As of today, there are more than 100 AI policies in place around the world, composed by governments, Knowledge institutions, committees, and NGOs. Each of them intends to suggest a constructive societal framework, with regulatory guidelines for AI implementations, while trying to keep pace with the unstoppable AI and ML revolutionized tools and applications, sometimes without the core competences to understand the subject matter in depth. In Parallel, the AI Giants are taking ownership of key risks of their tools, according to their judgments and interpretations, with a strong business/ profitability context. How can an organization that is willing to implement AI tools orient itself between the two? What are the legal, ethical, and business consequences of the lousy implementation of AI? Is there a sufficient workflow to ensure that? The challenges are enormous – from diversification to cope with universalism and pluralism through responsibility and accountability questions, to the potential practices to implement responsible AI within organizations. One of the key trajectories would be a new technology tooling that will bridge the gap between the legal/ legislative language and the embedded technology vulnerabilities within the AI/ ML tooling. Just as much as we are familiar with Privacy and Security policy maturity ( i.e, GDPR), there is a need to expand the validation of new domains (i.e EU AI Act), such as Transparency, Responsibility and accountability, and Justice and Fairness. These new toolings will complete the organization's readiness and auditing to adopt AI and ML technologies, and will ensure Responsible AI implementation throughout any business AI transformation.