The top enterprise technology trends for 2026 are agentic AI, enterprise automation, data quality and governance for AI, real-time analytics, AI-augmented security, identity-centric zero trust, hybrid/multi-cloud optimization, FinOps cost discipline, platform convergence, and infrastructure modernization. ETR surveyed 1,357 technology leaders to identify what will shape strategy and spend.

Top Trends in 2026

1. Advancing Autonomous and Agentic AI Systems
2. Expanding AI-Driven Automation Across the Enterprise
3. Strengthening Data Quality and Governance for AI
4. Scaling Real-Time and Streaming Analytics Implementations
5. Countering Cyber Threats with AI-Augmented Security
6. Adopting Identity-Centric and Zero-Trust Principles
7. Optimizing Hybrid and Multi-Cloud Environments
8. Mitigating Cloud Costs through FinOps
9. Converging AI, Data, Security, and Cloud Platforms
10. Modernizing Infrastructure and Reducing Technical Debt

Additional Areas Mentioned: Workforce reskilling and skills adaptation, compute constraints and infrastructure capacity (GPUs), end-to-end observability, specialized or domain-specific AI models, AI-enabled software development, enterprise knowledge graphs, internal decision-support and AI copilots, and quantum computing readiness.

How Enterprise Technology Priorities Have Evolved from 2025 to 2026

Comparing the 2026 survey responses with ETR’s 2025 data set, the most notable shift is not a change in core themes, but an evolution in maturity and realism. Many of the priorities evident in 2025, including AI, automation, cloud optimization, security, and data foundations, remain central in 2026, but are increasingly framed around execution, operational constraints, and integration challenges rather than potential alone. AI discussions, in particular, show less speculative language and clearer emphasis on scaling, governance, and dependency on data quality and security readiness, reflecting greater awareness of what is required to move from pilots to sustainable deployment.

Several themes that appeared more aspirational in 2025 are also more grounded in 2026. Automation is discussed less as a standalone initiative and more as a practical mechanism for driving efficiency and cost control. Cloud is treated as an established operating environment rather than an adoption decision, with responses emphasizing optimization, cost discipline, and day-to-day management. This shift also coincides with more explicit attention to budget accountability and return on investment, suggesting that technology decisions are increasingly being evaluated through the lens of financial discipline rather than expansion alone. Security remains a top concern across both data sets, but 2026 responses reflect a more immediate, execution-focused risk posture, with greater attention to identity-centric architectures, AI-enabled threat escalation, and automated detection and response. Overall, the comparison points to continuity in strategic direction, alongside a clear shift from aspiration to feasibility, and from conceptual ambition toward operational accountability.

How Enterprise Technology Priorities Vary by Size, Industry, and Role

While the same core themes recur across subsamples, the framing and emphasis differ in ways that reflect scale, regulatory exposure, and operational maturity. The table below highlights areas where these differences are most pronounced across key segments in our work.

• Large Organizations: Large organizations frame AI, security, and cloud initiatives as system-level transformation efforts. Responses more often reference autonomous capabilities, architectural simplification, and identity-centric controls, with strong emphasis on scale, governance, and integration complexity. Observability and end-to-end visibility surface more frequently as stack and data sprawl increase. 
• C-Suite Executives (CIO, CTO, CISO, CDO): C-suite respondents provide the most integrated perspectives, linking AI, data readiness, security posture, and cloud operating models. Compared to other roles, they focus less on tools and more on long-term operating model change. Workforce readiness and reskilling appear more often as secondary considerations. Practitioner and VP-level respondents more frequently emphasize execution mechanics such as automation scope, platform choices, and cost controls, while the C-suite abstracts these to broader questions of readiness, integration, and sustainability.
• Financials / Insurance: Financial services respondents over-index on security, identity, and data governance, reflecting regulatory and risk sensitivity. AI is discussed cautiously and is often framed as dependent on strong data lineage and trust. Post-quantum and cryptographic risk surface more frequently here as longer-term considerations.
• Midsize Organizations: Midsize organizations emphasize automation, efficiency, and cloud cost discipline, framing AI primarily as a near-term productivity lever. There is less focus on platform-level consolidation or architectural change. Compute constraints and infrastructure capacity are mentioned more often than among large enterprises.
• IT / TelCo: IT / TelCo respondents use more forward-leaning language around autonomous AI, automation, and architectural change. Responses more frequently reference early adoption and experimentation. AI-enabled software development appears more often in this group than in others. 
  
  

What Technology Leaders Are Saying

Today’s technology leaders aren’t just talking about adopting AI and cloud, they’re debating what it takes to make them operational at scale. Priorities are sharpening around four themes: moving beyond copilots toward more autonomous, agentic systems; treating cybersecurity as an AI-fueled arms race that demands faster detection and response; fixing the data quality and governance gaps that still limit AI’s real impact; and applying FinOps discipline to rein in cloud spend as environments mature. 

• Advancing Autonomous and Agentic AI Systems. Respondents increasingly describe a shift from assistive AI toward agentic systems capable of executing tasks, coordinating workflows, and acting with greater autonomy. While many characterize adoption as early, there is consistent intent to move beyond copilots toward systems that can operate with limited human intervention, particularly where workflows are well-defined and data foundations are mature.
• Addressing Rising Cyber Threats with AI-Augmented Security. Security-related responses consistently highlight a rapidly evolving threat environment with AI accelerating both attack sophistication and defensive capabilities. Many respondents frame cybersecurity as an arms race, emphasizing the need for AI-driven detection, response automation, and stronger security architectures to keep pace with increasing speed and scale of attacks.
• Strengthening Data Quality and Governance for AI. Across responses, data quality, lineage, and governance are repeatedly cited as gating factors for AI success. Rather than treating data as a downstream consideration, respondents emphasize that trusted, well-governed data is a prerequisite for scaling AI initiatives and realizing meaningful impact.

• Driving Cloud Cost Discipline through FinOps. Cloud-related commentary reflects a shift from adoption toward optimization and cost discipline. Respondents emphasize visibility, accountability, and governance as cloud environments mature, with FinOps increasingly embedded as an operational practice rather than a standalone initiative.