Generative AI grows 17% in 2024, but data quality plummets: Key findings from Appen’s State of AI Report

A new report from AI knowledge supplier Appen reveals that firms are struggling to supply and handle the high-quality knowledge wanted to energy AI techniques as synthetic intelligence expands into enterprise operations.

Appen’s 2024 State of AI report, which surveyed over 500 U.S. IT decision-makers, reveals that generative AI adoption surged 17% previously 12 months; nonetheless, organizations now confront important hurdles in knowledge preparation and high quality assurance. The report reveals a ten% year-over-year improve in bottlenecks associated to sourcing, cleansing, and labeling knowledge, underscoring the complexities of constructing and sustaining efficient AI fashions.

Si Chen, Head of Technique at Appen, defined in an interview with VentureBeat: “As AI fashions sort out extra complicated and specialised issues, the info necessities additionally change,” she stated. “Corporations are discovering that simply having plenty of knowledge is not sufficient. To fine-tune a mannequin, knowledge must be extraordinarily high-quality, that means that it’s correct, various, correctly labelled, and tailor-made to the particular AI use case.”

Whereas the potential of AI continues to develop, the report identifies a number of key areas the place firms are encountering obstacles. Beneath are the highest 5 takeaways from Appen’s 2024 State of AI report:

1. Generative AI adoption is hovering — however so are knowledge challenges

The adoption of generative AI (GenAI) has grown by a formidable 17% in 2024, pushed by developments in massive language fashions (LLMs) that enable companies to automate duties throughout a variety of use circumstances. From IT operations to R&D, firms are leveraging GenAI to streamline inner processes and improve productiveness. Nonetheless, the speedy uptick in GenAI utilization has additionally launched new hurdles, significantly round knowledge administration.

“Generative AI outputs are extra various, unpredictable, and subjective, making it more durable to outline and measure success,” Chen advised VentureBeat. “To attain enterprise-ready AI, fashions have to be custom-made with high-quality knowledge tailor-made to particular use circumstances.”

Customized knowledge assortment has emerged as the first technique for sourcing coaching knowledge for GenAI fashions, reflecting a broader shift away from generic web-scraped knowledge in favor of tailor-made, dependable datasets.

2. Enterprise AI deployments and ROI are declining

Regardless of the thrill surrounding AI, the report discovered a worrying development: fewer AI initiatives are reaching deployment, and people who do are exhibiting much less ROI. Since 2021, the imply share of AI initiatives making it to deployment has dropped by 8.1%, whereas the imply share of deployed AI initiatives exhibiting significant ROI has decreased by 9.4%.

This decline is basically as a result of rising complexity of AI fashions. Easy use circumstances like picture recognition and speech automation at the moment are thought of mature applied sciences, however firms are shifting towards extra formidable AI initiatives, corresponding to generative AI, which require custom-made, high-quality knowledge and are far tougher to implement efficiently.

Chen defined, “Generative AI has extra superior capabilities in understanding, reasoning, and content material technology, however these applied sciences are inherently more difficult to implement.”

3. Information high quality is crucial — nevertheless it’s declining

The report highlights a crucial difficulty for AI growth: knowledge accuracy has dropped almost 9% since 2021. As AI fashions turn out to be extra subtle, the info they require has additionally turn out to be extra complicated, usually requiring specialised, high-quality annotations.

A staggering 86% of firms now retrain or replace their fashions not less than as soon as each quarter, underscoring the necessity for recent, related knowledge. But, because the frequency of updates will increase, making certain that this knowledge is correct and various turns into tougher. Corporations are turning to exterior knowledge suppliers to assist meet these calls for, with almost 90% of companies counting on exterior sources to coach and consider their fashions.

“Whereas we are able to’t predict the long run, our analysis reveals that managing knowledge high quality will proceed to be a serious problem for firms,” stated Chen. “With extra complicated generative AI fashions, sourcing, cleansing, and labeling knowledge have already turn out to be key bottlenecks.”

4. Information bottlenecks are worsening

Appen’s report reveals a ten% year-over-year improve in bottlenecks associated to sourcing, cleansing, and labeling knowledge. These bottlenecks are immediately impacting the flexibility of firms to efficiently deploy AI initiatives. As AI use circumstances turn out to be extra specialised, the problem of making ready the best knowledge turns into extra acute.

“Information preparation points have intensified,” stated Chen. “The specialised nature of those fashions calls for new, tailor-made datasets.”

To handle these issues, firms are specializing in long-term methods that emphasize knowledge accuracy, consistency, and variety. Many are additionally looking for strategic partnerships with knowledge suppliers to assist navigate the complexities of the AI knowledge lifecycle.

5. Human-in-the-Loop is Extra Important Than Ever

Whereas AI know-how continues to evolve, human involvement stays indispensable. The report discovered that 80% of respondents emphasised the significance of human-in-the-loop machine studying, a course of the place human experience is used to information and enhance AI fashions.

“Human involvement stays important for creating high-performing, moral, and contextually related AI techniques,” stated Chen.

Human consultants are significantly necessary for making certain bias mitigation and moral AI growth. By offering domain-specific data and figuring out potential biases in AI outputs, they assist refine fashions and align them with real-world behaviors and values. That is particularly crucial for generative AI, the place outputs could be unpredictable and require cautious oversight to forestall dangerous or biased outcomes.

Take a look at Appen’s full 2024 State of AI report proper right here.