Agile AI

Chapter 4. Summary

As you can see, artificial intelligence (AI) has many moving parts.  A practical Agile approach to AI focuses on the right team mindset of flexibility, the right set of tools, and the right set of team skills. Across the first three chapters, we talked broadly about those ideas as well as identifying the proper uses cases, organizing your data, and changing your business by teaching it to trust the products you’re building. We want to take a few last words to close on the significance of these points so that you can go forth leading your team to achieve its most ambitious AI projects.

Use Cases

Integrating AI into your business happens one use case at a time. First, work to identify the appropriate use cases: use workshops and design thinking, and ideate as a team to get many perspectives and insights. Focus on business problems; don’t get caught in the trap of building technology for the sake of technology.

After a project, conduct retrospectives on those use case implementations. What worked well? What could’ve been handled better? Identify patterns in your use cases, and share those across the organization. Ideally, create a Center of Excellence for AI patterns within your organization.

Data

You can’t deploy AI without first getting your data into shape. Those are table stakes, the absolute basics. Recent industry surveys about AI adoption in the enterprise have shown that the majority of firms become bogged down in the technical debt they must resolve ...