Shane Buckley is President and Chief Executive Officer of Gigamon, a leader in deep observability.gettyArtificial intelligence (AI) has quickly become a boardroom mandate. Across industries, CEOs are pushing their organizations to deploy AI faster, scale AI initiatives more aggressively and embed AI into every corner of the business. The pace of adoption is extraordinary. But beneath the excitement lies a growing reality that many business leaders are only beginning to confront: AI adoption is accelerating faster than their ability to see, understand and govern how these systems are operating. For the past two years, the conversation around AI has focused on deployment. The next phase will focus on control. Token consumption, cloud inference costs, GPU infrastructure and the movement of enterprise data are creating new operational pressures. AI is not simply creating new applications. It is driving a surge in data volumes, traffic and infrastructure demands that traditional operational models were never designed to manage. As organizations scale AI initiatives, the challenge extends beyond cost. Uber burned through its AI budget in the first four months of the year, causing its CTO to question ROI altogether. At the same time, concerns around security, governance and intellectual property are increasing demand for greater visibility and control over the data powering AI systems. That is why metadata and observability are becoming increasingly important in the AI era. Rather than relying on massive volumes of raw data alone, organizations are turning to metadata-driven insights to better understand how AI systems and autonomous agents are operating across their environments. The ability to monitor AI activity in real time is becoming essential for managing costs, strengthening security and maintaining operational control as AI adoption accelerates. ​AI Adoption Outpacing Visibility A recent survey evaluated how organizations are adopting AI across both IT and security operations, while also battling new risks stemming from unsanctioned AI use, autonomous systems and evolving attack techniques. While 93% of respondents invested in new security technologies to improve detection and visibility last year, 41% reported it now takes longer to detect breaches. AI adoption is outpacing organizations’ ability to govern and monitor it properly, creating a dangerous imbalance. In many organizations, engineering teams are actively encouraged to increase AI usage. More tokens, more agents and more automation are often viewed as indicators of innovation. But consumption alone is not the answer. Without clear visibility into how AI systems are behaving, organizations risk creating environments where costs get out of control and accountability disappears. In the next two years, the average global Fortune 500 enterprise will run over 150,000 AI agents, according to Gartner. But only 13% of organizations think they have adequate AI agent governance in place. Without governance, these agents can quickly become a security and cost nightmare. Traditional operational tools were never designed for this level of complexity. As AI workloads proliferate across data centers, public cloud environments and emerging AI infrastructure, organizations require greater visibility into how data moves between systems, applications and models. Visibility must become a strategic priority rather than an operational afterthought. Why Visibility Matters In The AI Era Every AI interaction leaves behind a trail of operational intelligence. Understanding how data moves between systems, applications, models and agents is becoming essential for maintaining control as AI adoption accelerates. Metadata provides the context needed to understand how workloads communicate, where traffic is moving and how AI systems behave over time without requiring organizations to analyze every prompt, transaction or data payload individually. In many ways, the network becomes the most reliable source of truth for understanding AI activity. Network-derived telemetry, including application metadata, packets and flows, provides the real-time context needed to understand how AI workloads communicate, where data is moving and whether activity aligns with policy. The urgency of that visibility is growing as AI accelerates both innovation and cyber risk. AI tools like Mythos and ChatGPT 5.5 Cyber can automate tasks that once required weeks of manual effort, helping identify vulnerabilities and analyze attack paths at remarkable speed. The same advances are also lowering the barrier for bad actors, enabling faster reconnaissance and more sophisticated attacks. According to Chris Konrad, VP of Global Cyber at WWT, the problem is that most organizations cannot actually see these infiltrations happening inside their networks until it's too late. Real-time visibility into network activity is critical for detecting breaches and identifying lateral movement before attackers can expand their reach across the environment. In an era of AI-powered cyber attacks, operating without network visibility is essentially operating blind. Metadata-driven observability allows organizations to see patterns that would otherwise remain hidden. It helps identify abnormal behavior, detect security threats, understand resource consumption and optimize performance. Most importantly, it provides the visibility needed to make informed decisions about how AI systems are operating across the enterprise. AI Ops Will Define The Next Phase Of Enterprise AI Just as cloud computing gave rise to disciplines such as DevOps and FinOps, the growth of enterprise AI is creating the need for a new operational model: AI Ops. AI Ops will bring together AI performance, cost optimization, governance, observability and security into a unified discipline. Organizations will need continuous visibility into model behavior, workload performance, token consumption, security posture and the movement of data across AI systems and infrastructure. Managing these systems effectively will require the same rigor and operational oversight applied to every other mission-critical technology platform. As AI becomes increasingly autonomous, visibility will become the prerequisite for innovation. Organizations that understand how data, traffic and AI workloads interact across their environments will be best positioned to realize AI’s value while maintaining control of the risks that come with it. The companies that win the AI race will not simply be the ones deploying the most AI. They will be the ones with the clearest understanding of how AI is operating across their environments. ​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?