AI Trends in 2026
Where is AI heading next and what does it mean for business?
Explore the trends shaping enterprise AI and what they mean for your organization.
From AI Experimentation to Enterprise Impact
AI is moving beyond experimentation to become a critical part of how organizations operate, make decisions, serve customers, and compete. The key question is no longer “What could AI do?” but “Where can AI deliver measurable business value?”
Our latest report brings together insights from industry leaders on the trends shaping enterprise AI in 2026 and beyond, from the rise of agentic AI and the growing importance of trust and governance to the practical challenges of scaling AI and turning investment into impact.
Explore the latest insights in our report, AI in Action: From Experimentation to Execution, a mid-year review of the developments shaping enterprise AI and the priorities that will define the next phase of AI adoption.
Discover the trends, challenges, and opportunities that could shape your AI strategy over the next 12 months and beyond.
📌 Essential Takeaways for Business Success
🔹Scale AI from pilots safely and effectively into governed, production-ready enterprise systems.
🔹Agentic AI investment must deliver measurable business value.
🔹Your AI strategy must embed governance, security and compliance.
🔹Infrastructure choices will increasingly impact AI costs and performance.
🔹Data quality will determine the value AI delivers
📌 Report Preview
🌐10 AI Trends Every Business Leader Should Be Watching
What's changing: Single assistants are giving way to policy‑aware, multi‑agent workflows in production with built‑in guardrails, human‑in‑the‑loop, and auditability.
Why it matters: Safer, faster value with explainability and auditability; readiness for EU AI Act; measurable productivity and service‑quality gains.
What's changing: EU AI Act obligations are driving audit‑ready model inventories, risk classification, red‑team gates, and post‑market monitoring aligned to ISO/IEC 42001/NIST AI RMF.
Why it matters: Trust and compliance become operational; faster approvals and fewer incidents under EU AI Act, ISO/IEC 42001 and NIST AI RMF.
What's changing: NPUs and SLMs move everyday inference on‑device with hybrid routing to larger cloud models only when needed.
Why it matters: Lower latency and cost, stronger privacy; favored for sensitive workflows in 2026–2027.
What's changing: Open‑weight and custom models are being deployed in sovereign clouds with data‑residency controls, confidential computing, and strict egress governance.
Why it matters: Meets residency, IP, and regulatory obligations; auditable control accelerates adoption in regulated industries.
What's changing: Power and cooling are gating: liquid‑cooled high‑density racks, siting near clean power, and assured kilowatts shape capacity and costs.
Why it matters: Power is the gating resource; optimizing density and clean energy reduces risk, cost, and carbon exposure.
What's changing: Enterprises are standardizing on stable text‑image‑audio‑video platforms with long‑context; unstable video‑generation APIs are being retired in favor of governed options.
Why it matters: Richer copilots and analytics with measurable ROI; avoid unstable/discontinued video‑generation APIs.
What's changing: Retrieval is shifting to hybrid + rerank, tool‑augmented, self‑checking, graph‑aware pipelines with continuous evaluation of faithfulness.
Why it matters: Higher factuality and explainability with traceable citations; reliable, governable GenAI in production.
What's changing: SOCs are automating detection‑to‑response with agentic workflows; model‑aware monitoring and red‑team gates (OWASP/ATLAS) are go‑live requirements.
Why it matters: Adversaries scale with GenAI; consistent automation cuts risk and time‑to‑containment; SAIF/NIST‑aligned evidence expected.
What's changing: Platforms are adding FinOps “built‑ins”: cost attribution per task, quotas/budgets, and automated model routing by unit cost and quality.
Why it matters: From “try AI” to “prove value”: CFO‑grade visibility links token/GPU spend to outcomes; optimizes cost‑to‑serve.
Turning AI Trends into Business Advantage
What Do These Trends Mean for Businesses?
AI trends only matter if they change what your organization can do. The opportunity now is to look beyond individual technologies and ask where AI can improve performance, create new value and give your business an advantage.
Reimagine Productivity
Productivity
AI agents and increasingly capable GenAI systems can take on more complex tasks, helping teams automate repetitive work and spend more time on higher-value decisions.
The opportunity: Redesign workflows around what humans and AI can each do best.
Find New Paths to Growth
Growth
AI is moving beyond efficiency. Organizations are using it to personalize experiences, accelerate innovation and create new products, services and revenue opportunities.
The opportunity: Identify where AI can create value that wasn't possible before.
Build Trust into AI
Risk & Resilience
As AI becomes more deeply embedded in business-critical processes, governance, cybersecurity and responsible AI become essential to successful adoption.
The opportunity: Build the controls, security and accountability needed to scale AI with confidence.
Prepare Your People
People & Capability
The AI advantage won't come from technology alone. Organizations need the skills, culture and operating models to make human-AI collaboration work.
The opportunity: Invest in AI literacy, specialist skills and new ways of working.
What AI Leaders Are Predicting
Explore expert insights on the latest trends and predictions shaping the future of artificial intelligence.
Discover how AI is driving innovation, transforming industries, and solving global challenges through their visionary perspectives.
Katie Debrah
Founder and Creative Director
Halo and Echo
Agentic AI
Agentic AI is revolutionising content production by handling mundane tasks, but it risks sidelining creatives who don’t adapt. Layoffs are already happening, and those who refuse to upskill may be left behind. My advice: learn the basics, explore agentic AI, and engage with these tools now to elevate your creativity rather than risk being displaced.
Giovanna Martinez Arellano
Assistant Professor in Manufacturing Systems
University of Nottingham
Predictive Maintenance
The biggest AI gains in manufacturing have come from computer vision, especially in predictive maintenance, and this momentum will grow. Vision-based systems are easier to trust than opaque models. To scale AI on the shop floor, we need continual learning systems that adapt to changing environments, work with limited data, and remain reliable as conditions evolve.
Jules Love
Co-Founder and Chief AI Guru
wearespark.ai
AI-Enabled Workflows
Over the next year, we’re moving from enablement to execution—building AI-enabled workflows, testing pricing models, and focusing on relevant trends. The key is driving cultural change to turn AI from pilot projects into everyday performance.
Sourav Das
Senior Platform Product Manager
AP Moeller Maersk
GenAI
AI agents have moved from theory to production, taking actions rather than just offering recommendations. The challenge now is human readiness—teams and users must adapt to the rapid pace of change. GenAI is also transforming B2B workflows, especially in supply chain and procurement, with agents moving from prescribing actions to executing them.
Sudeshna Sen
Data Product Portfolio Manager
Financial Services Compensation Scheme (FSCS)
Trust and Explainability
The priority is trust and explainability. AI has advanced rapidly, and our job is to help leaders understand its risks and failure modes. By building governance structures, we can manage risk while enabling responsible innovation and bringing people along the AI journey.
Joel McKelvy
Head of Production
Glean
Token Yield
The focus is shifting from token consumption to token yield—maximising value per token spent. As AI moves from experimentation to production, organisations are transitioning to operational budgets with clear metrics and ROI. The question is no longer how much AI to deploy, but how to deploy it efficiently for measurable outcomes.