Human Hesitation, Not Tech, May Hold Back Agentic AI in Enterprise: GlobalData
Analysts acknowledged the technology’s impressive growth but emphasized that its success within enterprises hinges on whether decision-makers believe it can demonstrable business value.
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[Image source: Chetan Jha/MITSMR Middle East]
As agentic AI emerges as a powerful driver of digital transformation across industries, analysts at GlobalData caution that the technology’s greatest obstacle may not be its capabilities, but human skepticism.
In a recent analysis, the research firm pointed to a rapidly growing market for agentic AI systems, which are designed to operate with greater autonomy and sophistication than earlier generations of generative AI. The analysts emphasized that these systems are already opening up opportunities across sectors, from large enterprises to smaller businesses, by enabling more efficient AI-native operations.
Yet, despite the momentum, uptake remains uneven.
“The next step is crafting these agents for practical high-value use cases,” said Isabel Al-Dhahir, principal analyst at GlobalData.
Al-Dhahir acknowledged the technology’s impressive growth but emphasized that its success within enterprises hinges on whether decision-makers believe agentic AI can “add demonstrable business value.”
The firm sees strong potential for agentic AI in consumer, enterprise, scientific, and industrial domains, particularly in environments where multiple specialized agents could be deployed to handle complex, coordinated tasks.
GlobalData expects agentic AI to “play a central role in the digital transformation of enterprise systems to AI-native stacks,” a shift that could benefit not just large technology players, but also startups, systems integrators, and software providers.
Still, reservations persist. Al-Dhahir noted the “ongoing scepticism” around business outcomes but argued that the technology’s “greater autonomy and methodical approach to reasoning, problem-solving and decision making” gives it the potential to outperform “previous iterations of generative” AI.
DevOps Integration Remains a Key Challenge
One promising domain for agentic AI is DevOps, where the technology could extend prior automation advances in areas like continuous integration, delivery pipelines, and infrastructure as code.
However, GlobalData’s research director William Rojas issued a note of caution.
“Many will fail as developers cultivate best practices for designing, building, testing, and validating” agentic systems, he warned, adding that integrating the technology into current DevOps workflows will remain a “critical challenge” for some time.
While the long-term potential is significant, Rojas advises a measured approach. He highlighted concerns such as hallucinations from foundation models and the difficulties of shifting from traditional software stacks to AI-native architectures.
As GlobalData outlines, the road to adoption will not be linear. However, with strategic design and focused execution, agentic AI could redefine enterprise transformation across the region and beyond.
