Creating a successful AI-based solution is different from regular software development because how it will operate in a real-world situation isn't easily predicted. Development is an interactive process that requires extensive testing to ensure it will work well with a company's existing workflow and deliver a worthwhile return on its investment. As such, effective implementation requires an iterative approach that usually takes more time, effort, and money than traditional software efforts. Artificial intelligence is the key that can unlock a new generation of automation and productivity for businesses. But the development of AI can also help drive companies' sustainability efforts -- something increasingly important to today's workers.

IBM has been on the AI journey for a long time, but the path has not always been smooth. My experience in the consulting business has taught me that successful practitioners need to be flexible and quick to make course corrections. We at IBM have learned along our AI journey, and here are three lessons that come to mind from my own interaction with clients and business colleagues. Remember the adage: garbage in, garbage out. We've acknowledged that the results of data analysis are sometimes misleading or even inaccurate.