By
Lara | Community Champion | Administrator |
Introduction: What’s Next in AI?
As we move into 2026, the conversation about AI among business leaders is changing very quickly. It’s gone from asking, "What can AI do?" to "How deeply can AI run the business?"
Companies are moving beyond using AI as a support tool to employing AI systems at scale that can plan tasks, execute workflows, and optimize outcomes—with humans still steering the core business strategy. This evolution is driven by tighter margins, higher customer expectations, complex global operations, and the need for faster, better decisions.
AI is no longer about chatbots or isolated automation victories. It’s about systems that act, coordinate, and augment human decision-making at scale.
In simple terms, what’s next in AI is all about being autonomous with accountability.
To understand this better, let’s break down the top 3 AI trends in 2026 every business leader must understand. Starting with trend number one:
Trend 1: Agentic AI
According to a Fortune Business Insights report, the agentic AI market is valued at USD 7.29 billion in 2025 and is expected to surge to USD 139.19 billion by 2034.
Agentic AI is an autonomous system that perceives its environment, plans actions to solve a problem, and monitors its success or failure. These systems don’t wait for instructions; they plan actions, carry out tasks, and change workflows across different tools with minimal human intervention.
AI agents are a specific subset of generative AI that primarily focus on orchestration capabilities instead of just simple automation.
For example, if you want to resolve the hassle of flight cancellation, it typically involves a long wait to speak on the phone with the airline or a customer service agent at the airport.
However, with AI agents involved, you can now get:
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Immediate notification of flight cancellation
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Multiple alternative flight options with their respective time
Once you select your preferred option, the agent will automatically:
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Book the chosen option
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Assign you a seat number
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Update the booking record, and
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Send a final email confirmation
The result?
Everything got done, without the need to loop in a customer service agent even once.
According to Gartner, the integration of task-specific AI agents into enterprise applications is set for a dramatic increase. Currently, this method integrates less than 5% of applications, but Gartner projects a surge to 40% by the end of 2026.
Business leaders must view agentic AI as their new digital workforce. However, to harness its full potential, a shift in AI governance, KPI setting, and accountability structure is mandatory.
The next trend in line is…
Trend 2: Large Action Models (LAMs)
If agentic AI represents the "what," then Large Action Models (LAMs) represent the "how."
In simple words, LAM serves as the "brain," responsible for planning and executing actions, while the AI agents act as the "body" that carries out those tasks.
While large language models (LLMs) are limited to text generation, LAMs are designed to leverage deep thinking and align required actions—such as clicking, updating systems, and triggering workflows—to achieve a specific business objective.
Gartner research indicates that LAMs significantly improve agentic AI by enabling automated, efficient, and personalized interactions across various industries and departments.
For example, Human Resources (HR) agents can handle the complete employee onboarding process, including candidate screening, assignment evaluation, conducting interviews, shortlisting, and training.
IT Operations agents can detect incidents, auto-log into systems, apply fixes, restart services, and notify teams.
Procurement agents can analyze demand, place orders accordingly, negotiate pricing thresholds, and update ERP systems in real time.
For business leaders, the message is clear: LAMs transform AI into an operational layer rather than a mere support function. This shift, especially when paired with agentic AI, enables organizations to significantly expand execution capabilities without scaling up their headcount.
Last but not the least…
Trend 3: End-to-End Orchestration (E2E)
When agentic AI and LAMs are executed together, end-to-end orchestration (E2E) becomes the channel that brings everything together.
In 2026, the most successful organizations are moving beyond isolated AI initiatives. They are now integrating data, models, agents, workflows, and governance into a unified, enterprise system.
According to a research report by Zapier, 25% of business leaders anticipate reaching full-scale orchestration by 2026.
End-to-end orchestration ensures that all AI agents work together smoothly toward the company’s goals, rather than just doing separate tasks on their own or without any central control.
To understand full-scale orchestration better, here’s an example:
Let’s say a global supply chain company uses an end-to-end AI orchestration layer to seamlessly perform demand forecasting, procurement, logistics, finance, and executive reporting via agents.
But when demand spikes, the system triggers AI agents to handle procurement (assessing suppliers/pricing) and logistics (adjusting routes based on congestion), while finance agents recalculate margins and cash flow.
Simultaneously, the system updates leadership reports in real time, highlighting risk exposure and suggesting potential trade-offs.
That’s how the orchestrated system handles disruptions by coordinating cross-functional agents, enforcing governance, logging actions, and escalating only strategic choices to humans.
The Final Note
AI trends in 2026 are no longer about experimenting with chatbots—they’re about redesigning how businesses operate.
Agentic AI acts as an autonomous digital worker, and LAMs act as its brain. When orchestrated end-to-end, this system moves AI beyond an assistant to become the enterprise’s new operational layer.
However, the takeaway for business leaders is not merely fast AI adoption but smart adoption.
This involves three essential steps: establishing clear accountability, redesigning workflows, and ensuring that human oversight maintains control over strategy while AI handles scaled execution. Mastering this strategic balance provides organizations a competitive edge, boosting speed and efficiency, while giving them a competitive edge for the coming decade.
