Accelerating the new AI

Trends, challenges, and strategies for gen AI data annotation

The promise of generative AI is massive — so why are most projects stuck in pilot? This whitepaper explores the core data challenges slowing down enterprise gen AI initiatives, and provides five proven strategies to overcome them.

Backed by third-party research from McKinsey, Gartner, Forrester, Deloitte, KPMG, and MIT Technology Review, this report reveals what separates successful deployments from stalled experiments.

You’ll gain insights into how leading companies are building scalable data pipelines, training domain-specific models, managing risks, and leveraging human-in-the-loop annotation to meet rising expectations for quality, speed, and accuracy. Whether you’re a builder, buyer, or strategic partner in the AI ecosystem, this is your essential guide to advancing generative AI through better data.

What you’ll learn:

-> Why poor data quality is the #1 barrier to AI success
-> Five practical steps to improve your gen AI data pipeline
-> How to structure annotation teams, processes, and tools for scale
-> The shift from generalist to specialist annotators — and why it matters
-> How to address security, privacy, and ethical risks in your data strategy
-> What the rise of domain-specific models means for your next AI build

Don’t let data bottlenecks derail your AI roadmap.

Download the whitepaper and learn how to transform your annotation strategy into a competitive advantage.

Accelerating the new AI

Trends, challenges, and strategies for gen AI data annotation

The promise of generative AI is massive — so why are most projects stuck in pilot? This whitepaper explores the core data challenges slowing down enterprise gen AI initiatives, and provides five proven strategies to overcome them.

Backed by third-party research from McKinsey, Gartner, Forrester, Deloitte, KPMG, and MIT Technology Review, this report reveals what separates successful deployments from stalled experiments.

You’ll gain insights into how leading companies are building scalable data pipelines, training domain-specific models, managing risks, and leveraging human-in-the-loop annotation to meet rising expectations for quality, speed, and accuracy. Whether you’re a builder, buyer, or strategic partner in the AI ecosystem, this is your essential guide to advancing generative AI through better data.

What you’ll learn:

-> Why poor data quality is the #1 barrier to AI success
-> Five practical steps to improve your gen AI data pipeline
-> How to structure annotation teams, processes, and tools for scale
-> The shift from generalist to specialist annotators — and why it matters
-> How to address security, privacy, and ethical risks in your data strategy
-> What the rise of domain-specific models means for your next AI build

Don’t let data bottlenecks derail your AI roadmap.

Download the whitepaper and learn how to transform your annotation strategy into a competitive advantage.

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