AI is entering a new phase defined by innovations in agentic systems, multimodal interfaces, and a growing emphasis on governance and ethics. As organizations prepare for 2026, AI is expected to drive hyper-personalization, deeper automation, and even emotional intelligence in future digital systems.
These development trends are reshaping how businesses operate, market, and make decisions—while forcing renewed attention on responsible development and oversight.
Introduction to AI trends for 2026
AI will continue to move from supporting individuals to enabling organizational intelligence. One of the most significant shifts will be in how AI integrates into shared, real-time environments, where decisions are made and culture is shaped.
Oliver Van Camp, Director of Meeting Room Experiences at Barco ClickShare, says as AI tools evolve from static assistants to real-time facilitators, they’ll help reduce friction, interpret human dynamics, and surface actionable data insights across teams and systems.
“The true impact won’t come from novelty, but from how well AI adapts to operational complexity and accelerates alignment, collaboration, and business outcomes.” — Van Camp
This will drive more adaptive environments, where automation is context-aware and decisions are shaped by real-time understanding of how teams work.
“As AI matures, it will play a central role in helping organizations streamline complexity, reduce friction, and align technology more closely with human behavior and business goals.” — Van Camp
Ethical considerations in AI governance
Derek Ashmore, Agentic AI Enablement Principal at Asperitas, cautions ethical AI can’t be bolted on after deployment; it must be engineered in from the start.
“That means building governance as code and treating transparency the same way we treat reliability or security: measurable, testable, and automated,” he says.
Ashmore says practically, companies should start with three layers.
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Policy layer
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Establish clear, enterprise-wide policies around data provenance, model usage, and human oversight.
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Every model or agent should have an accountable owner and a traceable chain of custody, including where data came from, how it was trained, and what decisions it influences.
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Process layer
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Embed governance in the SDLC.
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Integrate ethical checkpoints into CI/CD pipelines, just like security scans.
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This can include automated bias testing, model explainability checks, and mandatory human-in-the-loop validation for high-impact workflows.
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For agentic AI, that means explicitly defining and logging each agent’s scope and autonomy.
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Transparency layer
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Make decisions observable via audit trails of prompts, data inputs, and outputs.
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Provide model cards and documentation that are version-controlled and accessible.
“When users can see why an AI made a decision, trust follows naturally.” — Ashmore
Mike Blandina, CISO for Snowflake, explains that responsible AI practices will soon be non-negotiable.
“CIOs won’t just be asked how they’re adopting AI — they’ll be held accountable for ensuring models are transparent, explainable, and free from harmful bias,” he says.
Governance frameworks will need to span the entire AI lifecycle—from data sourcing to model training to deployment and ongoing monitoring—with clear lines of ownership, regular audits, and documented risk assessments.
“This mandate will expand the CIO’s role beyond technology deployment into ethics, trust, and risk management, making responsible AI a central part of any CIO’s agenda,” Blandina says.
Marketing and business strategies leveraging AI
Ashmore explains AI-driven analytics are transforming marketing from retrospective reporting into real-time understanding.
The real power comes when predictive models and agentic systems continuously learn from every customer interaction, not just summarizing what happened, but anticipating what will resonate next.
How AI enhances marketing
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Predictive modeling helps marketers move beyond segmentation toward individualized intent prediction.
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Instead of demographic grouping, AI models detect subtle behavioral patterns across text, voice, and image data to understand why customers act.
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AI agents automate experimentation by running thousands of micro-tests in parallel, adjusting creative, tone, and channels in real time.
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Closed-loop intelligence emerges when analytics are integrated into CRM and automation systems, so strategy and execution constantly refine one another.
“That’s how campaigns evolve dynamically, not through manual iteration,” Ashmore says.
Human marketers, he explains, move up-stack to strategy, brand voice, and ethics, while AI handles scale and speed.
“The goal isn’t to replace marketing judgment, it’s to amplify it.” — Ashmore
AI gives teams the ability to see customers as moving patterns, not static profiles, and to act on insight while it still matters.
Predictions for AI development and future trends
Tiago Henriques, chief underwriting officer at Coalition, says highly networked, data-driven sectors—including healthcare, financial services, and critical infrastructure—are poised for both acceleration and disruption.
“These industries operate within vast digital ecosystems where AI can simultaneously enhance efficiency and expose new dependencies,” he says.
Recent large-scale incidents have shown how quickly disruptions can ripple through supply chains when connected systems fail. As AI strengthens detection, prediction, and automated coordination, it will redefine resilience and operational continuity at enterprise scale.
Van Camp says AI is becoming a foundational layer in how modern workplaces function.
“We should expect a dual-layer approach where some intelligence operates close to where interactions happen, enabling immediate responsiveness, while deeper insights are drawn from centralized systems built to scale,” he says.
Key AI takeaways for 2026
+ AI becomes organizational, not individual
+ Governance must be built in, not bolted on
+ Agentic systems transform marketing and operations
+ Transparency and explainability become CIO-level mandates
+ Real-time, context-aware AI reshapes resilience and workflows
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