4 Tips for AI and Digital Transformation Analysis

I am excited to hear that many of my clients are venturing into projects that include digital transformation, intelligent automation, machine learning and other artificial intelligence capabilities. I am passionate […]

I am excited to hear that many of my clients are venturing into projects that include digital transformation, intelligent automation, machine learning and other artificial intelligence capabilities. I am passionate about these topics and how these capabilities deliver value to our end users. 

If these topics are not already on your horizon, they will be soon, so here are some tips to help your intelligent automation and digital analysis:


1)  Analyze the customer journey. Remove touch-points, don’t add them!
Research and understand your customers’ experiences with your organization! Map out their journey. Find out how they achieve their goals and understand their pain. With AI, look to remove touchpoints, the ones that don’t add value of course, and make sure you are not adding touchpoints. 

2)  Experiment and hypothesize. 
These new technologies are complex, but can be quick to implement. To make sure you are on track with your ideas, build in “spikes” that serve as experiments to test the team’s big assumptions and hypotheses. Learn from these spikes. Make sure the team is not trying to perfect every idea and feature before learning.

3)  Elicit user stories that are innovative!
Is your backlog boring? Use creative facilitation techniques and collaborative games to liven up the backlog items and challenge the team to bring more innovation to backlog items. Your leadership team expects innovation. Don’t be the team that blames the big backlog at the end of the year. Change your backlog! 

4)  Be agile and split stories from a user point of view.
Digital transformations and AI capabilities are definitely candidates for an agile approach. To make sure you are getting the most from agile, your team needs to know how to effectively split and slice user stories into small enough pieces that can be estimated and understood by the team, while keeping the user and value focus.