Harness the power of artificial intelligence (AI) to elevate your organization’s productivity with our comprehensive playbook. We’ll guide you through every step of leveraging AI, from identifying your most significant challenges to implementing effective solutions. Keep reading to learn more about using artificial intelligence to accelerate productivity.
AI transforms the way we work
AI is revolutionizing our work processes with advancements like generative AI and automation. Our own suite of AI tools —Atlassian Intelligence — automates tasks and generates content across platforms such as Confluence and Jira. These features boost individual productivity and foster collaborative teamwork.
Implementing AI across an organization can be challenging, requiring careful integration and adoption strategies. Tools like Confluence and Jira use AI tools to support collaboration, project management, and operational efficiency. They seamlessly fit into your organization to enhance productivity and decision-making, allowing you to use AI while managing the complexities of large-scale adoption.
It’s important to identify your problems and users
Identifying your organization’s challenges and understanding user needs should be the first step in implementing AI best practices. Rather than adopting AI simply because you think you need to, focus on solving specific problems (use cases) and improving workflows through targeted solutions.
Run high touch AI pilot programs
Running high-touch AI pilot programs validates AI’s impact within your teams. For instance, Atlassian initiated a pilot program for AI product managers, starting with a small group to identify necessary tools that provide value. Participants reported significant benefits, with 88% saving at least an hour per week and 94% gaining confidence in using AI tools.
While pilot programs validate AI’s practical benefits, they also provide insights into how teams can integrate into existing workflows to improve productivity and decision-making. They can be a foundation for scaling AI initiatives across the organization, ensuring that AI adoption is strategic, impactful, and well-supported by user feedback and experience.
Differentiate between when to use AI and how to use AI
Understanding when and how to use AI can help you maximize its effectiveness, assign tasks appropriately, and choose the right tools for specific project management challenges.
Identify specific use cases where AI can add value, such as automating tasks, generating content, analyzing large datasets for insights, or improving customer service. Pinpointing these opportunities allows you to prioritize AI initiatives that align with your business objectives and operational needs.
Once you’ve identified when to use AI, focus on how to implement it. Assign tasks to AI that align with its strengths, such as data analysis, pattern recognition, or generating text, and choose the right tools that fit your project management challenges. For example, AI tools can summarize Jira Service Management issues and Confluence pages, define terms in Confluence, and generate content across various Atlassian products.
Integrating AI into workflows may require a mindset shift and continuous learning to capitalize on AI best practices fully.
Build trust between users and AI
Establish trust between employees and AI by being intentional about how AI is integrated into your workflows. For instance, Atlassian encourages employees to embrace AI by simplifying access to specific AI tools within platforms like Confluence and Jira.
Atlassian achieved impressive adoption rates by making it straightforward for employees to identify and use AI tools. Approximately 90% of Atlassian employees regularly use Atlassian Intelligence in their work.
Educate users on AI policies and demonstrate AI’s benefits and practical applications in real-world scenarios. By showing employees how AI best practices enhance productivity, you can illustrate the value of AI in practical terms. This process educates users and builds confidence in using AI to streamline workflows.
Measure the success of AI
Measuring the success of AI implementation requires a multi-faceted and adaptable framework. Incorporating qualitative and quantitative analytics and feedback can help you understand AI’s impact holistically.
Role-specific metrics can also help you evaluate AI’s effectiveness in different organizational functions. For example, sales teams might measure AI’s impact on lead conversion rates, while customer service teams might assess its effect on response times and customer satisfaction scores.
Focusing on progress over perfection can ensure organizations iterate and improve upon their AI strategies continuously. Setting realistic goals and benchmarks early on allows teams to measure incremental successes and identify areas for refinement.
Measuring AI success early in the implementation process can help you identify initial wins and potential areas for improvement. It allows organizations to gather data and insights from the outset, informing future decisions and strategic adjustments.
Remaining flexible in adjusting metrics as business objectives change ensures that AI initiatives remain aligned with organizational goals and responsive to changing user needs.
Scale the impact of AI
To scale AI’s impact, you’ll need to replicate successful outcomes across different areas and continually experiment to optimize tool usage for various use cases.
Identify areas where AI has demonstrated positive results and replicate those successes in other parts of the organization. Replicating successful implementations can accelerate adoption and maximize the benefits across various operational areas.
Experimentation can help you optimize AI best practices for different use cases. Organizations can discover new ways to use AI by continually testing and refining AI applications. This iterative process enhances operational efficiency and drives innovation by exploring novel AI-driven solutions to complex challenges.