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EPISODE · Sep 11, 2024 · 29 MIN

Collaborating to Create Your Enterprise AI Vision

from AI-volution: Redefining HR · host Powered by Mercer

In this episode, Mike Brennan, Partner & Digital HR & Technology Advisory Services Leader at Mercer, discusses the importance of cross-functional collaboration to create an enterprise AI vision. Learn why you need to jumpstart development of your AI vision and strategy, and why it's important to remain agile and adaptable to potential disruptors. Takeaways Cross-functional collaboration is crucial for creating an enterprise AI vision. Executive leadership, IT, HR, business stakeholders, legal and compliance, finance and procurement, and external constituents should be involved in the process. Strategic alignment with guiding principles and measures of success is important. Guiding principles may include data quality and management, integration with other systems, ethical and responsible use of AI, and changefulness. Success measures can vary widely and may include customer-related measures, innovation-related measures, operational efficiency measures, and measures related to the impact on the workforce and engagement. It is important to start developing an AI vision and strategy early and remain agile and adaptable to potential disruptors.

Episode metadata supplied by the publisher feed · Published Sep 11, 2024

What this episode covers

In this episode, Mike Brennan, Partner & Digital HR & Technology Advisory Services Leader at Mercer, discusses the importance of cross-functional collaboration to create an enterprise AI vision. Learn why you need to jumpstart development of your AI vision and strategy, and why it's important to remain agile and adaptable to potential disruptors. Takeaways Cross-functional collaboration is crucial for creating an enterprise AI vision. Executive leadership, IT, HR, business stakeholders, legal and compliance, finance and procurement, and external constituents should be involved in the process. Strategic alignment with guiding principles and measures of success is important. Guiding principles may include data quality and management, integration with other systems, ethical and responsible use of AI, and changefulness. Success measures can vary widely and may include customer-related measures, innovation-related measures, operational efficiency measures, and measures related to the impact on the workforce and engagement. It is important to start developing an AI vision and strategy early and remain agile and adaptable to potential disruptors.

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This episode was published on September 11, 2024.

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In this episode, Mike Brennan, Partner & Digital HR & Technology Advisory Services Leader at Mercer, discusses the importance of cross-functional collaboration to create an enterprise AI vision. Learn why you need to jumpstart development of your...

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