About this ebook
Business leaders want to harness the efficiency gains AI promises and invest heavily to transform raw AI ideas like "Chat with Your Document" into trusted business tools that improve team performance. But realising AI's promised gains and maximising your ROI demands speed. Your competitors are already deploying AI tools, and every month of delay means falling further behind.
However, Moving fast is risky because AI behaves unpredictably. Unlike traditional software that follows strict rules, AI is non-deterministic—meaning it doesn't always give the same answer to the same question. This inherent inconsistency means you can't predict outputs with certainty, and the output quality often degrades over time as the model drifts further out of alignment with your policies. To realise ROI you need to deploy and correct quickly, but you're blocked by three momentum killing obstacles:
1. The Risk Obstacle hits you two ways. Resilience Risk—catastrophic technical or operational failures that threaten the CIA of your data. And Trust Risk, the silent killer, where your AI works technically but produces subtle hallucinations and half-correct interpretations that go unnoticed until clients start complaining and adoption dies.
2. The Regulatory Obstacle buries you in walls of regulatory text and vague terms. Nobody's quite sure what [Art. 25(1)] "substantial modification" means, so you end up talking about compliance rather than realising ROI.
3. The Ethical Obstacle is mandatory, even if you are not subject to the AI Act. Ignore Job Preservation and workforce impact, and you'll face internal resistance that kills adoption before it begins.
The AI Onboarding Capability and its supporting GitHub repo is designed to maximise business value by deriving speed. It brings GRC to life in an AI GRC production line that addresses the three biggest threats:
1. For the Risk Obstacle, AI informs business decisions and influences client responses. As such, the capability regards all AI systems as high risk, not because the AI Act says so, but because they could cause reputational damage. It voluntarily wraps both Provider [Art. 16] and Deployer [Art. 26] obligations in a json Risk and Control Framework with pre-validated Resilience and Trust risks. Engineers get the highest risk standards upfront, so they can start building immediately instead of waiting weeks for security to tell them what to do.
2. For the Regulatory Obstacle, the production line links your controls straight to the AI Act and ISO requirements. When auditors show up, the evidence is already there—no more pre-adult scramble for evidence.
3. For the Ethical Obstacle, the capability bakes a Fundamental rights, Environmental sustainability and a "Job preservation" assessment into the production line. Guaranteeing your AI enhances what your team can do rather than replacing who they are.
The AI GRC production line is powered by the Define–Build–Test–Comply cadence that translates vague compliance into concrete requirements, where Job Preservation and Human Oversight are technically enforced, using Golden Datasets to catch hallucinations, and auto-linking evidence to AI Act and ISO requirements.
Supporting the cadence is a ready-to-adopt approach. When companies onboard AI they make different architectural and governance choices. Instead of trying to accommodate every choice and becoming less detailed in the process, the capability chooses one, like “Implement intended use with white and blacklist prompts”. If you agree with the choice, you inherit controls and tests to implement it. If not, simply unplug the choice from the json framework, adapt it and plug it back in.
The AI Onboarding Capability and its supporting GitHub repo turns GRC into a competitive advantage, delivering speed without compromising safety.
