Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Douglas Wadkins, CTO, Opengear
AI is now embedded everywhere-in data centers, public cloud environments, and increasingly at the edge, as organizations push to improve efficiency, customer experience, and real-time decision-making. But while adoption accelerates, operational readiness has not kept up. What’s required to deploy AI systems is very different from what’s required to keep them running reliably over months and years. In 2026, the industry will face a new reality: AI’s rapid expansion is creating complexity faster than most organizations can manage, and that complexity brings risk. Leaders across the infrastructure space, including teams at Opengear, are seeing firsthand how the rapid expansion of AI is introducing new operational risks and reshaping expectations for resilience.
AI’s Expansion Will Drive Unprecedented Complexity
AI is no longer isolated in pilots or R&D initiatives. It now spans the full infrastructure stack, with model training in centralized facilities, inference running across distributed edge sites, and data pipelines connecting multiple platforms and services. Because AI has applicability across data centers, cloud, and edge, the operational surface area grows exponentially.
Teams often underestimate what long-term reliability requires. AI introduces dependencies on orchestration frameworks, continuous model updates, telemetry ingestion, GPU fabric coordination, and strict latency budgets. Each additional component and each new location where AI runs creates another potential point of failure. In 2026, complexity itself becomes a top-tier operational challenge, demanding new visibility and risk-mitigation strategies.
Agentic AI Will Require Stronger Guardrails and Governance
2025 marked the early rise of agentic AI, systems that don’t just generate answers but take autonomous action. These systems operate at speeds, levels of persistence, and reasoning patterns that don’t always align with human expectations.
Early adopters have already uncovered agents attempting to access systems they were never designed to interact with or producing misleading explanations during audits. Because these agents work continuously and can connect disparate pieces of information in unexpected ways, organizations must strengthen governance frameworks in 2026. We’re entering an era where access has to be scoped, time-bound, and explainable to accommodate AI agents. Transparent policy gates, immutable audit trails, and independent oversight channels will become essential for safely scaling agentic systems.
Technical Roles Will Converge Into Hybrid Skillsets
AI-enabled tooling is blurring the boundaries between product management, architecture, and engineering. Product managers can generate UX flows and API structures with AI. Architects can quickly draft user journeys and functional requirements. Engineers can scaffold or refactor large portions of code automatically.
This convergence will accelerate in 2026, leading to hybrid roles that blend systems thinking with product instincts. Titles such as “Principal Product Technologist” will become more common, reflecting the need for professionals who understand systems holistically. For operations teams, this shift means moving from task executors to supervisors of automated and agentic workflows, humans in the loop responsible for escalation, oversight, and safe decision-making.
AI Processing Will Move to the Edge, Shifting Reliability Risks Outward
Performance demands, data sovereignty, and compliance requirements are pushing AI closer to where data is generated. Organizations are beginning to build “AI factories” at the edge, often repurposing facilities such as former telco central offices with existing power and connectivity.
This shift will accelerate in 2026, and with it, the reliability burden will move outward. Thousands of remote or lightly staffed locations will now host critical AI workloads and AI inference pipelines, and update processes. Outage domains that were once local will have system-wide consequences. As a result, organizations will adopt stronger automation, resilient network architectures, and operational strategies designed to handle edge failures effectively.
Quantum and AI Together Will Reshape Security Assumptions
The timeline for cryptographically relevant quantum computing is accelerating. Although broad quantum impact may still be years away, certain weak encryption algorithms could become vulnerable much sooner. When combined with AI-driven attack capabilities, where actors with limited technical expertise can execute sophisticated attacks, the security landscape will shift dramatically in 2026.
Organizations will begin adopting post-quantum cryptography, prioritizing auditability, and rethinking risk models in which AI is both attacker and defender. Governance frameworks will rise in importance, and conversations about risk tolerance will move from abstract discussions to everyday operational planning.
Resilience Will Become the Most Critical Layer Beneath AI
As AI increases system complexity, the industry will recognize that availability alone does not guarantee resilience. What’s required to deploy AI is not the same as what’s required to keep it functioning safely over long periods. In 2026, organizations will prioritize architectures that assume failure and are designed to recover from it.
This includes independent management paths, backup controls to mitigate unknown behaviors, secure fallback channels, and policy-driven recovery workflows. Whether supporting cloud, data center, or edge AI, resilience will shift from a background concern to a foundational design requirement.
Looking Ahead
AI will define 2026, but the ability to manage the complexity, governance challenges, and new reliability demands that come with it will determine which organizations succeed. As enterprises modernize their operational strategies, companies like Opengear, which focus on supporting secure access and resilient operations in complex environments, will play an important role in helping teams maintain continuity as AI scales. Organizations that invest early in resilience and operational readiness will be best positioned to adopt AI safely and sustainably in the years ahead.
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ABOUT THE AUTHOR
Douglas has deep product management, strategy, business development, and general management expertise in unified communications, cloud PBX and contact center, enterprise, carrier, and mobile networking, IoT, security and subscription-based business models. Prior to joining Opengear, he started and grew T-Mobile for Business’s Product and Strategy team which is responsible for all non core wireless businesses. He has been a member of the founding management team of several Silicon Valley technology startups with two successful exits and has taken stagnant companies with differentiated technology and found new markets leading to acquisition. Douglas has developed patented technology and written technical standards specifications for industry consortiums as well as contributed to standards bodies like the IETF, and ETSI.
