Ashley Gershoff leverages her cross-sector experience to strategize and implement enterprise-wide digital transformation initiatives.

Can you share the most interesting part of your career journey?

When I look back to starting at Deloitte three years ago, I would never have imagined how much I would grow and learn from my colleagues and mentors at the firm.

It’s been an exciting journey, building my knowledge of intelligent automation to now becoming more of a leader and mentor in my practice.

Being able to mentor new team members and share what I’ve learned with colleagues and clients energizes me—specifically, being able to take and apply the knowledge and skills I’ve gained from working across different industries and apply them across clients’ business processes.

It’s interesting to see how processes across different industries and companies can be very similar, so it’s exciting to use my experience and knowledge across clients within different sectors to both strategize and implement enterprisewide digital transformation and educate clients on intelligent automation capabilities so that they, too, can identify feasible and high-value automation opportunities.

I also enjoy working with processes that are not yet defined, which provides room for creativity to help define and streamline them.

What excites you most about working with data and AI?

In light of the recent pandemic, with many companies trying to become more digital, automation has seemingly become even more popular. Clients’ increased interest and enthusiasm about working with Deloitte to automate manual processes makes the work especially rewarding. I still remember a client saying that an intelligent automation project I worked on felt like “light shining at the end of the tunnel, especially during COVID.”

It’s interesting to see how different clients are at varying stages of AI maturity when it comes to intelligent automation. For example, one client may be on the lower end of the AI spectrum, just beginning its automation journey, and wanting to begin implementing robotic process automations, which can only automate rule-based processes with structured data.

It’s important that processes be defined and that the data involved in a process is accurate and clean because the automation is only as good as the data it receives.

At times, when I’m in workshops with clients, trying to identify and assess automation opportunities across their business processes, they may identify process gaps or processes that don’t yet exist. It’s important that processes be defined and that the data involved in a process is accurate and clean because the automation is only as good as the data it receives. It excites me to have conversations with clients during workshops to discuss ways processes can be streamlined.

Leveraging AI technologies, such as intelligent data extraction, machine learning, and conversational AI, with robotic process automation during solution designing enables end-to-end process automations, creating a higher return on investment for an organization—which maximizes the time employees can focus on other value-adding tasks.

Describe an interesting project that you have worked on.

During the pandemic, power and utilities organizations saw an increase in customers declaring bankruptcy, which resulted in higher volumes of bankruptcy cases to process. This was a situation that also occurred at one of my clients where I helped manage the automation implementation for three customer bankruptcy processes within the credit and collections business group, which included processing bankruptcy petitions, dismissals, and discharges. The automations reviewed the bankruptcy status for residential and commercial accounts based on court decisions and updated the account information in SAP.

As the design lead, I led design calls with the client, collected process requirements, and was the liaison between the business and development team during development. The bankruptcy process owners did not enjoy processing bankruptcies daily since it was manual, tedious, and prone to human error.

It was exciting to see the automations go from the development, to test, to the deployment phase, and to see the client’s reaction to how well the automations performed.

The client looked forward to no longer having to do redundant data entry and being able to focus more on analytical work.