CIOs Accelerate AI Spend Despite Risks
A new report from Logicalis reveals that 94% of CIOs have increased spending on AI, but the rapid pace is causing concern. Half of the CIOs surveyed believe adoption is moving too fast, and 62% admit to compromising on governance due to limited knowledge, creating significant unchecked risk.
This surge in AI spending is part of a larger trend, with global IT spending expected to rise 10.8% in 2026, largely driven by an 80.8% projected increase in AI investment from 2025. This push is fueled by early successes, as over a third of organizations have accelerated AI initiatives after positive proof-of-concept results in areas like predictive analytics and customer experience. The investment is already having a significant macroeconomic impact. In 2025, business investment in AI and related data centers accounted for an outsized 20% to 30% of U.S. GDP growth across the first two quarters. Looking forward, artificial intelligence is projected to add as much as $15.7 trillion to the global economy by 2030. The primary obstacle to scaling AI beyond initial deployments is not a lack of funding, but a shortage of talent. A lack of internal technical capability is reportedly holding back the AI ambitions of nearly 90% of organizations. This skills gap is a critical factor forcing many companies into a "learning as we go" approach, with 89% of CIOs describing their strategy in these terms. Specific governance failures stem from more than just a knowledge gap. Key challenges include integrating fragmented systems (cited by 58% of organizations), replacing or scaling inefficient manual processes (55%), and navigating complex regulatory and compliance hurdles (43%). These issues create vulnerabilities such as algorithmic bias, data privacy breaches, and a lack of transparency in AI-driven decisions. In response to these risks, formal frameworks are becoming critical tools. Organizations are adopting voluntary guides like the NIST AI Risk Management Framework to responsibly manage development and deployment. These frameworks provide structured approaches to identifying and mitigating AI risks throughout the technology's lifecycle. An emerging concern layered on top of governance is the environmental cost of AI. As data-intensive AI workloads increase, so does their energy consumption. Currently, only 39% of CIOs are extremely confident that their organization is actively managing the environmental impact of their AI systems
