With enterprise AI spending rising sharply, 2026 marks a pivotal year where tech teams are embracing agentic AI to automate and coordinate workflows, despite challenges in providing sufficient business context for more complex tasks.
- Tech teams confident in agentic AI for routine and data tasks
- Business context gaps limit agent readiness for complex workflows
- Human oversight remains vital as agent capabilities mature
What happened
Enterprise investment in artificial intelligence is experiencing significant growth in 2026, described by Gartner as an "inflection year" for aligning AI initiatives with strategic business objectives. As organizations look to derive clear returns on AI investments, agentic AI—intelligent systems that can automate and coordinate workflows—has become central to these efforts, particularly within technology functions.
Recent research based on a survey of 300 global technology experts highlights that confidence in using agentic AI is high across many AI, data, and cloud-related tasks. The study reveals tech teams are actively deploying AI agents to handle repetitive functions, streamline processes, and support complex decision-making. However, agent readiness drops when tasks require nuanced business context, which remains challenging to supply reliably at the scale and speed enterprises demand.
Why it matters
Tech infrastructure costs are projected to increase two to three times by 2030 without a proportional rise in budgets, according to McKinsey, underscoring the urgent need for automation and efficiency gains. Agentic AI offers the promise of bridging this gap by not only automating manual processes but also by enabling more dynamic coordination between human teams and AI agents to meet business goals.
Data workflows represent a breakthrough domain where agentic AI excels due to the structured nature of data and opportunities for domain experts to embed business context. This creates trusted outcomes in data monitoring, anomaly detection, and visualization. The ability to integrate agents into existing operational boundaries and governance models is improving trust, which is critical for broader adoption as automated decision making involves higher risk and accountability.
What to watch next
Advancements in context-generation capabilities for agentic AI remain an area to watch closely. Progress here will be essential to enable agents to handle more complex, multi-step workflows requiring reasoning and understanding of business nuances. As enterprise data environments become easier to access and integrate with agent lifecycles, confidence in AI-driven automation is expected to accelerate.
Human oversight will continue to play a key role in deploying agentic AI safely and effectively. Observing how organizations establish governance frameworks, oversight mechanisms, and identity systems compatible with AI will be important. The evolving collaboration between developers, executives, and AI agents will shape how AI technologies enhance career opportunities and transform operational models in the coming years.