The latest study reveals a significant gap between organizations’ AI ambitions and their ability to execute at scale. While an overwhelming 89% of businesses have revamped their data strategies to embrace Generative AI, only 26% have successfully deployed AI solutions at scale. This disparity underscores the critical need for improved data governance, scalable infrastructure, and analytics readiness to fully unlock AI’s transformative potential.
The findings, published in an IDC InfoBrief sponsored by Qlik, come at a time when businesses worldwide are racing to integrate AI into their workflows. With AI projected to contribute $19.9 trillion to the global economy by 2030, the pressure to adopt AI-driven innovation has never been greater. However, readiness gaps threaten to derail progress, shifting the focus from simply building AI models to establishing the foundational data ecosystems required for long-term success.
Stewart Bond, Research Vice President for Data Integration and Intelligence at IDC, emphasized that while Generative AI has generated immense excitement, a significant readiness gap remains. Businesses must address fundamental challenges such as data accuracy, governance, and integration to ensure that AI workflows deliver sustainable and scalable value. Without tackling these core issues, organizations risk falling into what is described as an “AI scramble,” where the ambition to implement AI outpaces the ability to execute effectively, ultimately leaving potential value unrealized.
James Fisher, Chief Strategy Officer at Qlik, reinforced this concern, highlighting that AI’s potential is directly tied to how well organizations manage and integrate their AI value chain. The research underscores a stark divide between ambition and execution. Companies that fail to build systems capable of delivering trusted, actionable insights will quickly lag behind competitors who are moving towards scalable AI-driven innovation.
The IDC survey uncovered several critical insights that illustrate both the promise and challenges of AI adoption. A notable 80% of organizations are actively investing in Agentic AI workflows—AI systems that operate with a level of autonomy—yet only 12% feel confident that their infrastructure can fully support autonomous decision-making. This signals a major gap between aspirations and technical readiness.
Another key trend is the momentum behind treating “data as a product.” Organizations proficient in curating and managing their data as a strategic asset are seven times more likely to deploy Generative AI solutions at scale. This finding reinforces the transformative potential of well-governed, accountable data ecosystems in driving AI success.
The study also highlights the rapid rise of embedded analytics, with 94% of organizations either embedding or planning to embed analytics into enterprise applications. However, only 23% have successfully integrated analytics into the majority of their applications, pointing to a clear challenge in fully operationalizing AI-driven insights.
Generative AI’s influence on business strategy is further evident, with 89% of organizations having overhauled their data strategies in response to AI advancements. Yet despite 73% of organizations integrating Generative AI into their analytics solutions, only 29% have fully deployed these capabilities, reinforcing the widespread struggle to move from experimentation to large-scale implementation.
These findings stress the urgent need for businesses to bridge the gap between ambition and execution. A clear focus on data governance, infrastructure, and leveraging data as a strategic asset is essential for unlocking AI’s full potential. To transition from AI pilots to enterprise-wide transformation, organizations must invest in scalable architectures, robust data management frameworks, and integration strategies that ensure AI delivers meaningful, business-critical insights.
The IDC survey findings highlight a pivotal moment for enterprises navigating the AI landscape. While enthusiasm for AI is at an all-time high, success will ultimately depend on whether organizations can establish the right foundations. By prioritizing governance, infrastructure, and seamless data integration, businesses can turn AI from a promising innovation into a sustainable driver of growth, efficiency, and competitive advantage.