Candidate project selection

For-profit organizations do things typically for three reasons;

• Increase revenue and profit

• Reduce risk

• Increase operational efficiency

Measurable and Aligned

Although not always possible, wherever practical, an automation initiative with objectively measurable business outcomes and strong subjective metrics are preferred. Having clear-cut success metrics where capital investment needs to be considered against other priorities helps justify future business cases and demonstrates value to build momentum. Also, and more critical, you want first to understand what the business priorities are, overall, what are the current strategic themes? Will the selected business case help to achieve the stated strategic objectives? In business, there are always many areas that can be improved. Prioritizing, in terms of strategic impact, is important. E.g., if your business is switching from wholesale to consumer direct, you may not want to be investing to make the wholesale model more efficient. The above criteria are not specific to decision automation but important for all business improvement initiatives and often neglected in choosing a starting point. To summarize, prioritize decision automation initiatives that:

• Align with strategic objectives

• Are objectively measurable

The following are specific criteria for decision automation project selection. Not all of these need to be true: 

Quick Criteria

A quick smell test can help to identify good candidates areas.

• Enough transaction volume?

• Many SME’s working on tasks that makes it hard to scale up quickly?

• Business frustrated with the pace of change?

Enough operational transaction volume?

Although each transaction's dollar value may be small for repetitive transactions, high volume transactions can add up to be of dominant financial importance in business. These are usually an easy target for decision automation. Inversely complex strategic or tactical decisions that are seldom made don’t make for good automation candidates in an operational sense. Decision support systems can better address these, Subject matter expert knowledge combined with ad hoc insights from analytics or machine learning.

High variability of interpretation

Complex or expert decisions made by subject matter experts can often result in different outcomes based on who is making the decision. These areas tend not to scale up well as it takes time to train up staff. Automation of decisions will allow for better scalability and more consistent decision making.

Areas of Change

Complex or expert decisions made by subject matter experts can often result in different outcomes based on who is making the decision. These areas tend not to scale up well as it takes time to train up staff. Automation of decisions will allow for better scalability and more consistent decision making.