Orchestrated Iterative Change Delivers On Step-Change Expectations
Harnessing Intelligent Workflow MappingAdd bookmark
Why does scaling intelligent automation seem so difficult if we’ve got better tools than ever to do what we always have done?
Automating repetitive tasks increases efficiency, reduces cost, can move your best talent to more enterprise productive tasks which can all lead to a more optimized enterprise. In fact global corporate enterprise has been expediting this very concept since the onset of the industrial age.
Before the beginning
As early as roughly 300 BC, automated water clocks were used for irrigation and simply keeping time- the system utilized water flow to measure time much like sand in an hourglass.
Farmers thus saved a tremendous amount of time having to regulate water flow to their crops. And of course, they saved time being able to keep time.
In the beginning
The prime mover, which converts energy into motive power, was introduced in manufacturing in the early 18th Century. The concept though was Aristotle’s prime mover theory and advanced by Leonardo Da Vinci’s draft of a steam-powered cannon.
Prior to the steam engine, water was the force du jour. But if one was not located near a water source, effort provided by horse or a human was the highest physical power attained. And so early external combustion steam engines were the driving force of the industrial revolution. Instead of relying on mostly human output, machines were relied on for efficiency, cost reduction, moving talent to more productive tasks and vastly more optimized enterprises throughout the land.
The modern age
The application of mathematics, electricity and eventually the computer hurled global society further faster forward to bring us to the onset of intelligent automation.
The factory floor is no longer the primary focus of automation. Intelligent automation has provided a solid foundation for the efficiency of the knowledge worker and the information based enterprise.
That early industrial age factory floor though was the breeding ground of workflow mapping, which led to optimizing workflows for the railways, which eventually led to Kanban and six sigma. Which brings us to you, the reader. Either you’ve got a black belt, a green belt or know someone who does.
And so, we have been optimizing automation all along. Why does scaling intelligent automation seem so difficult if we’ve got better tools than ever to do what we always have done?
Intelligent workflow mapping
Simply automating repetitive tasks with robotic process automation, for the most part- has been accomplished. The central issue that has bubbled to the surface for global enterprise executives is no longer basic intelligent automation- it’s optimizing that intelligent automation. Optimizing intelligent automation means that the organization is intelligently automating end-to-end processes thus scaling intelligent automation across the enterprise. One way to ensure this optimization is through intelligent workflow mapping.
But beware of plug and play. Just because intelligent is at the beginning of the term doesn’t mean it does it for you. Basic automation- going back to the clock example above is a simple building block. The strategy for how much water was delivered to the clock was conceived and rolled out by the farmer.
Think about how much of your effort went into your Robotic Process Automation journey from the first moment the concept came across your desk to now. How much of your effort was accomplished by RPA?
The same goes for intelligent workflow mapping- it helps you. It doesn’t do it for you.
Coca-Cola’s Max Just shares, “You don't have time to waste. Trying to make sure that we spend the time on making the process efficient and leveraging those platforms that allow you to do that- removing waste from the end-to-end processes, are becoming more and more important. We're trying to make sure that we simplify those processes as much as possible, make them user-friendly through mobile access and enable with real data.”
Did you see that? He mentions leveraging platforms only after he says we spend time on making the process efficient. Roll up your sleeves. Get down in the dirt. Look at your crops. Figure out how much water your crops need. Then apply the elegant solution.
GM’s Director of Data & Data Science notes that when “you basically know the workflow, the work is waiting on the data and we're making the decisions when we’ve got the data.”
Literal data-driven decision-making
Pearl Long Term Care Solutions CTO, Ty Grandison opens up on the work that needs to be done to benefit from intelligent workflow mapping, “to be honest any process bot map that you create from some automated tool never truly captures what really happens in real life. The best maps that I've seen are from executives going in, sitting with people, and going through- ‘what does my data look like’ and mapping it that way- because then you see how everything actually really works.”
In RPA, 'robotic' is the steam. The Process is the input. Automation is the outcome. Just like RPA, the intelligent workflow mapping solution is the steam and will only benefit you based on the cleanliness of your processes and data before you begin. You can only harness the motive power of automation if you’ve mastered the input and know how to use the outcome. Otherwise, you’ve just got hot air.