The Ripple Effect of AI

Artificial intelligence (AI) has been a central driver of the latest waves of digital transformation. As software and devices become increasingly connected, data continues to grow, creating a greater need for automation. AI was developed to process large volumes of data, and organizations are turning to automation to improve their bottom lines. History has shown that every innovation that brings value has a positive impact in the end.

From my perspective, the organizations I consult with have turned to automation to fill gaps in the human asset market. For example, during the pandemic, manufacturing companies started looking at robotics and other AI-driven machinery and processes to survive in a context where human capital was limited and highly delicate.

It is possible that primary jobs, such as cashiers and customer service agents, may be at greater risk than others, as they can be easily replaced by cognitive services and specialized software based on AI. However, I believe that a whole new set of jobs will be created to deploy, manage, and develop these systems further. A new army of knowledge workers will be required to use their minds to complete tasks that cannot be automated.

However, this is not the darker side to the SkyNet era depicted in movies. While ChatGPT is just an innovation, AI is already present in our daily software. It is "eating" software, as tools such as Picasa have been completely overrun by AI, enabling searching directly on image conversion, such as looking for pets, scenery, locations, etc. Business tools are also being infused with AI, and accounting systems can now easily digest images of receipts and convert them into journal lines without any manual intervention. These algorithms are also commoditized as code to be downloaded and used on data, reducing the time to production considerably and reaping benefits quickly. The most important factor to consider in this context is security and governance of data. Not all data available can be used to generate intelligence. Regulations such as GDPR make this a complex scenario. Another primary concern of any AI model is explainability. Clarifying the lineage of any data from ingestion up to result is extremely important to ensure results are unbiased and fair.

We are in an era where digitalization is happening everywhere and in every vertical, some more than others. As cloud adoption continues to increase, more platforms have become available as a service, and enterprise platforms are reachable by a wider audience who previously could not afford such solutions. The pay-per-use concept has become the order of the day, and paying for what is consumed and used will continue to accelerate the use of AI at the forefront of the digital transformation of organizations, creating a different workplace requiring different tools and opening up new frontiers for new analytical and knowledge jobs.