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SnapLogic CTO Jeremiah Stone on overcoming AI adoption barriers

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Content provided by Walter Thompson. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Walter Thompson 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://ro.player.fm/legal.

Everyone's looking for ways to implement AI solutions, but integrating them into existing systems means entering uncharted territory.

Despite the hype cycle, a majority of enterprise customers “are spending 70-80% of their capacity maintaining legacy application infrastructure,” said Jeremiah Stone, CTO of SnapLogic.

As a result, these companies often lack the necessary talent to effectively assess or integrate AI, making compliance and security the two biggest challenges for AI-first founders.

To overcome these and other adoption barriers, early-stage CEOs need to become educators and great listeners before they can ever act as salespeople.

In this episode, I interviewed Jeremiah to learn more about identifying and overcoming barriers to AI adoption, strategies for effective customer engagement and the importance of transparency and iterative experimentation in AI development.

Episode summary
  • Security and compliance “are the two things that are challenging for adoption right now.”
  • Before selling AI solutions, founders need to listen to (and educate) potential customers.
  • “Enterprises are spending 70-80% of their capacity maintaining legacy application infrastructure.”
  • The risk of data breaches and bad customer outcomes makes enterprises cautious.
  • Focus on solving unique problems AI can address better than existing solutions.
  • Build trust by starting discussions with security, compliance, and risk frameworks.
  • Innovate openly to demystify AI for customers.
  • Make sure your team is ready to answer typical security and compliance questions.
  • Maintain discipline in focusing on scalable solutions rather than niche problems.
  • “You have to create your own talent and upskill your data engineering team.”
  • “Experiment weekly on the things that look promising and treat it as an iterative process.”
  • Be transparent and involve customers early in your development process.
Links

Thanks for listening!

-- Walter.

  continue reading

36 episoade

Artwork
iconDistribuie
 
Manage episode 431255431 series 3533520
Content provided by Walter Thompson. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Walter Thompson 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://ro.player.fm/legal.

Everyone's looking for ways to implement AI solutions, but integrating them into existing systems means entering uncharted territory.

Despite the hype cycle, a majority of enterprise customers “are spending 70-80% of their capacity maintaining legacy application infrastructure,” said Jeremiah Stone, CTO of SnapLogic.

As a result, these companies often lack the necessary talent to effectively assess or integrate AI, making compliance and security the two biggest challenges for AI-first founders.

To overcome these and other adoption barriers, early-stage CEOs need to become educators and great listeners before they can ever act as salespeople.

In this episode, I interviewed Jeremiah to learn more about identifying and overcoming barriers to AI adoption, strategies for effective customer engagement and the importance of transparency and iterative experimentation in AI development.

Episode summary
  • Security and compliance “are the two things that are challenging for adoption right now.”
  • Before selling AI solutions, founders need to listen to (and educate) potential customers.
  • “Enterprises are spending 70-80% of their capacity maintaining legacy application infrastructure.”
  • The risk of data breaches and bad customer outcomes makes enterprises cautious.
  • Focus on solving unique problems AI can address better than existing solutions.
  • Build trust by starting discussions with security, compliance, and risk frameworks.
  • Innovate openly to demystify AI for customers.
  • Make sure your team is ready to answer typical security and compliance questions.
  • Maintain discipline in focusing on scalable solutions rather than niche problems.
  • “You have to create your own talent and upskill your data engineering team.”
  • “Experiment weekly on the things that look promising and treat it as an iterative process.”
  • Be transparent and involve customers early in your development process.
Links

Thanks for listening!

-- Walter.

  continue reading

36 episoade

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