The company wanted to roll Copilot out to every department within a year, so a broad impact with quick wins was the best path forward. They invested in Microsoft 365 Copilot and Copilot Studio to gain buy-ins internally from key stakeholders. Now that they have good momentum, they’re well poised to make a larger investment to roll out the tools to everyone. Later, they’ll be able to make further investments in functional scenario-based Copilots to make more targeted impacts.

Bring your own model and deploy efficiently across silicon – securely and locally on Windows

Intel’s EP combines OpenVINO AI software performance and efficiency with Windows ML, empowering AI developers to easily choose the optimal XPU (CPU, GPU or NPU) for their AI workloads on Intel Core Ultra processor powered PCs. Early results are already validating that promise. In a randomized study of 6,000 employees across 56 firms, Copilot users spent 30 fewer minutes on email each week, finished documents 12% faster, and nearly 40% used the tool regularly over six months. What are your business motivators for investing in AI? Before you decide what Copilot to convention of conservatism start with, you need to define your AI vision.

We previously worked with app developers to test the integration with Windows ML during public preview. AMD has integrated Windows ML support across their Ryzen AI platform, enabling developers to harness the power of AMD silicon via AMD’s dedicated Vitis AI execution provider on NPU, GPU and CPU. If your organization is already leveraging the Microsoft ecosystem, the real question isn’t whether to adopt Copilot—it’s how to do it in a way that maximizes ROI. With more than a dozen Copilot offerings across Microsoft 365, Dynamics, Power Platform, GitHub and beyond, companies need a clear plan to identify the right entry points, prioritize high-impact use cases, and manage change effectively.

Your favorite productivity apps

AI assistants and agents aren’t the future of work—they’re already here. Microsoft Copilot in particular is already embedded in the fabric of global business. Nearly 70% of the Fortune 500 use Microsoft 365 Copilot, and Barclays has rolled out more than 100,000 seats with plans to scale further.

Create an image

Windows ML is compatible with ONNX Runtime (ORT), allowing developers to utilize familiar ORT APIs and enabling easy transition for existing production workloads. Windows handles distribution and maintenance of ORT and the Execution Providers, taking that responsibility on from the App Developer. Execution Providers (EPs) are the bridge between the core runtime and the powerful and diverse silicon ecosystem, enabling independent optimization of model execution on the different chips from AMD, Intel, NVIDIA and Qualcomm. With ONNX as its model format, Windows ML integrates smoothly with current models and workflows. Developers can easily use their existing ONNX models or convert and optimize their source PyTorch models through the AI Toolkit for VS Code and deploy across Windows 11 PCs. Companies just getting started with Copilots should aim to make a broad impact to score some quick wins and drive further AI investment and internal buy-in.

Getting Started With Microsoft Copilot: A Guide To Driving ROI

• Microsoft Copilot in particular is already embedded in the fabric of global business.
• With ONNX as its model format, Windows ML integrates smoothly with current models and workflows.
• The answer to this question will anchor your Copilot strategy in a clear business case.
• These features make it easier to prepare and deploy efficient models with Windows ML, eliminating the need for multiple builds and complex logic.

Is your goal to stay competitive and relevant? Are you wanting to improve efficiency or optimize costs? The answer to this question will anchor your Copilot strategy in a clear business case. So if you’ve found yourself stressing over getting started with Microsoft Copilot, don’t ask “Which copilot should we buy? ” Instead, ask “What problem are we solving, and what level of impact do we need? ” That shift in thinking is how organizations will turn Copilot from an expensive experiment into a lasting competitive advantage.

A Simple Framework For Getting Started With Microsoft Copilot

This ability to run models locally enables developers to build AI experiences that are more responsive, private and cost-effective, reaching users across the broadest range of Windows hardware. Developers can take advantage of Windows ML by starting with a robust set of tools for simplified model deployment. AI Toolkit for VS Code provides powerful tools for model and app preparation, including ONNX conversion from PyTorch, quantization, optimization, compilation and evaluation – all in one place. These features make it easier to prepare and deploy efficient models with Windows ML, eliminating the need for multiple builds and complex logic. Starting today, developers can also try custom AI models with Windows ML in AI Dev Gallery, which offers an interactive workspace to make it easier to discover and experiment AI-powered scenarios using local models.

