Techno Blogging
Gartner has found that organizations achieving strong outcomes from artificial intelligence initiatives invest up to four times more—relative to revenue—in foundational capabilities such as data quality, governance, skilled talent, and change management.
Despite rising investment, confidence in AI returns remains limited. A global survey of 353 data, analytics, and AI leaders found that only 39% believe their current AI spending will positively impact financial performance.
Rita Sallam said data and analytics leaders will play a critical role in delivering AI value through 2030, but doing so will require major shifts in how organizations structure teams, build capabilities, and scale AI initiatives.
Gartner highlighted six key transformations needed to realize AI value. These include moving toward AI-first operating models, redesigning teams for human and AI agent collaboration, and treating data context—such as metadata and semantics—as critical infrastructure for intelligent systems.
The firm noted that organizations with advanced AI-ready data and analytics capabilities are achieving up to 65% better business outcomes, including revenue growth and cost efficiency. However, many enterprises remain stuck in pilot phases due to fragmented engineering practices and lack of integration across data, AI, and software systems.
Security and governance also remain major concerns. A separate Gartner survey found that only 23% of IT leaders are highly confident in their organization’s ability to manage risks associated with generative AI deployments.
Gartner emphasized that future AI success will depend on building trust into systems through governance frameworks that address bias, privacy, and compliance in real time, rather than relying on traditional control mechanisms.
The report concludes that organizations must move beyond short-term return-on-investment metrics and instead focus on creating long-term value cycles, where efficiency gains from AI are reinvested into innovation and growth.
Despite rising investment, confidence in AI returns remains limited. A global survey of 353 data, analytics, and AI leaders found that only 39% believe their current AI spending will positively impact financial performance.
Rita Sallam said data and analytics leaders will play a critical role in delivering AI value through 2030, but doing so will require major shifts in how organizations structure teams, build capabilities, and scale AI initiatives.
Gartner highlighted six key transformations needed to realize AI value. These include moving toward AI-first operating models, redesigning teams for human and AI agent collaboration, and treating data context—such as metadata and semantics—as critical infrastructure for intelligent systems.
The firm noted that organizations with advanced AI-ready data and analytics capabilities are achieving up to 65% better business outcomes, including revenue growth and cost efficiency. However, many enterprises remain stuck in pilot phases due to fragmented engineering practices and lack of integration across data, AI, and software systems.
Security and governance also remain major concerns. A separate Gartner survey found that only 23% of IT leaders are highly confident in their organization’s ability to manage risks associated with generative AI deployments.
Gartner emphasized that future AI success will depend on building trust into systems through governance frameworks that address bias, privacy, and compliance in real time, rather than relying on traditional control mechanisms.
The report concludes that organizations must move beyond short-term return-on-investment metrics and instead focus on creating long-term value cycles, where efficiency gains from AI are reinvested into innovation and growth.
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