Taking the Leap: Intelligent Automation Best Practices
Updated: Aug 13, 2019
- Data hygiene. “Good data” bar for ML and Human are often different and not having your data sterile from a human standpoint may not stop AI Learning from it.
- Use case. Select something everyone in your org understands and can validate, processes core to your business. A tedious, unsexy business process is what will bring the entire org onboard. Moreover, stay as close to the current manual task as possible - do not re-engineer (=confuse) your process on the fly. You will have a better picture, angle, process, and workforce analytics once it is automated.
- Assessment and Design. Empower your SMEs - convert them into product owners and solutions consultants early on - capturing and owning requirements by your LOBs will keep them focused. We have tools to help with that.
- Product. Stay within platform guardrails. - IT. Thorough architecture review beyond what was sold and demo-ed by platform vendors is a must (simple to develop and integrate tool, right) - there are tick-boxes to be checked on enterprise level, but don’t turn it into a filibuster by IT - for some clients it took quarters to check like if the tech wasn’t around for years already.