How are you going to measure success and drive adoption? Of course, selecting and buying a Copilot tool is only one step of AI success. Before you make any investment in Copilot, it is important to identify key use cases for how you plan to leverage Copilot in the organization and identify metrics to track success. For example, one use case could be improving overall productivity of the team when preparing for a meeting or streamlining the process for reviewing contracts. While developing Windows ML, we prioritized feedback from app developers building AI-powered features.

Develop local AI solutions with Windows ML

In this scenario, a rapid deployment of one of the core Microsoft Copilot options with low-risk, high-visibility use cases is ideal. Microsoft Copilot 365, for example, allows you to introduce some simple automation and build entry-level agents. We worked closely with our silicon partners to ensure that Windows ML can fully leverage their latest CPUs, GPUs and NPUs for AI workloads. Today we are excited to share that Windows ML is now generally available for production use to assist developers with deploying production experiences in the evolving AI landscape. For example, my company recently worked with a 300-person insurance company on Copilot integration.

Should you start small to get buy-in or go big to leap ahead of the competition? The answer largely depends on where your organization is in its AI journey. Experience the power of AI built into the tools you use every day. Find the tools that help you manage your everyday work and life in one place. Meet the Microsoft 365 Copilot app—your new home for Word, Excel, PowerPoint, Outlook, and more. Your favorite productivity apps, now with the power of AI for home, work, and school.

• Should you start small to get buy-in or go big to leap ahead of the competition?
• Of course, selecting and buying a Copilot tool is only one step of AI success.
• Developers can take advantage of Windows ML by starting with a robust set of tools for simplified model deployment.
• Meet the Microsoft 365 Copilot app—your new home for Word, Excel, PowerPoint, Outlook, and more.

Companies that have successfully integrated foundational AI tools (like Microsoft 365 Copilot) can aim for a targeted, high impact with Copilot. To do so, you’ll need to identify a specific problem or business function to address. This is ideal for organizations with a clear AI vision, with teams ready to tackle cultural, data and infrastructure shifts, and use cases with measurable, strategic outcomes. This path forward requires a high level of investment in one of the more complex Copilot offerings. With Windows ML now generally available, Windows 11 provides a local AI inference framework that’s ready for production apps.

On the developer side, GitHub Copilot now has more than 20 million users and is in use at over 90% of Fortune 100 companies. Windows 11 has a diverse hardware ecosystem that includes AMD, Intel, NVIDIA and Qualcomm and spans the CPU, GPU and NPU. Consumers can choose from a range of Windows PCs and this variety empowers developers to create innovative local AI experiences. Read on for a guide to help you sail smoothly through the Copilot waters and build a practical, strategic Copilot roadmap to make smart AI choices, drive employee engagement and—most importantly—deliver AI business outcomes. NVIDIA’s TensorRT for RTX EP enables AI models to be executed on NVIDIA GeForce RTX and RTX PRO GPUs using NVIDIA’s dedicated Tensor Core libraries for maximum performance. This lightweight EP generates optimized inference engines — instructions on how to run the AI model — for the system’s specific RTX GPU.

Windows ML is included in the Windows App SDK (starting with version 1.8.1) and supports all devices running Windows 11 24H2 or newer. The future of AI is hybrid, utilizing the respective strengths of cloud and client while harnessing every Windows device to achieve more. At Microsoft, we are reimagining what’s possible by bringing powerful AI compute directly to Windows devices, unlocking a new era of intelligence that runs where you are. With groundbreaking advancements in silicon, a modernized software stack and deep OS integration, Windows 11 is transforming into the world’s most open and capable platform for local AI.