A take on AI adoption through IT adoption lenses

Technology adoption has become an integral part of many businesses’ growth strategies. Information Technology (IT) has been at the forefront of this movement, providing businesses with the tools they need to automate and streamline their processes. However, with the rapid advancement of Artificial Intelligence (AI) technolog and the fuss it has created among people – businesses are starting to explore its potential benefits.

Let us start with some foundation. IT adoption refers to the process of integrating technology into a business to enhance operations, improve productivity, and reduce costs. This technology could include hardware, software, and network infrastructure. In most cases, businesses adopt IT to automate tasks that were previously manual, such as data entry, customer service, and inventory management.

Whereas AI adoption refers to the process of integrating artificial intelligence into a business to automate complex tasks, analyze data, and improve decision-making. AI technology includes machine learning, natural language processing, and computer vision. AI adoption can be used to enhance customer experiences, improve product recommendations, and optimize supply chain operations.

Yes, we know it is quite different thing. But how?

The primary difference between IT adoption and AI adoption is the complexity of the technology. While IT can be complex, AI is much more sophisticated and requires specialized skills to implement and maintain. Additionally, the potential benefits of AI adoption are much greater than those of IT adoption, although the costs of implementation and maintenance are also higher.

Let’s look at few areas:

• Capability to deliver results: with IT it is relatively easy to predict expected results with measured performance, features and well structured success criteria… which is nearly impossible with the AI as we know it for now. Main reason? In many cases we simply cannot predict how AI will behave with assigned task and available data. Whereas time to deliver is hard to assess with new IT projects it becomes almost impossible to assess when it comes to AI.

• Skills and subject matter experts available – in IT they are mostly “in-house” or easily outsourced whereas in the AI the skills needed are hard to predict and subject matter expertise goes from defining the problem through carefully choose data-input to assessing what is an expected result. Highly subjective work very dependable on data-science skills which are scarce in almost every company.

• Depth of integration – as with IT systems/applications the connections are usually well defined, available (API, protocols) and the data-flow requirements are measurable… in case of AI it is often managed “on the run” as new requirements show while running the tests for solving business problems. Again – data science is crucial to assess the needed depth of integration.

Conclusion?

AI is much more complex than IT and requires specialized skills to implement and maintain. Additionally, AI has the potential to automate more complex tasks and provide insights that would be impossible for humans to detect. However currently there is no way to measure what AI is capable of, thus it is extremely rare to assess what data is needed and how it will affect overall IT structure performance.

We are in the dawn of AI – let’s see the full picture when the sun raises further.