A recent survey by JP Morgan found that 86% of Australian businesses use or want to use AI in their business. 

Microsoft Copilot also became generally available to organisations of all sizes in January 2024, making generative AI capabilities even more accessible. If you are among those leaders wishing to leverage Copilot, you cannot simply 'switch on' this tool, you need a strategy for implementing it. 

Microsoft Copilot integrates advanced AI with Microsoft 365 and supports various productivity applications, including Word, Excel, PowerPoint, and Outlook. However, Copilot’s success hinges on a well-devised implementation plan, including a phased rollout and getting your data under control. Without these approaches, you may struggle to see Copilot’s value.

Source: JP Morgan.

Refine your data

Your organisation will need to evaluate any existing data before deploying Microsoft Copilot. Poor data quality—characterised by outdated, incomplete, or inconsistent information—can undermine Copilot's effectiveness. Before rolling out Copilot, it's best to undergo data audits to pinpoint and resolve quality issues and implement data management practices to uphold integrity.

Ensuring that Copilot uses updated and clean data enhances outputs and means your team has reliable and actionable insights available. Imagine an Australian retail company using Copilot to analyse customer feedback and sales data to generate targeted marketing strategies. If Copilot only has access to outdated or incomplete data, it might fail to address current market trends or customer concerns, leading to missed opportunities and potential revenue loss. 

Establish a centralised data management system

In addition to using clean data, Microsoft Copilot also needs accessible, centralised data. 81% of business leaders in MuleSoft’s 2024 Connectivity Benchmark report cited data silos as one of the key challenges holding back digital transformation. Consolidating all data into a single, accessible repository enables Copilot to access high-quality, integrated data. Centralised data reduces errors and boosts Copilot's ability to deliver precise insights and automation capabilities.

For example, consider a scenario where a company uses Microsoft Copilot to generate financial forecasts. If the organisation has financial data scattered across different systems, Copilot might retrieve incomplete or conflicting information, leading to inaccurate forecasts. Centralising financial records in one location ensures that Copilot can access the most comprehensive and up-to-date data to generate more reliable and actionable reports.

Optimising data for AI involves structuring it to improve accessibility and usability for Copilot. It includes tagging and accurately labelling data to streamline retrieval processes and regularly cleaning data to remove redundancies and outdated information. These steps ensure Copilot can access the most relevant and current data.

Review data security measures

Generative AI tools have brought up concerns about data privacy and security. How can you trust the platform not to store data and use it for training? Will people within the organisation be able to ask Copilot for sensitive data without authorisation?

As part of cleaning and centralising data, your organisation must address security and compliance measures. These will protect sensitive data from unauthorised access and ensure your organisation does not breach Australian data protection laws. Your organisation should revisit access permissions for folders and documents to ensure that people using Copilot do not accidentally access data not intended for their use. 

Strengthening these measures safeguards your business from potential data breaches and enhances customer trust by demonstrating your commitment to secure and responsible data management.

Plan a phased rollout

After preparing your data, the organisation is ready to roll out Microsoft Copilot. However, rolling it out at once can be overwhelming and potentially counterproductive. I recommend starting with a pilot program that involves a small, controlled deployment to test how well Copilot integrates with your existing business processes. This also creates an opportunity to collect feedback and refine the overall implementation strategy for a wider rollout.

A phased approach to deploying Copilot helps manage risks by addressing potential issues in a controlled environment. It also supports the gradual training of employees and the necessary workflow adjustments. Without proper training, your team might struggle to use Copilot effectively and maximise its benefits. Setting up regular training programs that reflect the latest updates and best practices will help people get the most from Copilot. Ongoing support and feedback channels also help ensure all employees are confident and competent in using Copilot.

Continually learn and adapt

Integrating AI into business operations is an ongoing process that requires continuous attention and adaptation. Your organisation should develop a strategy for updating data. Regular assessments and updates will ensure that Copilot continues providing value and keeping in sync with business objectives. This approach of continual improvement and adaptation enhances the capabilities of your workforce and ensures that your investments in AI remain relevant and deliver tangible benefits over time.

Conclusion

Microsoft Copilot is not just a platform you can 'switch on'. It requires your organisation to revisit how you manage data and prepare it for use by Copilot. The steps outlined—from assessing data readiness to fostering a culture of learning—form a comprehensive blueprint that prepares your business for using Copilot successfully. By carefully planning and implementing each phase of Copilot’s integration, your organisation can improve its chances of success with this powerful tool.

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