The hidden dangers of AI: key risks every business should know

Generative AI is ubiquitous these days, and organizations are rapidly integrating GenAI into their business processes. However, building GenAI applications comes with its own set of specific challenges. The models are often large, meaning inference costs for running these models can quickly get out of hand, and model selection often requires balancing performance against costs and latency. Other common challenges include the misuse of generative models or data leakage. While implementing measures such as rate limiting, monitoring, and guardrailing in your GenAI applications can help overcome these problems, doing so for every individual project brings significant overhead for your engineering teams. It also becomes easy to lose track of global usage of generative AI within your organization and leads to many cases of reinventing the wheel as teams solve the same problems over and over again.

Remember J.A.R.V.I.S. from Iron Man? That intelligent assistant that seemed to have a solution for everything? While we’re not quite there yet, the rapid evolution of Large Language Models (LLMs) like GPT-4, Claude, and Gemini is bringing us closer than ever. Today’s LLMs are impressive. They can generate content, translate languages, and even write code. But let’s be real — they’re still pretty much glorified text processors.

Executive Summary The article explains how Artificial Intelligence (AI) is transforming procurement from a tactical, manual function into a strategic driver of value, innovation, and resilience. By leveraging technologies such as machine learning, natural language processing, and Generative AI, organizations can automate and optimize every stage of the procurement lifecycle—from intelligent sourcing and supplier risk management to contract analysis and the procure-to-pay (P2P) process. The rise of integrated “Agentic AI” systems enables end-to-end workflow automation, predictive risk detection, and data-driven decision-making, while maintaining a human-in-the-loop approach to ensure strategic oversight. Ultimately, AI empowers procurement teams to reduce costs, improve efficiency, and proactively manage risks, positioning the function as a key enabler of organizational competitiveness and agility.

Executive Summary ML6 went to the AI Engineer summit in Paris. We are confident that agents will remain a vital part of the industry, and MCP will be at the forefront of this trend. While MCP adoption is skyrocketing, its potential remains heavily underutilized. And while many agent projects are still explorative, those that find a suitable use case can radically transform their processes through iterative engineering.

The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.