High-risk tolerance of the organization From agreeing to criteria for what is “good enough” to understanding how models must be validated and developed, machine learning often challenges traditional approaches to quality assurance and risk management.
Develop a robust data strategy and ecosystem Machine learning needs data – usually vast amounts of data.
Put together an interdisciplinary data science team Investing in machine learning and seeing results, you can not just invest in technology.
Establish an experimental way of thinking in the company Machine learning is an iterative and exploratory process.
Engagement for the adaptation of established business processes Whether it’s automating an existing decision-making point or providing a new product or service offering, machine learning is disruptive.