Right home for AI and analytics? – Part 1

Today AI as a general term seem to be moving very fast towards part of common office tools. Thus, also people utilizing it are often specialists in their own domain of expertise rather than AI specialist. This is quite natural for the features improving end-user efficiency and I think all of us should become some level of experts in utilizing AI supported tools like copilots for that.

In this article I am more considering the different options to locate AI specialists, who are working with algorithms and models embedded into business framework of the company. Often these solutions are invisible for end-users and just offer better situational awareness while critical business decisions are being made. These underlying knowledge refining components integral part of business processes of an intelligent company, which desires to make better use of their existing data assets. Main goal is usually to re-innovate way things are done in company’s operations or utilize hidden process related knowledge from massive data sources generate by those very same processes.

So here is a brief no exhaustive observation of 5 most obvious locations for AI and analytics talent to be located. There is no single answer to these questions as current maturity of AI, different motivational aspects and embedded disadvantages impact into optimal location.

Information technology

If company is having IT organization with clear, tightly business driven, strategy and it also has control over company’s data assets, it could be right place to orchestrate the use of AI. So in this case you could either build analytical capabilities within the IT organization or transfer individuals or teams located elsewhere into IT.

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Motivational driver for this is the fact/assumption that analytics are heavily dependent upon both data and software. So, experience on both of these is most likely to reside in an IT function. This experience is then harnessed to serve wide variety of organizational functions, which naturally demands in-depth understanding of their needs and challenges. Additional benefits is that analytics organization is this way also closely aligned with many other typical IT functions, especially those possessing access to data assets like business intelligence and data warehousing teams.

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There are also some potential disadvantages of this approach. IT function is often loaded with many operational responsibilities and sometimes even with different larger scale transformative activities. This might impact into the speed of IT functions capability deliver analytical solutions. This is naturally normal prioritization issue, which can easily be solved tightening the cooperation and focusing on business goals over technology. Other potential downside is actually related to same dilemma: overemphasize of the technical components of analytics. This might lead to lack of adequate focus on business, skill, cultural or organizational issues.

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Next part 2: Shared Services as location for AI and analytics.