| As someone who has been regularly creating different AI models in Azure AI and developing Copilots via Copilot Studio for businesses who are moving to becoming 'AI Native' - I've gathered insights through my experiences and learning and would love to share them. During these developments, deployments, and tests, I've identified several key lessons learned that may assist others on their own journey. Are you an AI Native business? An AI-native business seamlessly integrates artificial intelligence into its core operations and strategies. Unlike traditional businesses that may use AI as an add-on, AI-native businesses make AI central to their processes, leveraging it to drive enhanced data, trend and analysis data decision-making, optimize workflows and processes, and enhance customer experiences. These businesses utilize AI to improve efficiency, gain insights from data, and automate tasks, making AI a fundamental component of their competitive edge and overall strategy. Is your business AI Native? Lessons Learned - Clearly Define Goals, Audience, Purpose, Boundaries, and Measures: Before starting any AI project, having a well-defined goal and target audience is paramount. Understand your target audience, the purpose of the model, the boundaries within which it will operate, and the metrics you will use to measure its success. This clarity ensures the project stays on track and delivers meaningful results.
- Garbage In, Garbage Out: Ensure Data Quality: ️➡️ The quality of your AI model is directly related to the quality of the data you feed it. Ensure your data is as complete, accurate, and relevant as possible. Clean, well-structured data is the foundation of a robust and reliable model.
- Iterate and Validate Continuously: ✅ Developing AI models, especially generative and computer vision models, is an iterative process. Continuously validate your model against real-world data and scenarios. Regular testing and refinement help identify and rectify issues early, leading to a more reliable and efficient model.
- Expect the Unexpected: ⚠️ AI development is often unpredictable. Be prepared for unexpected challenges and surprises. Flexibility and adaptability are key to navigating the complexities and nuances of AI projects.
- Build an Open-Minded and Visionary Team: Having a team that, while they may not understand all the details of AI and its complexities, are open-minded, grounded, and have a vision of how they want their part to look is crucial. A team with a clear vision can drive innovation and provide valuable perspectives.
- Establish a Framework for Conversations: ️ Set up a framework to guide conversations around input, output, and outcomes in relation to the AI models and vision. This structure helps ensure that discussions are productive, focused, and aligned with the project's goals.
- Develop Using Integrations and Check Against KPIs/Expectations: It's important to develop AI models with integrations in mind and regularly check against key performance indicators (KPIs) and expectations. This ensures the model performs as intended within the broader ecosystem and meets the desired objectives.
These lessons have been pivotal in my journey and have significantly contributed to the success of my projects and the quality of output from the AI models. Happy Model creating! #AI #AzureAI #CopilotStudio #MachineLearning #DataScience #TechInsights #MVPBuzz #BestPractices #TipsAndTricks #Copilot #MicrosoftCopilot #GenAI #ComputerVision #NewWaysOfSeeing #AINative |
