Unleashing automation at scale to reduce costs

Deploying automation to optimize customer experiences 

Ahead of his presentation on his experience deploying intelligent automation technologies at Twitter, Nike, and Anheuser-Busch InBev at CXN Live: CX Automation 2021, CX Network caught up with senior program manager Andrae Kirkland. The Lean Six Sigma Black Belt shares an automation success story from his time with Anheuser Busch, detailing how an automation initiative served to reduce logistics and workforce costs while making life easier for both customers and employees.

During his time at Anheuser-Busch InBev, Kirkland led several robotic process automation initiatives, and the development of machine learning algorithms to increase forecast accuracy using the predictive capabilities of artificial intelligence.

At Nike, he deployed an automated quality management platform across the supply chain network to monitor shipment performance. This earned him Nike's highly regarded ‘Just Do It’ award within the North America supply chain, which he feels was extremely special and an unforgettable experience for someone who grew up in sports.

CX Network: Can you walk us through a particular CX success story you have had regarding automation

Andrae Kirkland: As director of business transformation during my time at Anheuser-Busch, I led efforts focused on automating processes within logistics operations, such as updating shipments along with generating and deprecating product IDs. These were processes with a high number of touchpoints which drove inefficiencies and frustrations for the logistics team. Because the processes were click-heavy and involved large amounts of data, it could take a significant amount of time to update just a single load or process a new product ID.

For example, when we mapped the process to understand the pain points in one particular case, we found there were several screens and systems involved that would often time-out if the team member spent too much time researching potential solutions to an issue with a customer’s shipment, causing them to log back into the system and re-enter information they had already entered.

We resolved these types of pain points by developing optimized, future state processes largely enabled by the capabilities of automation. It was great because we were able to favorably impact operational performance metrics like number of loads created and shipment quality as a result of the business logic we built into the bots.

CX Network: What challenges kept you up at night with this project and how did the end result benefit your customers?

AK: It was really the scope of the project. We experienced a lot of success from earlier automations, so my team was challenged by leadership to continue building on that success and explore other avenues to implement automated solutions.

From there, the scope of our automation efforts exploded as we looked to address inefficiencies across several processes simultaneously. The approach was great for fast-tracking benefit realization, but was extremely taxing on process analysis efforts, the development team, and process owners.  

To navigate this, we collaborated heavily and iteratively with subject matter experts on both the current state process along with the future state build because they owned the process and who knew it best. Also, for some processes, even though we had bots executing all or a large portion of the operational activities, team members were still needed to perform checks and review the productivity of the bots.

All in all, the project was tough, but that speaks to the inherent complexity you’re dealing with when it comes to deploying automated solutions in operations.

CX Network: What is your golden rule for success with automation in customer experience?

AK: My golden rule for automating and defining customer experiences is understanding the problem that a customer is having. Brands should then consider how they can define that problem in the most discrete and succinct way possible, so that they know what they are solving. If we start off with an ambiguous objective or task, that is going to lead to challenges in terms of process mapping, designing the solution, building out those business rules and developing the logic. If we start with a succinct, well-defined problem as it relates to the customer experience, then we can start developing a process map that makes sense and ultimately improve the CX.  

To hear more of Kirkland’s insights regarding the application of artificial intelligence and machine learning, sign up for CXN Live: CX Automation here.