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Human-in-the-Loop Isn't a Control If the Human Is Drowning
Every AI tool sold into a regulated firm ships with a human approval step. Few firms check whether thehuman can keep up. When they can't, the control exists only on paper.
I've written before about AI deployments that remove the human entirely. The more common failure inregulated firms is quieter. Last time the question was whether the human was there. This time it's whetherthey can do the job they're there for.
Hiring an AI Orchestrator to Fix a Spreadsheet Problem
Most businesses asking for agents need a process first. Some of them just need a spreadsheet.
AI Agent Orchestration Specialist. Agentic Workflow Engineer. n8n Expert Needed Urgently. I can't remember the last time I saw a posting for a prompt engineer. It's all agent orchestrators now, and next year it will be something else. The titles change. The pattern underneath them doesn't.
Read past the title and into the requirements, and a surprising number of these jobs describe something else entirely. Reconcile invoices. Chase overdue approvals. Pull three reports nobody reads into one report somebody might. Keep track of stock. Post to social media on a schedule.
None of that is an intelligence problem. Most of it is a management problem, and some of it is a spreadsheet problem. AI can help with both. But the job being advertised is rarely the job that needs doing.
Companies’ Priority in the AI Era: Evolving Quality Testing Practices and Data Traceability
The introduction of AI into the tech stack breaks traditional testing methodologies. You cannot test AI processes with confidence without specialized controls in place such as data traceability.
Check out the latest article in The European Financial Review, on how to integrate AI into the evolving business landscape, featuring David Carle, AI and Data Innovation partner at Capital Markets Advisors LLC, and partner at Nexus FrontierTech for North America.
In today’s rapidly evolving business landscape, many organisations remain anchored to an outdated mindset when implementing artificial intelligence, treating it like traditional software where inputs and outputs follow clear, predictable patterns. While familiar, this approach no longer serves modern AI systems’ complexities. While conventional software development allows for straightforward testing and validation—with developers confidently asserting, “We know what data went in and what results should come out,” generative AI (hereafter GenAI), such as ChatGPT, introduces new complexities and risks that demand a fundamentally different framework.