When we talk about AI adoption, most people mean efficiency. Making existing processes faster. With what has been borrowed against AI, efficiency does not come close to settling the account. The data centres behind AI run on staggering amounts of energy and water. Measured in what we were told AI would deliver. Cancer. Hunger. Not process cycle times. That is the only way AI ever delivers the goods.

We are now about to make the same mistake, at scale, with AI. The Same Mistake, Now Applied to AI New regulation for AI is arriving, and the predictable response is Most businesses will not build safety into their AI systems. That difference has always mattered, and with AI it becomes decisive. Every organization adopting AI now faces the same choice.

Forward Assurance for AI Systems Audit looks backward. It verifies that something was done. The gap AI is adopted when it can be trusted with the work that matters. professional standards — applying the methods, technical standards, and disciplines that govern how AI Three disciplines, working together → Engineering Methodology — how AI systems are engineered for the Who this is for Data, software, and IT solutions firms putting AI into client work.

Today many organizations use AI that is general. Curation of knowledge — including institutional knowledge — becomes an essential part of governing AI The medical profession has already faced the challenge of using AI where a wrong answer carries real a 2025 study in Bioengineering (Pingua et al.), trying to train medical knowledge directly into the AI Path forward A general AI cannot be trusted in a high-stakes domain because it is general.

With AI, we are skipping that step. We are not doing this with AI. A law that says AI must be safe is not an AI that is being kept safe, moment to moment, by something The AI version fails that test. For AI, it does not exist.

When regulators examine AI systems, they often focus on whether the software meets certain technical We lack the foundational work needed for proper AI governance. The Agentic AI Challenge Let's consider autonomous AI agents—systems that can set their own goals and The typical response is that AI can make decisions better and faster than humans. The European Union's AI Act represents significant progress toward this vision.

AI Assistants - Blessing or Curse? The rise of Generative AI has taken the world by storm, and AI assistants are popping up all over the However, for some, it is just an improvement in productivity, and they question whether the use of AI For those starting to use AI assistants, they are indeed a blessing, providing much-needed relief for The use of AI will be a threat for some but an opportunity for others.

Ethical Decision Making Involving AI In the domain of Corporate Ethics and Compliance, one hammer is this is important, it will not be enough to handle concerns associated with Artificial Intelligence (AI When decisions are made to proceed with a course of action, for example, to use or not use AI, in the We offer an online training course on, "Ethical Decision Making Involving AI." You will learn how to make ethical choices supporting responsible and safe AI along with your other compliance

on the page of AI history." AI, which Laqua approached from multiple angles, acknowledging that AI is being defined in real-time AI's Domain Diversity Laqua emphasized that no single domain captures the full scope of AI. of AI risk, but what's different with AI is the degree and scope of this uncertainty. The Call for AI Engineers The AI Engineering Body of Knowledge (AIENGBOK) The presentation culminated

When I read about AI in the news, and this includes social media, I am troubled by the way it is often The Need for AI Mythbusters These articles often don’t prove or demonstrate that AI is better, and neither The AI hype machine is definitely operating on all cylinders. Is that what we understand generative AI is - creative? Time for professionals to be AI Mythbusters!

As someone working in compliance during this wave of AI adoption, I've been thinking about how we approach Instead of implementing AI systems and then auditing their outputs, we might design systems that continuously A Different Relationship with AI What strikes me most about reflecting on Jidoka in the context of AI Instead of viewing AI as either fully autonomous or requiring constant supervision, Jidoka points toward For compliance professionals considering AI adoption, this perspective might be liberating.

Many organizations are adopting AI in their business, whether as generative AI or as AI agents. In all cases, the AI needs a context to work from, and for the AI to be effective that context must be An AI model knows nothing about your organization. AI workflows have no such reader. For each class of AI workflow, list what a correct output depends on.