AI pitfalls to avoid Part 19

I hope you'll be able to use AI to build exciting and valuable projects either for yourself or for your company and make life better both for yourself and for others. Along the way, I hope you also manage to avoid some of the pitfalls I've seen some AI teams fall into. Let's go over five don'ts and dos for if you're trying to build AI for your company. First one, don't expect AI to solve everything. You already know that AI can do a lot but there's also lots AI cannot do. Instead, you should be realistic about what AI can or cannot do, given the limitations of technology, data, and engineering resources. That's why I think technical diligence in addition to business diligence is important for selecting feasible and valuable AI projects. Second, don't just hire two or three machine learning engineers and count solely on them to come up with use cases for your company. Machine learning engineers are a scarce resource but you should instead air the engineer talents with business talent and work cross-functionally to find feasible and valuable projects. It is often the combination of the machine-learning talents worked to business talent that can select the most valuable and feasible projects. Third, don't expect AI project to work the first time. As you've already seen, AI development is often an iterative process so should plan for it through an iterative process with multiple attempts needed to succeed. Fourth, don't expect traditional planning processes to apply without changes. Instead, you should work with the AI team to establish timeline estimates, milestones, KPIs, or metrics that do make sense. The types of timeline estimates, milestones, and KPIs or metrics associated with AI projects are a bit different than the same things associated with non-AI projects. So, hopefully working with some individuals knowledge about AI can help you come up with better ways of planning AI projects. Finally, don't think you need superstar AI engineers before you can do anything. Instead, keep building the team and get going with a team you have realizing that there are many AI engineers in the world today including many that have learned primarily from online courses. They can do a great job building valuable and feasible projects. If you can avoid these AI pitfalls, you already be ahead of the game compared to many other companies. The important thing is to get started. You're second AI project would be better than your first. Your third AI project would be better than your second. So, the important thing is to get started and to attempt your first AI project. In the final video for this week, I want to share with you some concrete first steps you can take in AI. Let's go on to the next video.