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Making AI Safe to Scale: A Framework for Responsible AI Adoption
Privacy, AI Heather Cayouette Privacy, AI Heather Cayouette

Making AI Safe to Scale: A Framework for Responsible AI Adoption

Not long ago, AI in most organizations meant a handful of people trying something new — testing a tool, seeing what it could do, treating it as a side experiment. In a few short years, there's been a dramatic shift from one-off experimentation to a push for whole-of-organization adoption.

What we're seeing is that AI adoption rarely happens in one neat, predictable way. There are varying levels of comfort, interest, and reluctance across individuals and teams — which makes sense. AI brings real opportunities for innovation and efficiency, but if it isn't brought in thoughtfully, those opportunities can just as quickly turn into threats.

However far along any one organization is, the direction is clear: AI isn't going away. Vendors are building it into the tools already in use. Staff are finding and adopting tools on their own, with or without formal approval. New capabilities keep arriving faster than most organizations can formally evaluate them.

The question I see in front of us now is: How do we take advantage of new technology while protecting accountability and the people we serve?

For me, the answer starts with trust.

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What Responsible AI Can Look Like in Practice
AI, Privacy Heather Cayouette AI, Privacy Heather Cayouette

What Responsible AI Can Look Like in Practice

AI is no longer something organizations are preparing for. It is already part of how work gets done.

Teams are using it to draft, analyze, and problem-solve, sometimes with clear approval, often without it, and not always with a shared understanding of how it should be used. This creates a quiet but important gap between intention and reality.

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