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Copilot and AI agents

Get an overview of how a copilot and AI agents work together to transform business operations across major organisations.
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What is a copilot and what are AI agents?

A copilot is an AI-powered assistant that provides support for tasks, offers insights, and boosts productivity. Agents are specialized AI tools built to handle specific processes or solve business challenges. Think of agents as the apps of the AI era, with the copilot as the interface.

Key takeaways

  • Get an overview of the relationship between a copilot and AI agents.
  • Discover the capabilities of AI agents, including task automation, data analysis, decision-making, and adaptability.
  • Understand the different types of AI agents—and when to use them.
  • Dive into the technology that gives AI agents the ability to communicate, learn, and adapt.
  • See examples of AI agents in action.
  • Get guidance on how to implement AI into your workflows or systems.
  • Learn how AI agents are transforming business operations.
  • Find out what’s in store for the future for AI agents.

How a copilot relates to AI agents

A copilot, such as Microsoft 365 Copilot, is an AI-powered assistant that can help you be more productive and creative by providing real-time support, suggestions, and contextual guidance.

Agents are specialized and can be used with a copilot to perform specific tasks, often with minimal input from the people that use them. They can respond to and resolve user inquiries in real time, or they can operate independently, taking specific actions based on data and predefined goals. They can also run business processes, adapt to new challenges, and improve over time.

If agents are like apps on an AI-powered interface, then a copilot is the interface that allows you to interact with these agents. Microsoft 365 Copilot, for instance, features a constellation of agents, including Microsoft 365 Copilot for Sales, Microsoft 365 Copilot for Service, and Microsoft 365 Copilot for Finance, to help you get things done.

What AI agents can do

AI agents can be applied to many different scenarios across a variety of fields to drive efficiency and innovation. Some of these capabilities include:

  • Task automation: AI agents help streamline repetitive and mundane tasks so that users can focus on solving more meaningful challenges. They can automate customer inquiries through chatbots, manage scheduling, and process transactions, all of which boost productivity.
     
  • Advanced data analysis: AI agents can analyze vast amounts of data quickly and accurately, extracting insights that inform business strategies. They can also process customer feedback, sales data, and market trends to identify behavioral patterns and trends, helping teams make informed decisions based on real-time information.
     
  • Decision-making: By making use of data inputs and algorithms, AI agents can operate independently across many dynamic environments. This includes prioritizing tasks, recommending actions, or even acting autonomously, such as optimizing inventory levels based on sales forecasts.
     
  • Adapting to challenges: Through analyzing and processing user interactions and feedback, AI agents adapt and improve their performance over time. This capability allows AI tools to refine their responses, personalize user experiences, and become more effective in their tasks. For instance, in IT operations, AI agents can learn from historical data to hone and enhance incident response strategies, which can improve resolution times.
To learn more about the capabilities of generative AI, explore Microsoft AI products, solutions, and resources.

Types of AI Agents

Prompt-and-response agents

Prompt-and-response agents are AI tools designed to perform specific tasks based on a user’s input or “prompt.” These agents process the given input and generate a corresponding response, facilitating a back-and-forth exchange with the user. They can be used in various contexts, such as chatbots, virtual assistants, or specialized AI systems for business applications.

Prompt-and-response agents operate based on the type of data they have access to, as well as the set of predefined rules that determine their behavior. They can react quickly to any changes in the data, their rules, or the context in which they operate. 

Because they are meant to simulate natural conversation, they are commonly used in scenarios that require immediate responses to user inquiries or requests, such as customer service, for instance. Prompt-and-response agents are effective for organisations looking to streamline routine interactions and enhance customer support—without adding more complexity to their existing systems.

Task agents

Cognitive agents are designed to mimic human thought processes. They use machine learning and natural language processing to understand, learn, and adapt to user behavior over time, which makes them useful for analyzing historical data and taking action based on their learnings.

Cognitive agents are used in applications such as virtual assistants, like Siri and Alexa, that can learn from user preferences and continuously improve their responses. In businesses, cognitive agents can analyze customer data to deliver tailored recommendations that support more informed decision-making. Using data-driven insights, these agents help organisations create more personalized user experiences, improving customer satisfaction and engagement.

Autonomous agents

Autonomous agents have their own distinct roles and capabilities. While they operate independently as an entity, they’re also able to interact and collaborate with other agents to solve complex problems, optimise larger processes, or achieve a specific goal.

These multi-agent systems are frequently used in logistics and supply chain management, where autonomous agents can dynamically orchestrate tasks such as inventory management, shipment tracking, and resource allocation. They can also be applied to large-scale environments such as smart cities, where multiple agents manage traffic flow, public transportation, and energy distribution while also learning from the data. In the business world, organisations can use autonomous agents to enhance overall efficiency across departments.

How AI agents improve workflows

Enhanced productivity

Organisations around the world are already using AI agents to boost productivity and drive innovation. Healthcare organisations, for example, are using AI agents to assist with patient diagnosis and treatment recommendations. By analyzing vast amounts of medical data and research, AI agents provide doctors with insights that streamline the decision-making process, allowing them to focus more on patient care.

Manufacturing facilities, on the other hand, could use AI agents to optimise operations and reduce overall downtime. For example, agents can optimise inventory levels dynamically, ensuring that popular products are always in stock while reducing inventory costs. And by monitoring equipment health in real time, AI agents can predict maintenance needs before breakdowns even occur. 

Customer service

More and more retail companies have started deploying AI-powered chatbots on their website and in their mobile apps1 to assist customers with product recommendations, bookings, and inquiries. These chatbots instantly respond to customers regarding their orders, payments, and returns, resulting in greater customer satisfaction overall.

