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#221 Localization Is a Top ROI Use Case for GenAI with Lilt CEO Spence Green

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Manage episode 435066909 series 2975363
Content provided by Slator. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Slator 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://player.fm/legal.

Spence Green, CEO and Co-founder of LILT, the AI solution provider for enterprise translation, joins SlatorPod. On the podcast, Spence discusses the evolution of LILT's end-to-end platform, which integrates AI models with human verification to ensure quality standards.

The CEO notes a significant shift in enterprise approaches towards localization, now being more software-driven rather than service-driven, which has been influenced by the broader adoption and focus on AI technologies post-ChatGPT.

Spence emphasizes the importance of continuous training and customization of AI models to improve accuracy and efficiency in translation. He highlights how localization has emerged as an early winner to showcase the return on investment in AI.

Spence addresses the impact of AI on the translation industry, including the potential for linguist shortages due to low rates driven by machine translation post-editing. He predicts that the market will eventually adjust, but in the meantime, there is a need for higher-skilled linguists to manage the gap left by AI models.

The podcast concludes with insights into LILT's recent features, such as AI Analytics, which provide clients with deeper insights into the impact of AI on their localization processes. Spence also talks about the potential for multilingual content creation using AI, the challenges of segment-level interfaces, and the importance of workflow orchestration in localization.

  continue reading

Chapters

1. Intro (00:00:00)

2. LILT Progress Update (00:01:39)

3. Integration with LLM Technology (00:03:37)

4. Client Requirements and Market Changes (00:08:42)

5. Key Predictions Overview (00:11:08)

6. Decision-Making Dynamics (00:16:04)

7. Everyday Versus Enterprise Use Case (00:17:38)

8. Linguist Shortage (00:20:23)

9. Public Sector Use Cases (00:25:17)

10. LILT's AI Analytics (00:28:24)

11. Multilingual Content Creation From Scratch (00:31:36)

12. User Interface Challenges (00:34:18)

13. Workflow Orchestration (00:37:30)

14. Fundraising Environment (00:39:58)

15. Roadmap and Initiatives for 2024/2025 (00:43:09)

221 episodes

Artwork
iconShare
 
Manage episode 435066909 series 2975363
Content provided by Slator. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Slator 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://player.fm/legal.

Spence Green, CEO and Co-founder of LILT, the AI solution provider for enterprise translation, joins SlatorPod. On the podcast, Spence discusses the evolution of LILT's end-to-end platform, which integrates AI models with human verification to ensure quality standards.

The CEO notes a significant shift in enterprise approaches towards localization, now being more software-driven rather than service-driven, which has been influenced by the broader adoption and focus on AI technologies post-ChatGPT.

Spence emphasizes the importance of continuous training and customization of AI models to improve accuracy and efficiency in translation. He highlights how localization has emerged as an early winner to showcase the return on investment in AI.

Spence addresses the impact of AI on the translation industry, including the potential for linguist shortages due to low rates driven by machine translation post-editing. He predicts that the market will eventually adjust, but in the meantime, there is a need for higher-skilled linguists to manage the gap left by AI models.

The podcast concludes with insights into LILT's recent features, such as AI Analytics, which provide clients with deeper insights into the impact of AI on their localization processes. Spence also talks about the potential for multilingual content creation using AI, the challenges of segment-level interfaces, and the importance of workflow orchestration in localization.

  continue reading

Chapters

1. Intro (00:00:00)

2. LILT Progress Update (00:01:39)

3. Integration with LLM Technology (00:03:37)

4. Client Requirements and Market Changes (00:08:42)

5. Key Predictions Overview (00:11:08)

6. Decision-Making Dynamics (00:16:04)

7. Everyday Versus Enterprise Use Case (00:17:38)

8. Linguist Shortage (00:20:23)

9. Public Sector Use Cases (00:25:17)

10. LILT's AI Analytics (00:28:24)

11. Multilingual Content Creation From Scratch (00:31:36)

12. User Interface Challenges (00:34:18)

13. Workflow Orchestration (00:37:30)

14. Fundraising Environment (00:39:58)

15. Roadmap and Initiatives for 2024/2025 (00:43:09)

221 episodes

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