Workbook training: Barriers to scale in a complex environment, part 4
Unbundling AI: Building a sustainable data-driven organization | Workbook training, part 4/5
Hello,
This part of the training relates to part 4 of the workbook: "Barriers to scale in a complex environment." You can read it here.
▸ Why this training and how it was conceived
To help us define a better future, artificial intelligence will hopefully create, not diminish, opportunities and open new, broad perspectives. However, the higher the stakes, the higher the risk of being caught in series of infinite loops demanding highly complex and important decisions.
The real challenge is that most decisions are prone to distortion because they tend to involve assumptions, estimates, and inputs from too many perspectives.
What approach is needed to identify and remove internal business constraints based on data understanding?
In part 1 of the training, “Introduction to the data transformation era,” we saw how to apply that understanding to key decisions about AI-driven business models.
In part 2 of the training, “Perspectives for defensibility,” we focused on data-based decision management and weighing up immediate and expected future outcomes that may be conflictive.
In part 3 of the training, “Growth and resilience” we emphasized on the need for robustness, the right goal setting, and making the leap, particularly during uncertain times.
In part 4 of the training, “Barriers to scale in a complex environment”,
A reminder
The priority for organizations to build next-generation systems falls into three areas:
Improving data management;
Enhancing data analytics and machine learning;
Expanding the use of all types of enterprise data, including streaming and unstructured data.
🚨 You can get back to part 4 “Barriers to scale in a complex environment”
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