There’s no question that AI enthusiasm is surging, with 87% of global business leaders believing on its transformative potential for business priorities. Although an optimistic outlook about AI’s capabilities and potential is valid, there’s also a critical barrier that’s often overlooked in all the excitement: inadequate infrastructure.
Here’s the reality check: although AI initiatives are essentially meeting business leaders’ expectations, a staggering 93% of organizations acknowledge their networks can’t handle what AI demands. That’s not just a technical problem; it’s a potential business-killer.
It’s not a disappearing problem, either. This infrastructure gap has worsened significantly since 2024, with 45% (up from 38%) of tech leaders now reporting network performance issues that limit AI projects. Additionally, 53% of organizations experienced direct revenue losses due to network issues over the past 12 months, with 22% losing more than 1% of their revenue.
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What can companies do to avoid these dire consequences? I see three critical areas that demand our immediate attention as technology leaders:
Stop Building Tomorrow’s AI on Yesterday’s Infrastructure
Here are some numbers that should wake up any business leader. Only 8% of enterprise networks are fully ready for next-gen AI initiatives. Another 40% are mostly ready with gaps, while 45% can only support current workloads.
Put simply, most companies are trying to build tomorrow’s AI on yesterday’s infrastructure.
It’s an easy problem to have, because network investments can be a hard sell to decision-makers. However, among companies indicating they had been financially harmed by network downtime or poor performance, 93% were not set up for full AI functionality. Just 6% of those with AI-ready networks reported the same. That’s not just correlation, it means holding back on network investments is a false economy that companies can’t afford.
It also means companies that fail to budget for comprehensive upgrades and create phased implementation roadmaps to account for AI now will fall further behind. A good place for leaders to start is by thoroughly evaluating their current network capabilities against AI requirements.
Our research identified three urgent improvement areas: flexibility and scalability (40% of organizations cited this as urgent, up from 27% in 2024), resilience and reliability (34%, up from 20%), and application performance, including latency issues (31%, up from 28%).
The scalability crisis is particularly acute: 46% of companies report their networks can’t scale up or down flexibly on demand to support AI projects. That’s a significant increase from 38% in 2024, and it directly constrains how quickly companies can deploy AI effectively.
Build the right partnerships — internally and externally
One of the most significant and misunderstood barriers to infrastructure challenges isn’t technical, it’s organizational. Twenty-eight percent of IT leaders struggle to meet AI expectations that are growing faster than their ability to deliver, while 33% believe the rest of the C-suite has unrealistic expectations about what technology can accomplish.
Combine that with 35% of organizations citing employee resistance as a barrier to AI implementation, and tech teams are facing a top-to-bottom collaboration nightmare. One path to success is to create AI infrastructure committees with stakeholders from IT, operations and business units. Our research indicates that 42% of AI initiatives are already jointly managed by business and IT, with 64% of CEOs actively supporting digital initiatives.
The skills of teams on the front lines also play a role, with 36% of organizations struggling to find or retain networking skills and 39% facing cybersecurity skills gaps. Leaders are addressing these challenges on two fronts: 52% are implementing internal upskilling programs, and 45% are increasingly turning to external partnerships for networking expertise. This trend reflects the shift from in-house network management to managed services.
Above all, leaders need to set clear metrics that connect infrastructure improvements to business outcomes. This will help ensure collaboration and strengthen advocacy from all stakeholders, including the C-suite and boardroom.
Prepare for the unpredictable
Geopolitical tensions are increasingly disrupting growth strategies, with 34% of organizations citing global turmoil as a top inhibitor of growth. This often takes the form of difficulties deploying AI outside of your own country, as network reliability varies dramatically by region.
Regional disparities can be quite pronounced. While only 3% of companies in North America face significant problems sourcing networks, this increases to 28% in Latin America, 32% in the Middle East, and 48% in Africa. The top challenges when sourcing networks globally are reliability (40%), finding the right providers (37%) and cost management (37%).
One key solution is diversification of strategic partners. Organizations are increasingly recognizing that specialized providers offer greater expertise, rather than building in-house teams. Right now, only 8% of companies are prioritizing upgrades to internal networking skills, while 45% are expanding external partnerships for networking support.
Leaders can build infrastructure to withstand regional constraints by diversifying those external partners and developing contingency plans for connectivity disruptions. This is particularly critical, as 43% of organizations plan to achieve international growth through cost containment and efficiency improvements.
The future is written: AI success will depend on the readiness of infrastructure. AI will be how businesses operate in every department in the future, but success won’t come from rushing it out the door.
Leaders must focus on investing in what matters: resilient networks, internal collaboration, and finding the right global partners. Companies that build infrastructure to drive success now will continue to thrive in the AI economy for years to come.
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