TECH & INNOVATION

By FedEx | March 2, 2026

As AI agents evolve beyond simple task automation, businesses need to rethink how decisions are made – and where humans add the most value. Here’s a look at the trends shaping the next wave of AI, and how your business can prepare for change.

• Interest in agentic AI is growing globally, with 62% of companies experimenting with AI agents.
• Four trends usher in the next era of agentic AI: physical AI, interpretive AI, multi-agent systems, and self-updating AI models.
• Early adopters of agentic AI can benefit by establishing strong data foundations, setting clear guardrails for AI adoption, and experimenting with use cases to build confidence before scaling.

If 2023 and 2024 were the years generative AI found its voice, 2025 was the year agentic AI came into its own. Businesses are now asking a pivotal question: what if software didn’t just respond to tasks, but took action independently?

Agentic AI – which refers to AI systems that can assess situations, act, and learn from outcomes – is quickly moving from research labs into everyday business tools. Around the world, companies are testing what this next wave of agentic AI could mean for operations, customer experience, and product innovation. Early signals suggest strong momentum: 62% of companies say they’re already experimenting with AI agents, even if most are still in pilot mode.

Unlike earlier forms of automation, this shift goes beyond merely speeding up tasks. It enables AI systems to coordinate work, adapt to changing conditions, and support problem-solving within clearly defined boundaries. As we step into 2026, four emerging trends are beginning to shape how businesses of all sizes might use agentic AI next.

1. From software to the streets: Physical AI enters the real world

One of the biggest shifts ahead is the move from digital-only agents to physical AI – intelligent systems that sense, navigate, and act in real-world environments.

Think of autonomous vehicles that adjust routes based on weather or traffic, and warehouse bots that can plan movements without step-by-step instructions. These systems combine AI with robotics, sensors, and edge computing to make decisions in real time.

In Singapore, FedEx has collaborated with agentic AI-powered robotics platform QuikBot to support autonomous last-mile deliveries in high-rise commercial buildings. This collaboration shows how physical AI could reshape logistics in dense urban areas, especially across Asia’s fast-growing cities.

Physical AI is gaining ground across industries. In manufacturing, for instance, adaptive robots are learning to adjust to materials and production flows. In agriculture, robots and AI systems are used to scan crops to monitor health and recommend timely interventions. In retail, autonomous systems move through store aisles to monitor shelf inventory while minimizing disruption to shoppers.

Gradually, these tools are moving from experimental prototypes into everyday operations. For logistics providers, retailers, and manufacturers, physical AI represents a major step toward blending digital intelligence with real-world action, pushing AI-enabled logistics into new territory.

2. The rise of interpretive AI: Systems that understand context

If early generative AI excelled at producing text, images, and code, the next generation shines at interpreting context and intent. Interpretive AI refers to AI systems that can infer nuance and purpose, rather than just relying on rules-based logic. This allows interpretive AI to provide context-aware assistance and make informed decisions.

For example, interpretive AI agents can analyze documents and extract meaning, rather than just keywords, which is useful in fields such as legal research and social work. They can also decide when to take action and when to escalate issues to a human agent, adding value in customer-facing or high-stakes environments like finance and healthcare.

For example, in customer service, interpretive AI can help you review customer conversations and distinguish urgent issues from routine queries. In logistics, it can assess supply chain disruptions by combining weather signals, location data, and inventory levels.

Businesses are also increasingly combining interpretive AI with real-time operational data to strengthen decision-making. As these AI systems get better understanding of operational and customer context, the distinction between assistant and colleague begins to blur, allowing AI to play a more integrated role in daily workflows.

3. AI working together: A society of multi-agent systems

Another rising trend is the emergence of societies of AI, where multiple AI agents collaborate to solve complex tasks. Instead of relying on a single large model, businesses can deploy teams of specialized AI agents that “talk” to one another and divide work in ways similar to human teams.

In software development, for example, one AI agent writes code, another tests it, and a third checks for security compliance. In supply chains, planning agents may forecast demand while monitoring agents track the real-time movement of goods and recommend adjustments.

This approach is already starting to pay off for some. One enterprise cut its invoice processing time from two days to just 20 minutes by coordinating a team of five specialized AI agents. This shows how multi-agent AI systems can dramatically reduce manual work when tasks involve multiple steps and documents.

Even large platforms are experimenting with this architecture. LinkedIn, for instance, built an enterprise multi-agent AI system on top of its existing messaging infrastructure, enabling its Hiring Assistant, the company’s first AI-powered recruiting agent, to scale globally.

4. Designed for continuous improvement: Self-updating AI systems

Finally, we’re entering an era where AI doesn’t just perform tasks. It improves itself over time. Rather than operating autonomously, these systems evolve through structured feedback loops, refining their outputs based on data, human evaluation, and operational context.

New research shows that large language models (LLMs) can fine-tune themselves based on task performance, allowing them to adapt without waiting for a full retraining cycle. However, these techniques remain early-stage and computationally intensive. In practice, most organizations focus on supervised learning approaches, where AI systems improve through monitored performance data and human-in-the-loop evaluation.

In fast-moving sectors like finance, AI systems can support adjustments to regulatory changes, but only within clearly defined guardrails. Similarly, automation tools may optimize workflows or flag inefficiencies, yet human oversight remains essential to manage drift, bias, and unintended consequences.

As these capabilities evolve, strong data governance becomes even more critical. Businesses that build robust feedback mechanisms and accountability structures will unlock scalable, adaptive systems without compromising safety or control.

How your business can prepare for agentic AI today

Agentic AI is advancing quickly, but you don’t need to overhaul your operations overnight. The most resilient businesses take small, deliberate steps to build their readiness and capabilities. Here are five practical ways to get started:

1. Start with a single high-impact workflow

Identify a process that’s repetitive but requires coordination or small decisions. Many businesses start with customer support, invoice processing, or inventory planning. These tasks benefit from AI workflow automation while providing enough decision-making complexity for an AI agent to demonstrate value. Test one AI agent, measure results, and expand from there.

2. Treat AI agents like team members

As agentic AI becomes more autonomous, companies are learning to manage AI agents like new employees. This means setting clear expectations, defining boundaries, and establishing escalation rules, so that the AI agent knows when to hand off decisions to a human.

3. Build a clear data foundation

Agentic AI learns from real-world signals, which means it depends on reliable data. Clean, well-structured data helps AI agents make accurate decisions, recognize patterns, and learn more effectively. If data is inconsistent or siloed, even advanced AI systems will struggle. Many organizations start by consolidating data sources, aligning naming conventions across teams, and improving documentation.

4. Prioritize safe and responsible AI adoption

Responsible AI frameworks emphasize human oversight and transparent decision-making. A common approach is to implement “human-in-the-loop” mechanisms that define when AI can act independently and when it must escalate decisions to a human.

This structure reinforces a critical principle: AI systems are designed to empower human expertise, not replace it. By maintaining human accountability alongside technical advancement, organizations prevent errors from compounding and ensure teams retain control over sensitive or high-impact processes.

5. Encourage experimentation

Businesses that give teams the freedom to explore use cases, test simple tools, and learn from low-risk experiments tend to build AI capabilities faster. Even lightweight tools like automated meeting summarizers help to show employees what agentic AI can do. These early wins build internal momentum and make it easier to justify more advanced deployments later.

The next chapter for agentic AI

Agentic AI is evolving to support coordinated task execution and informed decision-making, helping businesses improve operational efficiency and customer experience when deployed responsibly. While the technology is still evolving, the opportunity is clear: businesses that stay curious, experiment early, and strengthen their data foundations will be well-positioned to capitalize on what comes next.