Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Ed Keisling, Chief AI Officer, Progress Software
As organizations move from testing AI in isolated pockets to embedding it across their technology stacks, 2026 is shaping up to be a defining year. The experimentation phase is ending. Teams are no longer evaluating whether AI can provide value but are now focused on how to operationalize it responsibly, safely and at scale. What emerges is the beginning of an intelligent enterprise: one where engineering workflows become AI-augmented, infrastructure evolves toward autonomous operation, and governance frameworks finally bring structure to AI’s rapidly expanding footprint.
The trends converging next year point to a future in which people will feel increasingly comfortable delegating tasks to AI, as long as they have effective controls in place to monitor the outcomes. Developers will rely on AI not just for code suggestions but to orchestrate entire workflows. Infrastructure teams will work alongside systems that can anticipate issues before they occur. And AI governance will evolve from an afterthought to a core pillar of enterprise operations. Taken together, these shifts signal a meaningful transformation, one that elevates the way organizations build, run and secure modern digital environments.
The first major shift is the maturation of AI-augmented engineering for all. Generative AI’s initial value in software development was largely tactical: suggesting code, generating boilerplate, helping with documentation, and automating routine and less complex tasks. In 2026, it will become mainstream for engineers to delegate significantly more meaningful work to AI coding agents, not only to accelerate new development but also to help address technical debt.
AI will move from assistive to orchestration, coordinating builds, testing dependencies, provisioning environments, validating configurations and supporting deployment decisions with far more contextual awareness. This is where agentic RAG (retrieval-augmented generation) becomes essential. Rather than making decisions in isolation, these systems will retrieve architecture diagrams, deployment histories, compliance policies, runbooks and operational data to reason through the “why” behind a recommendation and to provide critical context for the “where” and “how.” Enterprises are already scaling agentic AI systems, with one recent survey finding that 23% of respondents report their organizations are scaling an agentic system in at least one business function. The result is AI that doesn’t just generate output, it understands the environment it operates within.
This evolution will give rise to a more experience-focused model of DevOps, often referred to as “DevExOps”. Developers gain not just speed but clarity – less cognitive load, more proactive risk identification and better alignment with organizational standards. AI will help standardize quality across teams of varying skill and experience, making engineering more consistent and predictable. Automation frameworks across the industry, including those used for configuration management, provisioning, security and compliance, will become more adaptive, offering intelligent insights rather than static logic. In practice, this means teams spend less time stitching tools together and more time focusing on the work that genuinely moves the business forward.
The second shift is unfolding at the infrastructure layer, where complexity has outgrown human-scale monitoring. Hybrid and multi-cloud environments require constant attention, and traditional alert-driven operations simply cannot keep up. In 2026, these environments will begin transitioning from automated to truly intelligent. Load balancers, network intelligence platforms and observability tools will increasingly incorporate predictive analytics: forecasting demand, detecting anomalies earlier, and automatically proposing or initiating remediation actions based on historical patterns and learned behavior. Infrastructure will stop simply reacting and begin anticipating, and in some cases auto-remediating. Infrastructure and operations leaders already identify massive technical-debt burdens (with over 40% of systems reportedly past end-of-life or support) and foresee GenAI significantly impacting their function within the next two to three years.
This shift does not replace infrastructure teams; it elevates them. Instead of firefighting, teams will spend more time designing the policies and boundaries that guide autonomous systems such as defining reliability thresholds, security requirements, escalation paths and fail-over rules. The systems themselves will handle more of the repetitive work-analyzing telemetry, identifying correlations, understanding context and addressing common issues – before they escalate. Intelligent infrastructure becomes an enabler of uptime, resilience and more efficient operations, something organizations of all sizes are under increasing pressure to deliver.
The final, and perhaps most important, shift in 2026 will be the rise of AI assurance. As AI becomes embedded into engineering workflows and infrastructure operations, enterprises will demand transparency and trust – not just performance. The focus will move away from “how powerful is the model?” toward “can we understand, verify and audit what it is doing?” AI assurance will include explainability, lineage tracking, robust governance pipelines and clear visibility into how decisions are made. Agentic RAG strengthens this effort. By grounding reasoning in validated, enterprise-owned knowledge sources rather than opaque model behavior, it becomes easier to verify outputs, track recommendations and maintain compliance. Every decision an AI system makes will increasingly need to leave a trail, one that satisfies auditors, security teams, regulators, and internal stakeholders. Enterprises will elevate governance to the same level of importance as performance and efficiency.
Together, these trends point to a year in which enterprise technology becomes more intelligent, more autonomous and more accountable. Engineering teams will gain new leverage through AI-driven orchestration. Infrastructure will become more resilient and proactive. And AI systems will finally operate within clear guardrails that maintain trust and reliability. The organizations that embrace these changes early will benefit not only from greater speed and efficiency but from systems that can adapt, scale and secure themselves in ways that were not previously possible.
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