A new technology brings a new language—here’s a simple set of definitions to get you up to speed:

• Copilot: An AI assistant that responds to people’s prompts and requests.
• Agent: A software entity that can make decisions and act on information in their specific domain of expertise, by using custom-made or self-generated tools.
• Multi-agent AI: Multiple agents that act on behalf of people and are empowered to make autonomous decisions and work collectively to complete tasks.

New ways of working—what sets AI agents apart?

• Autonomy and proactivity: AI agents can operate independently once set up. They proactively complete tasks, make decisions, or alert you to anomalies without needing constant prompts or input.
• Specialization vs. generalization: Unlike generalist tools like copilots or chatbots, AI agents are specialists. They can be trained and fine-tuned for specific tasks, ensuring deep domain expertise that matches your organization’s unique needs.
• Integration with workflows and systems: AI agents are deeply embedded into workflows, connecting seamlessly across tools and systems. They adapt and become part of your processes, without disrupting how teams work.
• Learning and adaptability: Agents continuously learn from employees’ interactions and refine their behavior to align with your organization’s preferences and evolving workflows.
• Task execution and ownership: AI agents take full ownership of tasks within their scope, executing them end-to-end without needing employees to micromanage every step.
• Context awareness: AI agents understand and respond to the context of your work, adapting dynamically to changes in priorities or external factors.
• Decision-making and reasoning: They can make informed decisions based on real-time data and employee-defined parameters, enabling them to act autonomously while maintaining accountability.
• Efficiency gains through delegation: By automating repetitive or time-consuming tasks, AI agents let you delegate effectively, freeing your people to focus on higher-value work.

Leaders are already factoring in how to measure value from gen AI, with 28% measuring the contributions of AI capabilities like Microsoft Copilot, and 53% planning to do so in the next 12 months. The dominant value drivers are productivity and operational improvements, with 69% using these to measure value.

Looking ahead, agentic AI has the potential to redefine the productivity equation as we know it. Instead of shaving time off universal tasks—drafting documents, creating content, writing emails—agents will empower us to automate and reinvent entire processes.

The possibilities are both infinite and profound. Imagine a world where organizations are supported by a network of AI agents that never sleep, continuously learn, and can predict, diagnose or plan with insights from unfathomable volumes of data.

Frontline workers could rely on AI systems to protect them in physical work environments by spotting when machinery goes wrong, or by undertaking diagnostics in conditions which are inaccessible or unsafe for human employees.

Healthcare providers could offer round-the-clock patient assistance and advanced health monitoring by analyzing vast volumes of medical data.

Contact centers would never shut, as agentic AI systems handle limitless call volumes, make context-based decisions and intelligently carry out next best actions without the need for human intervention.

Supply chain and logistics would enjoy next-level inventory management, demand forecasting and planning capabilities which can react and re-route around unforeseen supplier disruptions

Software development could be revolutionized by agents that can write code, manage development lifecycles and test software updates.

Sales teams can leave AI agents to fulfill entire back-office processes while focusing on new ways to add value to customers.

AI is already proving its worth in the workplace. A striking 96% are using it to make decisions, with nearly a third (29%) trusting it to make potentially high-risk decisions. As the role of AI expands, we anticipate a further shift in workforce dynamics. Instead of a passive tool waiting for prompts, AI agents will serve as proactive team members.

This isn’t a question of human versus AI. Instead, it’s about learning how to combine our unique strengths to work together. In turn, this will increase demand for human AI literacy and technical expertise. While some tasks may disappear from our to-do lists, new roles will emerge to develop, train and oversee the use of AI.

Humans have always been able to learn, adapt and evolve and it’s these innate skills which led us to create technology that can grow our collective intelligence. Organizations that can harness this as a force multiplier will be able to act more intelligently than any single person, group, or computer.

AI agents spark new questions, wider implications

This new dynamic will profoundly change what work looks like with humans and technology. From agents’ data access to risk tolerance and when humans should be in the loop (or not) – there are many considerations to make.

For example, we are witnessing the rise of agents at the same time as a new type of developer emerges, where anyone can create copilot and agent-infused apps. Leaders should question what “apps” really are – including where app boundaries are, whether or not every app needs a copilot and who is building the apps. This may entail eliminating silos between professional developers and app-building non-developers, enabling them with the data they need, and determining who is best to build a given app.

Every organization will need an agentic layer to support people and AI working together – weaving together and enabling data governance and security, copilot and AI use cases, agent orchestration, and industry workflow reinvention. This can help to avoid an explosion of agents that lack security and governance, don’t have the right data access, and don’t communicate with each other.

A new type of management will be required, too: leaders will need to manage both people and the agents. Human employees will need to demonstrate resilience and adaptability through this shift. Meanwhile, the AI needs to adapt to evolving environments including data availability, plus learning new skills and applying those learned skills. There will be a lot to navigate along the AI journey.

Our research shows an impatience to move fast with AI—85% worry they risk falling behind—but successful adoption is not as simple as turning a switch on technology. Here are the key considerations we recommend you prioritize now:

• Your organization’s strategy is your AI strategy. While agents expand the possibilities of AI by acting autonomously, their goals and behaviors need to sit within the parameters of your strategy, values and principles—in the same way we expect of human employees. If your organization is one of the 30% still crafting a visionary AI strategy, we recommend you start here. This will give you a ‘north star’ to decide where to adopt technology, how to train your people and which tasks or processes agentic AI should handle.
• Decide how much ‘agency’ agentic AI should have. Despite most using AI to make decisions, only 26% fully trust the results and just 39% have a complete set of responsible AI guidelines in place. This exposes an urgent need for leaders to agree the right level of risk tolerance for their organizations. If agents drive more workflows, and employees make fewer choices, how will you set thresholds for human intervention? With this will come the need to continuously train and fine tune AI agents so that they ‘forget’ and unlearn misguided or unethical facts.
• Prioritize the human factor of AI adoption. With only 33% very confident that their leadership can reliably differentiate between AI and human-generated work, how will you measure the contributions of AI compared with human employees? The impact of what makes us human at work—our desire for belonging, engagement and purpose—should also not be underestimated. It’s clear we have work to do, as only 40% feel very confident in the ability of leaders to manage the social and emotional aspects of working with AI.
• Data still matters for agentic AI. Copilots and agentic AI share the same data foundation—large language models (LLMs). A robust data strategy—which defines how data is collected, stored, managed, analyzed, and utilized—is essential for AI success. The combination of productionized tools, such as Microsoft Azure AI Foundry, with the security and governance rules and principles of platforms like Microsoft Purview, is an important piece of the puzzle. By securely bringing together data from multiple sources these tools will create a standardized architecture that can accelerate the time it takes to create and scale new agents.

To get more insights into AI value for 2025, and what this means for you, download our latest research Avanade Trendlines: AI Value Report 2025