Organizations may find that in 2026, the next wave of adopting AI (Artificial Intelligence) will not simply be a continuation of 2025 but mark a shift toward more structured, more accountable and more scalable AI operations. AI is moving from experimental workflows and deployment into core business processes, which brings a new set of responsibilities. Across industries, and particularly in domains where reliability, safety and quality are essential, one foresees that the following four major trends will define how organizations navigate the coming year:
Fast-Moving AI Regulatory Landscape
In Europe, the EU AI Act moves toward implementation, but for some high-risk systems it must firstly be ensured that harmonized standards, conformity pathways and technical guidelines are strong enough for full enforcement. Other European regulations such as the Data Act and updated Machinery Regulation will add concrete obligations for integration of AI into products, production tools and connected systems.
In USA, regulatory activity is decentralizing primarily through federal agency mandates. In December 2025, the federal government signaled its intent to limit conflicting state AI laws through Trump’s executive order, aiming to block states from regulating AI companies and strengthen U.S. leadership in AI through low-burden national policy. The regulatory environment becomes dynamic and demanding for industry to keep track of.
Scaling AI with Tools & Technology
When shifting from isolated pilots to enterprise-wide AI governance platforms, governance must modernize at the same pace as the technology itself. Advanced tooling is required for risk management, documentation, monitoring, and compliance evidence. Also, automation is critical to support audits, model inventories, and scalable AI deployment. One realizes that trustworthy AI cannot be achieved by human oversight alone.
Autonomous AI Under Control
In 2026, autonomous AI will drive efficiency while introducing operational risk. Systems can move beyond their intended scope, chain multiple steps unpredictably, amplify errors faster than humans can detect, and interact with each other in ways that create emergent behavior. Managing these risks is becoming a core enterprise competency. For this, the Model Context Protocol (MCP) is emerging as a foundational standard.
Responsible AI Procurement
AI is now entering organizations primarily through procurement rather than internal development
Trust, safety and compliance must therefore be established before a system enters the organization, not retrofitted afterwards. Suppliers should also be assessed on their AI risk ratings, not least when integrators or consultants develop AI solutions on a client’s behalf.
Under the EU AI Act, organizations bear supply chain responsibility for high-risk AI systems. Tools like Nemko Digital’s AI Trust Mark offer an efficient way to demonstrate trustworthiness across governance, safety, and operational standards.
More insight is given in section 3 of Nemko Digital’s New Year AI Trust Special
For further information and/or application for Nemko’s AI related services, please contact Alicja.Halbryt@nemko.com or Bas.Overtoom@nemko.com.
(This article is based on section 3 of Nemko Digital’s publication New Year AI Trust Special; edited by T.Sollie)