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Working Smarter with AI: Empowering your nonprofit team with improved productivity
Time and resources are precious to your nonprofit. The eBook, "Working Smarter with AI", shows how Microsoft 365 Copilot enables teams to reduce digital overload, automate repetitive tasks, and improve decision-making. Download the eBook for more insights, and contact for tailored guidance on implementing AI tools.
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Digital debt refers to the growing amount of data, information, and communications that employees must manage daily. This burden can consume significant time, with employees spending an average of one full workday each week on emails or meetings. As a result, innovation often takes a backseat to busywork, making it challenging for employees to engage in strategic thinking.
How can AI improve productivity?
AI tools such as Microsoft 365 Copilot can streamline daily tasks by automating repetitive activities, allowing employees to focus on higher-value work. For example, Copilot can assist in creating presentations by pulling relevant data from existing documents or help manage emails efficiently, enabling teams to dedicate more time to creativity and innovation.
What is responsible AI?
Responsible AI is built on ethical principles that prioritize fairness, transparency, and accountability in AI design and deployment. It ensures that AI serves as a supportive tool for employees rather than a replacement. Organizations must focus on maintaining user control and protecting data privacy to foster trust and maximize the benefits of AI technology.
Working Smarter with AI: Empowering your nonprofit team with improved productivity
published by Cyberworld Logistics
Cyberworld Logistics is an ICT Consultancy, services focused company we provide end-to-end solutions and needs driven consultation. Providing consulting and IT Services to clients in Botswana make sure they conceptualize and realize technology driven business transformation initiatives. Using a low-risk delivery model to accelerate schedules with a high degree of time and cost predictability.
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