#AI@Work: Structures & Outcomes
Developing an accurate picture of the current structure will also help in revamping it. How high is it? How deep? Whether it is a pyramid or a matrix model or somewhere in the middle, the more top down or sideways layers the more difficult it is to move. Power struggles can easily emerge from authority and accountability. The results are anything but effective and agile. Agile is about movement, adjustment, fluidity, and awareness. When change comes quickly, the tradeoffs between costs and responsiveness are critical. Agile is crucial for survival.
Agile organizations ask, “What can we do to make that happen?” Change is not about predictability. It is not about “How much will that cost and how long will that take?” It is about response and accountability. “What can we make happen?” “How can we get that done by the end of the month?” Continuous delivery and analytics play a very important part in Agile transformation. This process helps to justify itself. “Fast Feedback and Fail Fast” are two sayings that follow this approach. With “AI” knocking at the door, some variety of the build-measure-learn loop so familiar to lean startups is key to Agile. Part of the building process has to include building the skills of human beings.
Many new systems claim to be AI. It’s a broad definition. Some offer cost reduction or productivity and service improvement. HR systems for recruiting and talent matching are prime examples. Customer service apps are another key example. AI in its current state is more suited to operational work than asset creation. It can aid in asset creation in some areas. Design, develop, experience, deploy and test are usually not redundant tasks. Operational tasks like monitoring, maintaining and automating are more predictable. Sometimes these cross paths. When they do, AI can take over part of the operation as long as it is monitored and audited.
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