7. How will you measure the impact of intelligent automation?
Enterprises should set key performance indicators (KPIs) for their automation strategy, just as they would for a human workforce. These include direct KPIs, such as process accuracy, speed of handling, and cost savings, as well as indirect KPIs like staff satisfaction, tools replaced, manual labor savings, and error reduction.
8. Is your intelligent automation strategy secure?
Intelligent automation often leverages sensitive data such as passwords, addresses, credit card numbers, and other financial information. Therefore, it's essential to mitigate these risks with a robust security framework that includes:
- Controlled access, protecting privileged accounts across the digital and human workforce
- Protection against sensitive customer and organizational data disclosed by bots and developers
- Traceability, auditability, reliability, and resiliency
- Data privacy by anonymizing data to avoid compromising the privacy and security of a consumer
9. Are your digital workers showing up for work?
In most cases, it's easy to tell when an employee isn't doing their job. In an automated process, you wouldn't know until an output was missing or a process was held up. Therefore, you need a way to spot which bots are at work and which require corrective action. This is where digital workforce management comes in. You can see how digital workers initiate, process, or complete work on a continuous, real-time basis.
10. Is the intelligent automation liable?
A liability framework helps you ensure that the data used to train intelligent automation is only used for the intended purposes of the algorithm. For example, you might create a model from several datasets covering different variables. Then, the AI might take one data stream and use it for another purpose, creating concerns around who's responsible for the outcomes. A liability framework sets parameters around data permissions and intended use.
11. Are bots and humans working well together?
Getting humans and machines to work together successfully is challenging. Define and design this collaboration through hybrid workforce management. Then you can anticipate breakdowns and exceptions, and pass an issue on to the appropriate employee using dynamic workflows.
It's also essential to effectively supervise, monitor, and instruct automated systems. As AI and machine learning generate new insights, you must deliver information to the appropriate decision makers. And of course, people will need training in how to work alongside their new digital counterparts.
12. Is your intelligent automation strategy cost-effective?
One-time costs include technology procurement and development. Recurring costs include infrastructure, licenses, and maintenance. To keep costs down, design automated solutions for stability and low maintenance upfront. Your governance framework should not only track performance, but also the overall cost effectiveness of the digital workforce.
Although the technologies involved in intelligent automation are quite powerful, they are powerless without proper governance. The reward for governance is greater trust, return on investment, compliance, and an intelligent automation strategy that's built to last.