Robots and AI are redefining the rules for business execution and organizational agility. Investment in new technology is driven foremost by the goals increasing execution capacity (scalability) and organizational agility. Seeking more, faster, and most often with fewer personnel, enterprises prioritize technology investments which can speed time to market, to empower workers to make better informed decisions, as well as to reduce the overhead otherwise required with delivering products and services to market. The ability to adapt and respond according to both new events and consistent with existing rules and policies is critical to organizational agility. Yet this goal is often at odds with automation focused on scalability and repeatability. Today’s process automation looks a complex set of conveyor belts designed for optimal efficiency and consistency. Industrial engineers designed the ideal routes to move packages in the most efficient way possible, and indeed these pathways are fixed. They do not change or adapt their paths based on what is in the package.
Most of today’s process automation systems and indeed many business automation initiatives were designed and built in the same manner. The challenge with that model of automation is that rigidly following fixed pathways are not consistent with the way we work. We do care about what’s in the package. We cannot fully script out in advance the sequence of steps and end-to-end processes without knowing the exact context of any given task we will be performing. For this reason, process automation to date has been limited to repetitive and relatively simplistic process areas. Yet when we combine case management and data-driven intelligence with process automation, we can expand the range of what can be automated or otherwise managed. This combination of capabilities enables “Intelligent Automation.”
What does Intelligent Automation look like? Using the same metaphor as before, consider one of Amazon's fulfillment centers where its Kiva robots have replaced the fixed conveyor belts. Just as we do in our own work, the robots do care and in fact know what is in the package. Using this awareness of context (what’s in the package and where it’s going) the robots determine the best pathways and placement of products to enable the fastest possible fulfillment process. The robots leverage process, rules and data to define pathways which adapt to the context of work at that moment, just as we need to adapt on successfully complete our work.