Chief Digital Officer, Head AI Transformation Management Office in Media21 days ago
We started with a loose infrastructure (Community of Practice) across the enterprise bringing leaders who were AI-forward together every other month to address proactive and reactive governance issues. Out of it, we built the MVP tools: 1. An employee AI acceptable use policy 2. A process and list for Approved AI platforms/tools 3. Base priorities for the organization/a central list of AI uses being developed within the various businesses.
The model had to evolve as we dialed up use of AI in products, in day-to-day workflows and commercially for client use. We have since moved to an AI Transformation Management Office with two teams who are accountable for the primary governance of our AI usage:
1. People + Ops (including legal/contracting) responsible for reviewing contracts, building employee and client FAQs, reviewing new use cases and helping to respond to new challenges, reviewing tech and security issues on platforms and monitoring usage (authorized and unauthorized). This team is also building a pre-jump workshop for us to use when kicking off any net-new commercial campaign that leverages AI in more than 50% of it's outputs. The group includes inside and outside counsel from regions, our Security & technology team, Operations and financial leads from businesses.
2. Product + Positioning: This team helps us manage and monitor our reputation, provide regular positions and thought leadership around AI's impact on our business areas and improve the way we safely escort products to market.
This model has been more than 2 years in the making, but the governance needs have increased rapidly as user adoption and client demand increases. Highly recommend a more inclusive AI TMO rather than just a top-down governance approach as shared accountability is really important in this changing space.
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We started with a loose infrastructure (Community of Practice) across the enterprise bringing leaders who were AI-forward together every other month to address proactive and reactive governance issues. Out of it, we built the MVP tools: 1. An employee AI acceptable use policy 2. A process and list for Approved AI platforms/tools 3. Base priorities for the organization/a central list of AI uses being developed within the various businesses.
The model had to evolve as we dialed up use of AI in products, in day-to-day workflows and commercially for client use. We have since moved to an AI Transformation Management Office with two teams who are accountable for the primary governance of our AI usage:
1. People + Ops (including legal/contracting) responsible for reviewing contracts, building employee and client FAQs, reviewing new use cases and helping to respond to new challenges, reviewing tech and security issues on platforms and monitoring usage (authorized and unauthorized). This team is also building a pre-jump workshop for us to use when kicking off any net-new commercial campaign that leverages AI in more than 50% of it's outputs. The group includes inside and outside counsel from regions, our Security & technology team, Operations and financial leads from businesses.
2. Product + Positioning: This team helps us manage and monitor our reputation, provide regular positions and thought leadership around AI's impact on our business areas and improve the way we safely escort products to market.
This model has been more than 2 years in the making, but the governance needs have increased rapidly as user adoption and client demand increases. Highly recommend a more inclusive AI TMO rather than just a top-down governance approach as shared accountability is really important in this changing space.