AI Readiness: Operational Readiness

Above we looked at Professor Malone’s four level of collaboration and saw that you can use A.I. as a tool, an assistant, a peer and a manager.

We assumed also that we would buy the A.I. application and run it via a cloud provider. However, when you want to make your own A.I. application, the needed level of A.I. readiness changes dramatically and the complexity multiplies.

For now we want to just focus on the operational readiness and we will assume, ceteris paribus, that you have a spirited and educated leader and a team willing to change. You have identified a great business case and know that this project will align with your strategy (Transformational readiness). Furthermore we assume that your IT team would be able to meet any challenges they face (foundational readiness).

What is Operational readiness?

If you want to make your own A.I. application, you need a small pre-project team to even assess what you need.  This team needs to be able to define the needed skills and map them to the existent and available skills in your company. How many times have we seen great organization chart with great skills added to some names and then it turned out that they already were working 100% on other projects for the next year. Thus there is always a difference between existing and available skills. Furthermore your pre-project team needs to tackle all legal and compliance issues. This also includes data protection and data security issues. Make sure to choose a pre-project team which is able to handle these legal issues professionally. What is your compliance manual saying to A.I. projects? How knowledgeable is your data protection officer? How well is your data security team able to asses potential threats caused by the A.I. project. How well are you insured towards incidents caused by the A.I.? I am just mentioning this as A.I. applications are very tricky and hard to handle by compliance and insurances. The nature of A.I. applications is that it is changing with the inputted data. Thus you cannot define a definite use-case as you might be able to do with a software application like Powerpoint. Also insurances have a hard time insuring a changing application. Before you start your first A.I. project, make sure to have talked to your existing insurance providers and check if you are breeching any defined clauses. 


The pre-project team also has to make sure that the project you are planning is not using one-sided or biased data. All in all, all data is in some way biased, but you don’t want to have a social media negativity storm brewing over your head, just because your pre-project team was to junior to grasp the severity of the potentially tilted data.

In the end your pre-project team needs to also make sure that your company wide project management framework is set for A.I. projects. Talk to your project management office (PMO) to request changes to your upcoming A.I. projects. I have seen in many companies that the demanded processes by the PMO were hurting young A.I. projects as they were demanding information A.I projects were not able to provide. Many companies have two tracks of project management frameworks. One for software development and one for other projects. A.I. projects are however different and thus need a third project management framework (s. previous articles).

In the following section we will find out how data and data management is involved with operational readiness.

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