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Making AI Your Next Competitive Edge - William McKnight- The Agentic Insider - Episode #13

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Content provided by Iridius.ai Inc. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Iridius.ai Inc or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

“Data is the lifeblood of AI agents.” Is your data ready for the future of AI? Phillip and Alistair welcome William McKnight, President of McKnight Consulting Group and a leading global influencer in big data, data centers, cloud, and analytics, on the podcast today. William shares his insights on preparing enterprises for AI adoption, the importance of data accessibility, and the critical role of thoughtful data architecture. He also discusses the evolving role of data scientists, the AI-driven transformation in various industries, and the significance of strong leadership in managing AI implementations. The episode concludes with practical advice on maximizing ROI from AI investments and preparing for the future landscape of AI agents.

Takeaways:

  • Develop a Strong Data Strategy: Ensure all your data is accessible, well-performing, and governed. Consider adopting a data mesh approach to balance centralization and decentralization.
  • Commit to AI Agents: Begin experimenting with AI agents now rather than waiting. Pick initial use cases that are simple yet impactful and scalable for future projects.
  • Emphasize Organizational Change Management: Successful AI implementation often requires significant organizational change and strong leadership. Be prepared to tackle change management as a critical component of AI projects.
  • Focus on Data Maturity: Assess your organization's data maturity by evaluating data performance, understanding, and governance. Implement an information management action plan to improve analytical capabilities and enterprise data architecture.
  • Embrace New Roles: Consider emerging roles like prompt engineer, AI ethics manager, AI explainer, and advanced data architects to support AI initiatives. Encourage upskilling and reskilling of current team members to fill these roles.
  • Explore Diverse Use Cases: Investigate and apply AI in diverse areas such as drug discovery, predictive maintenance, fraud detection, personalized marketing, and healthcare.
  • Prepare for Integration Challenges: Anticipate and address data integration challenges, especially in environments with multiple legacy systems.

Quote of the Show:

  • “Data is the lifeblood of AI agents.” - William McKnight

Links:

Ways to Tune In:

  continue reading

14 episodes

Artwork
iconShare
 
Manage episode 492371154 series 3657874
Content provided by Iridius.ai Inc. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Iridius.ai Inc or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

“Data is the lifeblood of AI agents.” Is your data ready for the future of AI? Phillip and Alistair welcome William McKnight, President of McKnight Consulting Group and a leading global influencer in big data, data centers, cloud, and analytics, on the podcast today. William shares his insights on preparing enterprises for AI adoption, the importance of data accessibility, and the critical role of thoughtful data architecture. He also discusses the evolving role of data scientists, the AI-driven transformation in various industries, and the significance of strong leadership in managing AI implementations. The episode concludes with practical advice on maximizing ROI from AI investments and preparing for the future landscape of AI agents.

Takeaways:

  • Develop a Strong Data Strategy: Ensure all your data is accessible, well-performing, and governed. Consider adopting a data mesh approach to balance centralization and decentralization.
  • Commit to AI Agents: Begin experimenting with AI agents now rather than waiting. Pick initial use cases that are simple yet impactful and scalable for future projects.
  • Emphasize Organizational Change Management: Successful AI implementation often requires significant organizational change and strong leadership. Be prepared to tackle change management as a critical component of AI projects.
  • Focus on Data Maturity: Assess your organization's data maturity by evaluating data performance, understanding, and governance. Implement an information management action plan to improve analytical capabilities and enterprise data architecture.
  • Embrace New Roles: Consider emerging roles like prompt engineer, AI ethics manager, AI explainer, and advanced data architects to support AI initiatives. Encourage upskilling and reskilling of current team members to fill these roles.
  • Explore Diverse Use Cases: Investigate and apply AI in diverse areas such as drug discovery, predictive maintenance, fraud detection, personalized marketing, and healthcare.
  • Prepare for Integration Challenges: Anticipate and address data integration challenges, especially in environments with multiple legacy systems.

Quote of the Show:

  • “Data is the lifeblood of AI agents.” - William McKnight

Links:

Ways to Tune In:

  continue reading

14 episodes

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