AI Copilots are revolutionizing the workplace, making teams more efficient, productive, and creative in virtually every task. Our research found that companies following the best practices for AI copilots and Copilot implementation benefit from improved communication and collaboration, reduced repetitive tasks, and enhanced feelings of inclusivity among teams.
But implementing intelligent tools into your workflows isn’t a walk in the park. There are plenty of challenges to overcome, from issues with employee resistance to integration challenges. So, how do you access the benefits of AI copilots while side-stepping the headaches?
The simple answer is to develop the right strategy – one that focuses on careful implementation, intelligent training strategies, and ongoing process optimization.
Best Practices for AI Copilots: The Implementation Guide
AI copilots, from pre-built solutions integrated into business systems like Google Workspace (Gemini) and Microsoft Teams (Copilot) to custom apps, are reshaping everyday workflows. They’re empowering teams to collaborate more efficiently, make data-driven decisions, and stay ahead of the competition. The key to making the most of these tools is a strategic implementation plan.
Here’s how you can get started.
- Discover the best AI copilots success stories with our guide here.
Initial Implementation: Integrating Copilots into Workflows
First, you’ll need to figure out where AI copilots can have the biggest impact on your team. Start by breaking down each workflow – where do bottlenecks usually crop up? Which tasks eat up the most valuable time?
A customer support team, for instance, might outline the journey from the moment a user submits a ticket to the initial resolution. This map reveals where an AI copilot could automate repetitive tasks, prioritize urgent issues, or deliver real-time insights.
Don’t feel pressured to deploy AI across your entire organization at once. Launch pilot programs within a select department or two. Keep the scope narrow and focused on a handful of tasks. Let those teams gather and share quick wins: such as a 20% cut in data-entry time or a surge in positive customer reviews.
Remember, AI copilots don’t have to be one-size-fits-all. Many vendors offer solutions that help you to customize your models with your own data. For instance, there’s Copilot Studio for Microsoft’s AI assistants, and OpenAI allows users to create hundreds or thousands of custom GPTs at scale. Be ready to customize your tools based on the goals and outcomes you want to achieve.
- Find out everything you’ll ever need to know about AI copilots and assistants in UC in our comprehensive guide here!
Choosing the Right AI Copilot: Considerations
Choosing the right AI copilot can be complicated. There are a lot of different options available, from those pre-integrated into specific solutions like Microsoft, Google, or Zoom workplaces, to dedicated standalone systems that can integrate with existing technology stacks.
If you’re following the best practices for AI copilots, the first thing to focus on is compatibility and integration. For example, if you’re already using Microsoft Teams and Microsoft Office tools for everyday work, Copilot might be your best bet. If you rely heavily on Salesforce and Slack, you might use tools like Einstein or create your own AI agents with Agentforce.
Make sure you can customize each copilot based on your employee’s requirements. Even if you’re using a pre-built model, you should be able to implement your own data and set your own guardrails to reduce the risk of ethical issues. Other things to focus on include:
- Ease of Use: An AI copilot should make life easier for employees and users. Look for straightforward interfaces, intuitive dashboards, and accessible tutorials. If your finance team needs two weeks of training just to understand the basics, you risk slower adoption.
- Security, Privacy, and Ethics: AI copilots often handle sensitive data. So, you need robust guardrails, access controls, and ways to protect your data. Look for a solution built to meet enterprise needs and compliance standards.
- Scalability: Ensure your AI copilot can scale with your teams, supporting new users and larger volumes of data over time. Make sure you can create new workflows as necessary, and even design specific copilots for certain tasks.
Best Practices for AI Copilots: Ongoing Training and Development
When it comes to following the best practices for AI copilots, a focus on constant training is a must. After all, Copilots are constantly evolving with new language models and capabilities. We’re even seeing the rise of agentic AI solutions that can combine the features of multiple copilots.
Build a culture of continuous learning to go beyond the basics of initial onboarding and training strategies. Host weekly meetings where teams can share their thoughts on improving copilot performance. Launch micro-training sessions every time you embed a new feature into your tools.
Invest in cross-functional collaboration strategies that help teams from multiple departments make the most of various AI solutions and features. You could even invite your team members to participate in online workshops and webinars hosted by your AI vendor.
Keep everyone focused on constant learning and growth, and remember to maintain an open feedback loop so you can personalize training and adapt copilots to specific user needs.
Measuring AI Copilot Impact: Metrics to Monitor
If you want to improve the ROI of your copilot solution, you need to track whether your systems are delivering the right results. You can monitor plenty of different metrics and KPIs based on the goals you want to achieve. For instance, you might track:
- Reduction in Task Completion Time: Does your team wrap up projects faster? Compare new task durations with pre-AI benchmarks. Softchoice saw a 97 percent reduction in time spent summarizing technical meetings after adopting Microsoft 365 Copilot.
- Customer Satisfaction Scores: Real-time AI support can lead to quicker resolutions and more personalized experiences, boosting CSAT metrics.
- Employee Feedback: Survey your workforce about ease of workflow and AI adoption. GitHub found that 90 percent of developers felt more fulfilled using GitHub Copilot.
Take advantage of real-time dashboards to track these metrics, as well as historical reports and direct feedback from team members. Don’t forget to look beyond the numbers too. Find out whether your copilots are leading to more engagement amongst teams, reduced burnout, or more loyal customers.
At The Ottawa Hospital, for instance, implementing the Dragon Ambient eXperience (DAX), Copilot gave physicians back hours in their day for patient care. That meant team members spent more time on fulfilling tasks, and patients got more personalized support.
- Curious about which AI copilot best suits your business use cases? Find out here.
Best Practices for AI Copilots: Continuous Improvement
Another thing to keep in mind when you’re implementing best practices for AI copilots is that the world of artificial intelligence moves fast. Today’s copilots are already introducing multimodal capabilities, broader integrations with more tech tools, and stronger language models.
As mentioned above, many companies are even building on AI copilot solutions with agentic AI systems. Make sure you stay in the loop with the latest updates. Keep a close eye on the latest news from your chosen vendor, or attend AI-focused events and webinars.
Host regular review sessions with teams where you can discuss wins, struggles, and opportunities offered by upcoming features. Constantly look for ways to improve your strategy with enhancements to training data or by scaling your copilots into new workflows for different teams.
Remember, IBM found that by implementing incremental improvements, most companies saw a 12 percent improvement in productivity gains from copilot assistants.
- Interested to know how AI copilots are transforming comms workflows? We detail five key ways here.
Following the Best Practices for AI Copilots
Implementing AI copilots into your organization isn’t as simple as just choosing a tool and rolling it out for employees. You need a comprehensive action plan to drive adoption, ensure you achieve the right results, and keep employees moving forward in the AI age.
The good news is that companies that follow the best practices for AI copilots achieve a higher return on investment faster while empowering teams and delighting customers.
