AI Predictions for 2026: 5 Changes Reshaping Enterprise IT

Artificial intelligence has spent the past few years proving its usefulness.

Enterprises will expect AI to demonstrate its trustworthiness by 2026. After years of pilots and proof-of-concept projects, AI is moving into permanent roles inside enterprise IT.

The focus is shifting away from impressive demos and toward reliable, day-to-day work. Agentic systems are beginning to handle real tasks, while AI governance is moving from IT departments into boardroom discussions. At the same time, companies are facing scalability constraints, including energy costs and geopolitics.

With this in mind, here are my top AI predictions shaping how enterprises will build, govern, and operate IT systems in 2026.

Agentic AI replaces assistants as digital co-workers

AI assistants that answer questions or generate content are giving way to agentic AI systems that can initiate tasks and manage workflows.

Microsoft set the tone by declaring 2025 the start of human-agent collaboration, rolling out assistants that can search for information and complete tasks such as scheduling or onboarding. This signals where enterprise AI is headed.

According to Microsoft’s 2025 Work Trend Index, 81% of business leaders expect AI agents to be deeply integrated into their strategic roadmap within the next 12 to 18 months. Microsoft also noted in its 2026 predictions report that AI agents will proliferate and play a larger role in daily work, acting more like teammates than tools.

As agentic systems become more capable of completing tasks end-to-end, enterprises are expected to deploy them more aggressively in 2026, particularly in areas where automation can reduce operating costs. Unlike traditional assistants that wait for prompts, agentic systems can schedule work, route service tickets, and coordinate across different platforms with limited human intervention.

The shift turns AI into a digital co-worker rather than a productivity tool, raising new questions around permissions, accountability, and escalation when systems act independently. 

Structured AI governance becomes a core enterprise requirement

As AI systems influence customer interactions, hiring decisions, and operational outcomes, informal oversight is no longer sufficient. 

A 2025 survey by Gradient Flow found that 75% of organizations had formal AI policies outlining acceptable and prohibited uses, a figure echoed by 74% of technical leaders. Adoption varied by company size, with 81% of mid-sized firms and 77% of large enterprises reporting AI policies, compared with 55% of small businesses. 

However, the presence of policies doesn’t always translate into consistent enforcement. In many organizations, AI guidelines function more as a reference document than operational controls. As agentic AI and automated decision-making expand, enterprises are under growing pressure to translate policy into enforceable workflows, technical guardrails, and a clear accountability strategy. 

Regulatory pressure is pushing organizations to move from written policies to enforceable controls. For instance, the EU’s landmark AI Act will become fully applicable in August 2026, marking a significant milestone in AI governance. This will enforce obligations for high-risk systems in 2026, while export controls and sector-specific rules are already shaping enterprise AI design.

AI-enabled cloud spending accelerates as deployments scale

AI is driving a new phase of cloud growth. But this time, it’s tied to production workloads rather than experimentation. 

Gartner forecasts that worldwide IT spending will total $6.08 trillion in 2026, an increase of 9.8% from 2025, with AI-related services driving that demand. As organizations move models into daily operations, cloud providers are reporting rising demand for AI training, inference, and data platforms. 

That growth is also reshaping where enterprises choose to run large-scale AI workloads.

According to NASDAQ, Google Cloud has positioned its Tensor Processing Units as an alternative to traditional GPU-based deployments, attracting AI developers focused on optimizing performance and cost at scale. Alphabet and Anthropic also announced that the large language model developer would begin using TPUs on Google Cloud in 2026, signaling a shift in how enterprises evaluate cloud-based AI infrastructure. 

As AI-enabled cloud spending accelerates, enterprises face growing pressure to strike a balance between performance, cost, and vendor dependency. Infrastructure decisions made during this phase should be strategic, shaping long-term operating costs and positioning the organization to compete effectively as AI becomes integral to business operations. 

Digital sovereignty reshapes where AI runs and who controls it

Geopolitics is increasingly shaping enterprise IT strategy. As AI systems rely on vast amounts of data and computing power, governments are increasingly asserting control over where data is stored, how models are trained, and which providers can be used. 

The European Union’s General Data Protection Regulation (GDPR) already restricts cross-border data transfers, and the EU’s AI Act adds new obligations. The EU AI Act focuses on transparency, risk mitigation, robust governance, and documentation for AI systems. Similar localization and security requirements are emerging in regions including India, China, and the Middle East, which can complicate global AI deployments. 

Enterprise behavior is beginning to reflect those pressures. Organizations consider data residency and national regulations as primary factors in their AI selection and adoption processes. Governments are also investing directly in domestic AI infrastructure, including sovereign cloud initiatives and state-backed compute programs designed to keep sensitive workloads within national borders.

As a result, organizations are adopting hybrid and multi-regional deployments that allow sensitive data and workloads to remain in specific jurisdictions. Additionally, open-source models will continue to gain traction as a way to retain greater control over training and deployment without relying entirely on foreign providers.

Embodied AI and robotics scale in logistics and operations

AI’s impact is expanding beyond software into physical environments. Embodied AI and robotics are scaling across warehouses, factories, and logistics networks, handling inspection, sorting, and movement tasks with more autonomy. 

The scale of potential impact is significant. McKinsey has estimated that more than half of the US workforce could be automated with today’s technologies. According to the report, currently available technologies could theoretically automate activities that account for about 57% of work hours in the US, spanning manufacturing, transportation, and logistics roles.

However, the firm emphasized that automation is likely to reshape jobs rather than eliminate them, shifting human work toward supervision and coordination. 

For enterprises, that distinction matters. As physical AI systems move into production, IT leaders need to make decisions that extend beyond software development. For instance, robotics adoption now requires coordination across IT, operations, safety, and workforce planning, along with clear governance policies around autonomous systems. In 2026, embodied AI is expected to be one of the most visible indicators that AI has moved from experimentation into everyday operations.

What enterprises should prepare for in 2026

Artificial intelligence is no longer confined to pilots or standalone tools. AI systems are being deployed across core business applications, cloud infrastructure, and physical operations.

Agentic AI will execute tasks across multiple platforms, AI workloads will run at scale in production cloud environments, and automated systems will operate alongside the human workforce in logistics and manufacturing.

To support this shift, enterprises will need enforceable AI governance, production-grade infrastructure, clear ownership, and controls that can adapt to real-world conditions.

For a look back at how enterprise AI reached this point, see eWEEK’s roundup of the biggest AI moments of 2025. 
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