The article provides insights into seven key takeaways for successful AI projects.
SwissCognitive Guest Blogger: Eugene Kharitonov – “My Seven Lessons Learned, as Executive from Failures in AI Business Projects”
These include building a highly skilled team, defining clear objectives and KPIs, focusing on data quality, conducting thorough testing, monitoring AI models, aligning with business objectives, and learning from failures. By following these guidelines, organizations can improve their chances of success and create meaningful value with AI.
As with any business endeavor, AI projects don’t always succeed on the first try. Having fallen short in a number of such projects myself, I’ve learned a few valuable lessons along the way.
Here are seven key takeaways from my experiences:
1. Build a highly skilled team
Ensure that your team has both data science and business expertise, and that every member knows and understands their specific role.
2. Define your objectives clearly
Clearly define your goals and the KPIs you’ll use to measure your success. This will help ensure that your project stays on track and produces meaningful results.
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3. Focus on data quality
Ensure that your data is of good quality and that you can rely on it to make informed decisions.
4. Conduct thorough testing
Testing is critical in every phase of an AI project, from development to rollout. Ensure you have a comprehensive testing plan in place.
5. Monitor your models
AI models need constant monitoring to ensure consistent performance. Keep a close eye on their accuracy and results to stay ahead of problems.
6. Don’t lose sight of the business case
Ensure that the AI project aligns with your business objectives and complements your products or services.
7. Learn from your failures
Most importantly, use failures as an opportunity to learn and improve. Build on your experiences to make your next AI project even better.
By incorporating these lessons into your AI business projects, you’ll be better equipped to succeed and create meaningful value for your organization.
About the Author:
Eugene Kharitonov is a seasoned Program Manager with a commendable history of success in digital initiatives, particularly in the realm of AI/ML. His expertise spans across program management, coordination, team building, and the capacity to operate autonomously. Eugene’s experience is underscored by his management of large-scale international projects and partnerships with leading consultants such as BCG, McKinsey, and Deloitte. His skill set includes Agile, Design Thinking, Data Science, and AI/ML, complemented by excellent communication, problem-solving, and interpersonal abilities.
The article provides insights into seven key takeaways for successful AI projects.
SwissCognitive Guest Blogger: Eugene Kharitonov – “My Seven Lessons Learned, as Executive from Failures in AI Business Projects”
As with any business endeavor, AI projects don’t always succeed on the first try. Having fallen short in a number of such projects myself, I’ve learned a few valuable lessons along the way.
Here are seven key takeaways from my experiences:
1. Build a highly skilled team
Ensure that your team has both data science and business expertise, and that every member knows and understands their specific role.
2. Define your objectives clearly
Clearly define your goals and the KPIs you’ll use to measure your success. This will help ensure that your project stays on track and produces meaningful results.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
3. Focus on data quality
Ensure that your data is of good quality and that you can rely on it to make informed decisions.
4. Conduct thorough testing
Testing is critical in every phase of an AI project, from development to rollout. Ensure you have a comprehensive testing plan in place.
5. Monitor your models
AI models need constant monitoring to ensure consistent performance. Keep a close eye on their accuracy and results to stay ahead of problems.
6. Don’t lose sight of the business case
Ensure that the AI project aligns with your business objectives and complements your products or services.
7. Learn from your failures
Most importantly, use failures as an opportunity to learn and improve. Build on your experiences to make your next AI project even better.
By incorporating these lessons into your AI business projects, you’ll be better equipped to succeed and create meaningful value for your organization.
About the Author:
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