What is A.I. Readiness and how to achieve it?

Everyone talks about A.I., but when you want to do it, it seems harder than you might think. Over time many people realized that a company has to fulfill certain criteria to be able to do A.I. projects. Thus the term A.I. readiness was coined. Capgemini defines A.I. Readiness as

“Extent to which a country and its institutions & businesses have the ability to reap the benefits of A.I.”

As we learned before, the benefits of A.I. are defined as improving the Voice of the Customer (Product, Services, Customer Satisfaction), the Voice of the Business (Strategy, lower cost, higher revenue, increased stock prices or profits) or the Voice of the Employee (Employee satisfaction). That means that A.I. Readiness  is the

“Extent to which a business has the ability to improve the VOC, VOE or VOB  by using A.I.”

As there are many, many definitions of A.I. readiness, I personally like Intels approach as it is relatively easy to understand and a good starting point for you to dissect the term A.I. readiness into operational, usable terms.  Intel wrote a nice birds-view whitepaper on A.I. readiness. In this paper they define A.I. readiness on three independent dimensions.




It is important to dissect A.I. readiness to these independent dimensions, because many companies just focus on one dimension and wonder later why their A.I. initiatives are not working.

Transformational readiness includes, according to Intel, strategic leadership, educated leadership, a clarity of potential business cases and the acceptance of your organization to evolve. Even the most spirited leader will fail, if the employees themselves boycott change. Thus a big part of A.I. readiness is change management. The further the business is originally away from A.I., the more attention we have to put into change management.

Operational readiness includes all levels of management. On a governing level it covers governance, compliance, risk but also cyber security. On an operational level it builds up human resource readiness and needs to address the issue of assessing existing skills and expertise and how to fill the existing gaps. Furthermore it includes project management readiness. This includes the adaption of existing management frameworks to yield to specific software development & A.I. issues. Additionally it includes a specific focus on data management, data flow and data resource management.

Foundational readiness is the groundwork for A.I., but should only be tackled as an output of the two above dimensions. It consists of the infrastructure platform, cloud resources, data sources and software packages. Questions in this dimension might be:

Is my existing data center set up for increased network bandwidth needed by A.I.?

Do I know enough about the offerings cloud providers have? Do I know exactly how expensive cloud services would be? Do I understand the extend of services e.g. AWS offers and how much training my employees need to work with it?

Is critical data available? Do you have it labelled correctly? Is it up to date? Can you retrieve it in real time or does your database has to calculate a SQL request for 20 minutes?

Do you have people in your organization who have worked with AI software packages? Do they know which software is out there? Have they worked with e.g. TensorFlow and know how to connect it to your existing software landscape?

So, we see the foundational readiness is a product of operational and transformational readiness. In the following pages we will dive deeper into the different levels of A.I. collaboration.

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