It's worth elaborating on these categories.

- Simple circumstances are ones like filling out a standard form and transferring the data from one system to another, tasks that are easy to describe and easy to repeat. Chatbots, intelligent forecasting and robotic process automation all excel in this area.

- Complicated circumstances are ones like transferring an employee to a new location. Sometimes we can break them into smaller simple problems, but not always. Success usually requires coordination with different people, multiple teams and context-specific expertise. Things often go wrong and timing and coordination can cause problems. Some of these problems are also amenable to automation.

- Complex circumstances are ones like developing a cadre of managers. Once you learn how to transfer an employee, you can repeat the process with other employees and perfect it. One transfer is like another transfer, but that's not the case with developing a layer of management. Although developing one cohort may provide experience, it does not guarantee success with the next. Expertise is valuable but not sufficient. The next group may require an entirely different approach from the previous one, which points to another feature of complex problems–their outcomes remain highly uncertain. Yet we all know that it is possible to develop a team of managers well even if the process is complex. Complex problems also involve rules that change based on circumstances, making it often hard to know when the change that matters will take effect–and rendering AI difficult to use here.

- Chaotic circumstances are ones that defy predictability. Many of the core processes of our organizations are chaotic. They are driven by the operation's response to emergent phenomenon like key personnel shifts, reorganizations, market changes, regulatory surprises, missed financial targets, continuous imprvement or new disruptive competitors. Most of the organization's processes are driven by difficult to predict factors, which are not favorable for automation projects.

There are two keys to successfully implementing AI projects: picking areas where the tools will work and having a larger vision of the fully automated organization. Among the latest tools out there, chatbots are a great example of the strengths and limitations of contemporary intelligent tools.

Solving HR's Problems with a Chatbot

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