Use Case

The “Negative News” Phenomenon Have you noticed a darker tone in AI news lately? At System in Motion, we did. When our last newsletter landed with heavy focus on deepfake scandals and regulatory crackdowns, we questioned: were we selecting negative stories—or had media’s narrative fundamentally shifted? We tasked five AI models to analyze global coverage. The verdict? This isn’t just bias it’s a global reckoning. Media now frames AI through ethics, regulation, and measurable impact—demanding wiser adoption strategies from established businesses.

Artificial intelligence (AI) has rapidly transformed legal research, offering practitioners and organizations efficient access to case law, statutes, and legal analysis. However, the reliability, depth, and accuracy of AI-generated legal research remain critical concerns, particularly in specialized jurisdictions like Hong Kong, where common law principles intersect with local statutory frameworks.

In today’s rapidly evolving business landscape, AI is transforming how companies identify prospects, engage decision-makers, and drive growth. This post explores the effectiveness of various AI models in business development by testing their ability to generate high-potential leads, analyze customer profiles, and craft compelling cold outreach messages.

When it comes to evaluating the return on investment (ROI) of text models (aka Large Language Models), visual representations can be a game-changer. A well-structured graph can instantly highlight cost-efficiency trends, helping businesses make informed decisions. But which AI agent is best suited for this task?

Part 5: Find the best option - Asking Manus In Part 2, Part 3, and Part 4 we tested twelve tools to enhance one of our Brainies, the text model. We decided in Part 1 to take inspiration from Agatha Christie to give more personality to the mascot.

Part 4: Testing KlingAI, Dezgo, Midjourney, OpenArt, and Recraft In Part 3, we tested Fotor, PhotoEditor.ai, Consistory, Phot.ai, and GetImg to enhance our Brainies, but we did not improve the results from Part 2.