Despite a global enthusiasm for GenAI, a recent IDC study, sponsored by Qlik, a leader in data integration, analytics, and AI/ML solutions, highlights a significant gap between companies' ambitions and their actual preparedness for these technologies. While 89% of organizations have revised their data management strategy in response to the emergence of generative AI, only 26% have deployed GenAI solutions at scale, and only 12% consider their infrastructure suited for agentic AI workflows.
According to the report "The Global Impact of Artificial Intelligence on the Economy and Jobs: AI will Steer 3.5% of GDP in 2030" published by Qlik in August 2024, AI is expected to contribute $19.9 trillion to the global economy by 2030, representing 3.5% of global GDP. 
Faced with this unprecedented opportunity, companies are accelerating their investments to integrate AI into their operations: 41% are dedicated to Gen AI, 16% to agentic AI. However, despite these efforts, IDC's survey results highlight their shortcomings, emphasizing their lack of preparedness.

Adoption Slowed by Structural Challenges

One of the main difficulties identified by the study is data management and governance.
As noted by Stewart Bond, Research VP for Data Integration and Intelligence at IDC: 
"To ensure they leverage AI workflows that deliver sustainable and scalable value, companies must address fundamental challenges such as those related to data accuracy and governance."
Organizations adopting the "Data as a Product" model are seven times more likely to deploy AI at scale, demonstrating the importance of rigorous data structuring. This model, which involves managing data as a complete product, requires high standards of quality and accessibility. Yet, although 94% of organizations integrate or plan to integrate analytics features into their applications, only 23% actually succeed.
Data Governance and Infrastructure: The Crux of the Matter
To bridge this gap, companies must move beyond experimentation and focus on establishing solid foundations:
  • Strict data governance: ensuring the quality, accuracy, and security of the information used by AI.
  • A suitable and scalable infrastructure: organizations must invest in systems capable of supporting autonomous decision-making processes.
  • Effective analytics integration: transforming data into actionable insights to create value and promote informed decision-making.
James Fisher, Chief Strategy Officer at Qlik, emphasizes the importance of this transformation: 
"The potential of AI depends on how effectively organizations manage and integrate their AI value chain. Companies that fail to develop systems capable of delivering reliable and actionable insights will quickly fall behind."
IDC's report highlights a simple reality: enthusiasm is not enough, especially when it struggles to translate into concrete actions. The successful adoption of GenAI relies on companies' ability to effectively structure and exploit their data, a strategic asset that would otherwise remain underutilized.

Translated from GenAI : selon Qlik, combler l’écart entre ambition et réalité organisationnelle devient urgent pour les entreprises

To better understand

What is the 'Data as a Product' model, and why is it important for large-scale AI adoption?

The 'Data as a Product' model treats data like products, ensuring they are managed with high-quality standards. This is crucial for AI because it ensures access to accurate and reliable data, essential for AI-driven decisions.

What are the main regulatory challenges associated with the adoption of generative and agentic AI?

Regulatory challenges include data protection, AI model transparency, and accountability for automated incorrect decisions. Regulators seek to ensure AI compliance with ethical and legal standards.