How to achieve excellence in applying Machine Intelligence in business operations

Below you can read CosmoEmporio's key takeaways on a very interesting survey-research made by McKinsey.

We summarized the 13-page article focused on replying to the practical question:

"How to achieve excellence in applying Machine Intelligence in business operations?"

1.What is the opportunity of NOW for small-medium companies that want to start moving towards Smart Production?

A decade ago, any company wishing to apply MI to its operations had little choice but to build the necessary infrastructure and systems in-house. That would be too expensive for an SME. Today, organizations have the option to buy tools and expertise from a huge ecosystem of vendors. That makes it possible for executors to achieve significant gains in specific use cases without doing as much groundwork upfront

2. What are the similar characteristics of the Leading companies that achieve excellence on applying Machine Intelligence in business operations?

-Leading companies will only leverage machine learning or AI if its results are intuitive and transparent and, in some industries, explainable to external regulators.

-Leading companies have strong governance in place to keep their digital programs on track. Leaders have a defined process for the assessment and implementation of digital innovation, they follow that process regularly and update it continually as the organization learns more about what works and what doesn’t

-Leading organizations apply MI more widely and use more sophisticated approaches.(see Exhibit 5 at the article,you can find link in the comments)

-At leading companies, advanced digital approaches are not the concern of only a few specialists. Leaders take steps to ensure as many of their employees as possible have the skills and resources they need to make use of these techniques.

3. What are the Steps that the Leading Companies followed to achieve their level of excellence?

-Identify the organization’s North Star and conduct a visioning exercise that describes an ideal version of its operations in three or four years’ time. This will require looking past legacy infrastructure and skill constraints.

-Conduct an honest assessment of the organization’s starting point across dimensions.

Many companies find that an objective external diagnostic is the pragmatic choice in understanding where they really stand versus their competitors—and, more importantly, versus their potential.

-Scope out a rough transition plan, accounting for barriers to change, critical infrastructure (such as data migration to the cloud), and assigning realistic medium-term targets.

Most leaders started by using data to make decisions using tools simpler than MI. As they built maturity and familiarity with their data, they could move to more advanced techniques.

-Find a variety of use cases to gain early momentum while scoping out the necessary changes in infrastructure, talent, and related supports.

P.S: If you want to read all the 13 pages-article of McKinsey related to this survey-research you can find the link in the comments below.