Organizations that leverage artificial intelligence (AI) and machine learning (ML) to automate manual processes stand to realize benefits such as increased efficiency, productivity, and profitability and reduce exposure to human-caused mistakes. But often, organizations jump onto the automation bandwagon without fully understanding what they’re trying to automate and what’s involved. The results can be expensive and lead to failure and disappointment. In this article, I want to explore the reasons why you should conduct an audit before you automate.
Know what you trying to automate and why
While this statement may seem obvious, we often find that the answer is far more convoluted than organizations realize. On the surface, you likely know what needs to happen. But when you look under the hood, things get more complex and multiple questions must be addressed, such as:
Digging in and answering these and other questions can help you expose complexities and assess the full scope of what you’re wanting to automate.
Establish clear KPIs
Once you understand the goals of your automation project, make sure you have a way to track against those goals by establishing a clear set of KPIs. Defining KPIs can help keep progress and scope on track, align stakeholders around expectations, and quantifies the projects success.
Automation KPIs might include:
How an automation audit helped a major software company save time and money
We recently worked with a major software company that relies on multiple third-party, customer service vendors to manage customer support issues. At the time, 20-30 people manually monitored just 20-30% of the agents’ work. Our customer wanted a comprehensive, automated tool to monitor all the agents with fewer people.
Before diving into the project, Beyondsoft first conducted an automation audit, to answer the questions listed above as well as many others. The audit uncovered many complexities that we knew we needed to address. For example, our customer wanted to monitor information in near real time. However, we determined that there was no data available, necessitating the creation of a new real-time API.
Then there were the integrations. Our customer wanted visibility into issues originally posted on social media. But upon closer examination, we found that the effort to accurately analyze the data (e.g., determining if there is an issue or if a customer is airing past grievances) could not, at the time, simply be handled by AI and ML.
We not only looked at the customer’s pain points, we identified and analyzed multiple data sources. We explored the technical framework, including the tools and services required, the necessary integrations, workflows that would need to be created, and the essential skillsets needed. From there, we put together a proof of concept to demonstrate the capabilities and limitations of the chosen solution. Then we estimated the costs and implementation timeframe and determined a clear set of KPIs to help keep the project on track and ensure the outcome would meet customer expectations.
In the end, by taking the time to conduct an automation audit and ensuring the customer had a clear understanding of the solution they were after, we were able to deliver a solution for monitoring 100% of the third-party agents with just half the staff previously required.
If you are looking to automate, contact Beyondsoft to schedule an automation audit. For additional insights, check out Considerations for designing a test automation strategy and Say hello to productivity with intelligent automation