Melisa
Tokmak
GM, Document AI at Scale AI
"Unlocking Trapped Data with AI"
"Unlocking Trapped Data with AI"
08 - 10:50 AM EDT/New York (See in local time)
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04//2024 10:50 AM
04//2024 11:10 AM
America/New_York
#WTGC2024
"Unlocking Trapped Data with AI"
#WTGC2024
"Unlocking Trapped Data with AI"
https://app.events.ringcentral.com/events/women-in-tech-global-conference-2024/reception
https://www.womentech.net/vfairs
"Unlocking Trapped Data with AI"
"Unlocking Trapped Data with AI"
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Session: Unlocking Trapped Data with AI
It is no secret that technology has so much potential to transform the way enterprises innovate, operate and serve customers but one of the areas that can benefit the most from AI/ML can be overlooked by organizations - document processing. Today, businesses have mass amounts of data stored in complex documents that require time-intensive manual labor to extract. These processes are inefficient and are not only wasting employees' time but the end consumer’s time - therefore losing organizations’ money.In this talk, Melisa Tokmak, GM of Document AI for Scale AI will outline how utilizing machine learning can unlock this trapped data and increase efficiency and accuracy while speeding up processing. Leveraging Scale’s Document AI business unit as an example, she will discuss the need for robust machine learning models vs. rule-based programs to better serve customers and how Scale AI has developed a service that understands document layout thanks to a mix of computer vision and NLP. She will also discuss the need for humans to stay in the loop at specific moments to ensure the highest accuracy of data extraction and specifically for highly regulated industries, like healthcare. She will conclude by giving examples of how automated document processing helped organizations in different industries to simplify transaction understanding, mortgage and lending processing, and patient onboarding, in a world where customers want everything faster.
Key Takeaways
- What are the biggest barriers to AI adoption and specifically for document processing
- Why there isn’t “one size fits all” for document processing
- Why humans need to be kept in the loop to ensure quality
Bio
Melisa Tokmak leads the Document Processing business unit at Scale that focuses on building fine-tuned ML models with human-in-the-loop annotation to automate the handling of documents at a high quality. Previously, she was the Chief of Staff for the company and started the Government business unit. Before Scale, Melisa built products for new product monetization at Facebook. She graduated from Stanford University with a degree in Computer Science and is from a small town in Turkey.