A new IDC study, sponsored by Qlik, reveals a growing divide between AI ambition and execution. While 89% of organizations have revamped their data strategies to integrate Generative AI, only 26% have successfully deployed AI solutions at scale. The study underscores the critical role of data governance, scalable infrastructure, and analytics integration in realizing AI’s full potential.
AI’s Promise vs. Readiness Challenges
1. AI Adoption vs. Readiness Gap
- 80% of organizations are actively investing in Agentic AI workflows, yet only 12% believe their infrastructure is ready to support autonomous decision-making.
- Despite 73% of organizations integrating Generative AI into analytics, only 29% have fully deployed these capabilities.
2. The Importance of Data as a Strategic Asset
- Organizations that treat data as a product are 7x more likely to scale Generative AI solutions.
- Embedded analytics is a priority, with 94% of enterprises embedding or planning to embed AI-driven analytics, but only 23% achieving widespread integration.
3. Governance & Infrastructure: The Missing Link
- Without addressing data accuracy, governance, and infrastructure scalability, businesses risk falling into an “AI scramble”—where enthusiasm outpaces execution, leading to missed opportunities.
- A lack of trusted, actionable insights threatens competitiveness, as companies struggle to turn AI ambition into sustainable, AI-driven innovation.
Strategic Imperatives for AI Success
Stewart Bond, Research VP for Data Integration and Intelligence at IDC, states:
“Generative AI has sparked excitement, but readiness remains a major hurdle. Businesses must tackle core challenges like data governance and infrastructure scalability to ensure AI delivers long-term value.”
Qlik’s Chief Strategy Officer, James Fisher, reinforces this, adding:
“AI’s transformative power depends on how well organizations integrate and manage their AI value chain. Without a solid foundation, companies risk lagging behind competitors who are moving to scalable AI-driven innovation.”
The IDC-Qlik study highlights a clear call to action: AI adoption must be backed by robust governance, infrastructure, and data integration to unlock scalable, enterprise-wide AI success. Businesses that bridge the readiness gap will be best positioned to capitalize on AI’s $19.9 trillion economic potential by 2030.
