Another exciting year for AI is drawing to a close. And what a year it has been, with the global AI market reaching an estimated $244 billion and the LLM race more intense than ever. The year 2025 brought ChatGPT 5, Gemini 3, Comet and Atlas AI browsers, Swedish startup Lovable becoming the fastest growing software company on record, and “Vibe coding” crowned as Collins Dictionary’s Word of the Year.
It is no surprise that 78% of organisations in the tech sector now reportedly integrate AI into their operations, up from 55% in 2023. This signals a seismic shift in how content is produced and consumed. Against this backdrop, we break down the enterprise AI trends we believe will define 2026.
Agentic AI and agentic workflows
In a recent set of industry assessments, many agentic deployments were shown to deliver disappointing results because they lacked practical use cases and measurable value. Many organisations could not even demonstrate an agent at work, despite considerable spending. With generative AI adoption at 65% and daily active users at 42%, expectations have sharpened, and tolerance for exploratory investment is low.
We expect this to change in 2026 as companies align with clearer benchmarks. Successful agentic AI now encompasses business-relevant metrics tied to profit and loss (P&L) and workforce trust. It also requires centralised oversight supported by shared libraries of agents, templates and tools. Agents are tested in advance, with working demos and clear feedback loops to build early user confidence.
The most mature organisations are rolling out agents as part of redesigned workflows, with explicit human roles for review and oversight. Built-in monitoring, including agents that cross-check each other’s work, is becoming a standard feature. As more agents automate their decision-making, monitoring becomes easier and trust increases. Agents remain imperfect, but 2026 could be the year they begin to deliver on their promise.
In 2026, enterprise AI will grow up. After years of experimentation, organisations are shifting into operational mode, leading to more predictable returns on AI investments. According to recent market analysis, delayed spending and vendor fragmentation are driving demand for composable architectures and what some analysts call agentlakes, which streamline integration and scale. The winners will mix powerful models with semantic structure while maintaining a balance between automation and human oversight, making workflows more flexible and robust.
Hybrid architectures, governed knowledge, explainable agents and human-in-the-loop systems will form the blueprint for next-generation AI. Rather than replacing human insight, the aim is to extend it. Through these changes, the most competitive companies will become learning systems that adapt at the pace of their environments.
The real story of 2026 is not how smart machines become but how intelligently we choose to build, govern and integrate them.
Mandatory AI training
Poor AI literacy still undermines adoption. According to a recent Forrester survey, 21% of AI decision-makers cited employee readiness as a major barrier. Organisations also face rising liability, especially in regulated sectors, prompting the need for structured AI education as a baseline for responsible use.
This is why Forrester predicts that 30% of large enterprises will introduce mandatory AI training in 2026. With enterprise-wide literacy, the organisation’s artificial intelligence quotient rises, strengthening its approach to risk. This also ensures that workers know how to evaluate, supervise and challenge AI outputs, which is essential in high-stakes contexts.
Companies are already partnering with service providers and technology vendors to create formal training routes. Clear measures of progress and role-specific learning paths are becoming standard as AI knowledge becomes a strategic asset rather than a nice-to-have.
Industry clouds, quantum readiness and digital twins
Quantum computing is gaining strategic relevance on two fronts. First, it offers transformational opportunities in finance, logistics and pharmaceutical research by solving complex calculations beyond the reach of classical systems. Secondly, with the threat it poses to current encryption standards, 2026 is seen as a deadline year for planning the shift to quantum-safe encryption, according to recent security readiness reports.
Digital twins are also evolving quickly. Instead of modelling isolated systems, they now simulate entire facilities and processes. With real-time data, sensing technologies and AI modelling, enterprises are moving from reactive approaches to predictive ones. Organisations report reduced development times, fewer failures and improved workflow optimisation.
These technologies connect with broader enterprise trends, including zero-trust edge architectures and agentic platforms. While the technical challenges are significant, human factors also come into play, such as closing skills gaps and introducing ethical guardrails to protect creativity and prevent harm.
Domain-specific models and decision intelligence
Enterprises are outgrowing general-purpose cloud platforms. Industry Cloud Platforms are gaining traction as providers offer vertical solutions with built-in compliance and data models. Gartner predicts that by the end of 2026, 70% of enterprises will use these platforms, up from under 15% in 2023.
These platforms support domain-specific models that allow organisations to automate complex decisions. They also unify infrastructure and data, reducing fragmentation and improving reliability. The result is cognitive automation that is easier to scale across departments.
As decision intelligence matures, enterprises are integrating models directly into their workflows, providing teams with faster and better-informed choices. This development marks a shift from ad hoc automation to structured intelligence embedded across the business.
Trust has become the currency of AI. With regulatory scrutiny increasing, organisations must engineer trust instead of assuming it. Structured and semantic data provides the foundation. Knowledge graphs and governed ontologies enable transparency by allowing every AI-driven conclusion to be traced to its source. This reduces hallucinations, supports compliance and enables continuous learning through expert feedback.
Data governance is no longer a supporting function. Now, it is a strategic asset that answers the hardest question: why did the model decide that? As regulators, boards and customers demand clear explanations, enterprises that take governance seriously will gain an advantage.
The new governance paradigm spans data, models and decision making. It includes versioned knowledge graphs, reasoning constraints and detailed logging of agent interactions. Leading organisations are forming cross-functional AI councils to oversee ethics, safety and performance. In this environment, governance becomes brand protection.
Responsible innovation and integration
Executives increasingly recognise the value of Responsible AI. In a 2025 survey, 60% said it boosts ROI and efficiency, while 55% reported improvements in customer experience and innovation. Yet almost half also noted difficulties in operationalising responsible principles.
In 2026, companies will move from intention to execution. Agentic workflows can now perform roughly half of the tasks humans currently handle, which raises new governance demands. New monitoring capabilities, from automated red teaming to deepfake detection, can support continuous assessment and strengthen oversight.
For Responsible AI to scale, organisations need upskilling programmes, clear documentation standards and risk tiering that determines when human intervention is required. Early integration between IT, risk and AI functions helps operationalise these frameworks and maintain trust. As oversight becomes more critical, independent assessments will also play a role for high-risk systems.
The enterprise AI landscape in 2026 will be defined by maturity. Organisations are moving from experimentation to disciplined, scalable implementation. Agentic systems will grow more capable, governance will become more rigorous, and AI literacy will become a requirement rather than an option. Domain specificity and trustworthy data will set leaders apart as they build systems that are not only powerful but safe and transparent. The organisations that thrive will be those that balance innovation with responsibility and treat AI not as a bolt-on tool but as a core part of how they think and operate.
Get ready for enterprise AI in 2026. Turn trends into practical value.