that relied on rigid keyword matching, LLMs understand context and nuance, and when trained on organization-specific examples—such as approved contracts and compliance templates—they can assess documents against internal governance standards.

The Illusion of Just Knowing
Introducing AI/ML in your business emphasizes the importance of data for understanding business operations and driving growth. Relying on assumptions hinders progress and creates an illusion of competence. The document advocates metrics, experimentation, and the scientific method to create a data-driven approach to doing business.

Ethics Is Not “One and Done”
Implementing ethics in Machine Learning is not a one-and-done effort. Organizations must build trustworthy systems that serve their customer’s best interests fairly. Ultimately, ethically designed machine learning models align with compliance and regulations while also avoiding harmful outcomes.

Your Starter Guide to Data Governance
Data governance establishes standards for data collection, storage, and analysis, ensuring accuracy and mitigating risks associated with regulatory non-compliance. Moreover, governance promotes ethical data practices, safeguarding individual privacy rights and societal norms.