In banking, AI agents can manage customer inquiries regarding account balances, transaction history, and loan applications, providing instant assistance to customers whenever it’s needed. And in hospitality, an AI concierge could assist guests with bookings, provide local recommendations, and address concerns in real time. For any customer-facing organisation, an AI agent has the potential to improve customer experience, streamline operations, and generate higher revenue.

Operational efficiency

Tech organisations across a variety of industries are using AI agents to maximize operational efficiency at scale. In warehouses worldwide, AI-powered solutions are picking items and optimizing routes to reduce the time taken for order processing. This automation not only speeds up operations but also lowers labor costs, allowing businesses to maintain their competitive edge.

AI’s potential for operational efficiency is nearly limitless. Consider a construction company deploying AI agents to manage project timelines and resource allocation. An AI agent can analyze weather conditions, workforce availability, and material supplies to adjust project schedules dynamically. In agriculture, an AI agent has the ability to monitor crop health using drones and sensors, providing farmers with real-time data and recommendations for irrigation and pest control. In both scenarios, an AI agent ensures the timely completion of a project, all while reducing costs.

How to get started with AI agents

If you’re looking to start implementing AI agents into your business workflow, consider the following best practices:
 
  1. Identify use cases. First, you’ll want to clearly outline what you want to achieve. Are you looking to optimise your customer service initiatives? Or are you more interested in gaining deeper insights from your data? Start by analyzing your existing workflows to identify specific tasks that can improve with automation or AI assistance.
     
  2. Research and select AI solutions. When researching different AI agents and platforms, consider factors such as functionality, ease of use, scalability, and compatibility with your existing systems. Choose a solution that offers strong security, reliable customer support, and resources to assist with your ongoing needs.
     
  3. Pilot testing. Before moving forward with implementation, conduct a pilot test with a small group of users. This will allow you to assess the AI agent’s performance and collect insights to identify any challenges or areas for improvement.
     
  4. Training and configuration. It’s crucial that you configure your AI agent to align with your specific needs. This may involve setting up workflows, defining user permissions, and customizing responses. It may also mean training the AI agent using historical data to improve its accuracy and effectiveness. During this phase of implementation, pay attention to data privacy and compliance requirements, especially when handling sensitive information.
     
  5. Implementation. During implementation, you’ll want to ensure that your AI agent integrates seamlessly into your existing software, systems, and tools. This may involve using APIs, connectors, or other integration methods. You’ll also want to conduct thorough testing to confirm that the AI agent works well with your existing processes.
     
  6. Monitor and optimise. Once you’re done, set performance metrics, such as response times, customer satisfaction, and task completion rates, to track the AI agent’s effectiveness. Be sure to continuously monitor the AI agent’s performance and adjust as needed based on user feedback and performance data.
     
  7. Scale and expand. Based on the success of your initial deployment, you may want to consider expanding your AI initiatives to other departments or workflows for adoption. This may also involve training or educating your team to ensure that they have the skills needed to use AI effectively. 

Business operations transformed

How agents are transforming business

By significantly reducing manual tasks and facilitating faster, more accurate decision-making, AI agents are revolutionizing business operations. Unlike traditional automation methods, which typically rely on predetermined rules and static workflows, AI agents use machine learning and advanced algorithms to adapt to changing conditions and learn from interactions. This adaptability allows them to handle complex tasks such as customer inquiries, inventory management, and data analysis with greater nuance and efficiency.

Organisations use these tools to augment every aspect of their business, including supply chain operations, finance, customer service, and even sales. In sales, for example, AI agents empower teams by providing them with predictive analytics based on customer data. By identifying high-potential leads, AI agents enhance the decision-making process, giving sales reps the ability to focus on the most promising opportunities.

Organisations that use AI agents often report significant improvements in operational efficiency and cost savings. Global science materials company Dow, for example, recently partnered with Microsoft to use Copilot and agents to transform its freight invoicing system, identify invoice anomalies, and streamline its global shipping operation. Once rolled out across all shipping modes and global locations, this system is expected to potentially save the company millions in shipping costs within the first year.

Discover the value and impact of AI for business leaders

Next step

Whether you’re automating customer service with chatbots or making use of predictive sales analytics, the potential applications for AI agents are vast and varied. AI agents offer a wealth of benefits that can significantly enhance business operations and provide valuable insights for decision-making. By reducing manual workload, these agents allow teams to focus on more strategic initiatives. Their ability to learn from interactions allows them to adapt and improve over time, delivering more personalized experiences and optimised workflows.

As technology continues to evolve, adding AI agents into your operations can not only streamline processes but also provide a robust framework for growth and adaptation—helping your business stay competitive in today’s fast-paced market. 
Resources
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Microsoft 365 Copilot for Work

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Frequently asked questions

  • Start by identifying specific use cases where automation might provide value for your organisation. Next, select an AI solution that aligns with your needs, conduct a pilot test, and gather feedback for refinement. Finally, connect the AI agent to your existing systems, train your team, then continuously monitor performance to optimise impact.
  • Yes. Some AI agents can integrate with existing software and systems through APIs or built-in connectors. When selecting an AI solution, check for compatibility with your existing platforms to ensure seamless integration.
  • To measure the ROI of your AI agent, consider establishing key performance indicators (KPIs) that align with your business objectives, then regularly track these metrics to assess its impact.
  • AI agents improve operational efficiency by automating systems without human oversight, allowing employees to focus on more complex and strategic activities. They can also analyze large datasets quickly, identify patterns and trends that might not be immediately obvious, and provide actionable insights for better forecasting and strategic planning.
  • Some common methods include encryption, access controls, and compliance with industry-specific standards. However, not all AI agents use the same security measures, as these can vary based on the industry, the type of data being handled, and how to agent is used within the organisation. Learn more the responsible AI practices at Microsoft.
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    Availability of mobile apps varies by country/region.