Navigating the Landscape of AI: Insights from the 2024 State of AI Report

As artificial intelligence (AI) adoption accelerates, organizations are encountering a myriad of challenges related to data management, model reliability, and scalability. The State of AI in 2024 report delves into the crucial factors driving AI development, highlighting trends such as the rise of generative AI, enterprise headwinds, the importance of data quality, and the growing demand for strategic partnerships to tackle the complexities of AI implementation.

Conducted in collaboration with The Harris Poll, Appen’s research surveyed over 500 U.S. information technology decision-makers (ITDMs) across various industries. The findings reveal several key takeaways shaping the future of AI.

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 Generative AI Adoption is Increasing

Generative AI is experiencing rapid growth in 2024, with adoption rates up 17 percentage points from last year. This surge is driven by advancements in natural language processing (NLP) and its increasing use in chatbots and automation tools. Companies are primarily adopting generative AI to boost productivity and efficiency in internal operations, IT operations

support is the second most common use, while its role in research and development has grown by 9 percentage points year-over-year.

Despite this momentum, significant challenges remain—particularly around managing bias and ensuring fairness in model training. Custom data collection has emerged as the primary method for sourcing training data, with its usage rising 7% since 2023.

 Fewer Enterprise Deployments & ROI Challenges

As companies become more selective with AI projects, fewer initiatives are reaching deployment. This trend likely stems from diminishing returns or the increased complexity of AI models. Many organizations are now shifting their focus from simpler AI use cases, like image recognition or automation, which have largely matured, to more complex problems requiring generative AI. These more advanced systems come with higher unpredictability and subjectivity in their outputs, making it harder to define and measure success. The mean percentage of deployed projects making it to deployment has dropped to 47.4% in 2024, reflecting the growing challenges organizations face in achieving successful deployments.

To achieve enterprise-ready AI, organizations must customize these models with tailored, high-quality data. Custom human-generated data, tailored to specific use cases, can improve AI model performance and reliability. By investing in expert-labeled training data and thorough evaluation processes, enterprises can ensure that AI models are aligned with real-world needs, enhancing their ability to achieve meaningful ROI.

 Data Quality: The Backbone of AI Success

Our research found that 86% of companies are retraining or updating their machine-learning models at least quarterly, underscoring the rapid development across the industry. To maintain accurate, up-to-date data and adapt to real-world changes, organizations need to build robust data pipelines with tight feedback loops between data collection and model development. Nearly 90% of respondents rely on external data providers for AI model training and evaluation, revealing the critical role third-party data plays in sustaining AI systems.

Custom datasets, especially in text, images, video, and audio, have become vital for AI success. Additionally, 80% of ITDMs emphasize the importance of human-in-the-loop machine learning, which involves human oversight in model training to ensure data accuracy and reduce bias.

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Persistent Data Challenges

As AI use cases become more specialized, data preparation problems have intensified, with a 10-percentage-point rise in bottlenecks related to sourcing, cleaning, and labeling data. A 7-percentage-point increase in data availability issues has also emerged, reflecting the increasing demands for custom data required for more advanced AI applications.

Despite the importance of high-quality data annotation in AI development, the average accuracy of data has dropped about 9 percentage points since 2021. This may be a result of the increasing complexity of AI systems and the corresponding data annotation requirements, which increasingly require specialized domain-specific knowledge and quality control across large datasets. To resolve these challenges, companies must prioritize long-term strategies that ensure high-quality, diverse, and consistent data, while forming partnerships with expert data providers to navigate the complexities of AI data lifecycles.

 

The Search for Strategic Data Partners

In response to these challenges, 93% of organizations are seeking strategic partners with expertise across the entire AI data lifecycle. Human expertise plays a key role in improving data quality, especially when dealing with complex or nuanced data. By combining automated data pipelines with human-in-the-loop processes, companies can ensure their AI models remain accurate, adaptable, and aligned with real-world needs. Companies are finding that consistency and accuracy in data annotations are crucial, with 95% of respondents emphasizing that choosing the right data partner is essential for successful model deployment. This trend underscores the importance of collaboration and specialized expertise in achieving successful AI implementation.

Conclusion

The journey to AI success is complex and multifaceted. From the need for high-quality human-in-the-loop data to the challenges of managing bias and ensuring fairness, organizations face numerous obstacles in their pursuit of reliable and effective AI systems. However, with the right approach and partnerships, these challenges can be overcome.

The findings from the State of AI 2024 report underscore the need for organizations to prioritize data quality and management. Companies should focus on building a robust data foundation that facilitates ongoing model training and refinement. Investing in diverse, accurate, and well-annotated data can help organizations enhance the effectiveness of their AI initiatives, paving the way for more successful deployments and increased ROI.

Methodology

The State of AI 2024 survey was conducted online by The Harris Poll, gathering insights from 509 U.S. IT decision-makers between April 18 and May 9, 2024. Check out the full report here.

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