The development of broadly understood artificial intelligence is gaining momentum. This is evidenced by the widespread interest in this topic and an increasing number of media articles and publications. What draws particular attention are the data illustrating how much financial resources are allocated to investments in semiconductor technologies, i.e., the computational capabilities and memory of new AI data centers. Everything indicates that this development will be very dynamic in the coming years.
Reports such as “The Future of Jobs Report 2020″ prepared by the World Economic Forum and research conducted by the consulting firm McKinsey & Company highlight the dynamic changes in the job market that will take place in the coming years. Both sources agree that we will witness significant transformations in the types of professions and responsibilities, resulting from technological progress, automation, and changing economic and social needs.
In the article ” Most of 2030’s Jobs Haven’t Been Invented Yet” we can read:
“The IFTF, a nonprofit that seeks to identify emerging trends and their impacts on global society, forecasts that many of the tasks and duties of the jobs that today’s young people will hold in 2030 don’t exist right now.”
source: https://www.voanews.com/a/most-of-2030-s-jobs-haven-t-been-invented-yet/4778002.html
From the management perspective, this presents a substantial challenge. The way relationships and duties among employees of different departments evolve will significantly change the operation of many organizations. Identifying these areas and any potential consequences arising from the AI revolution will be crucial for success in the coming years.
“In my opinion, understanding the capabilities that new technologies offer and assessing how they can improve a given organization will be a key managerial skill in the coming decade”- emphasizes the CEO of Inero Software, Andrzej Chybicki.
He points out two ways for businesses to develop their technological awareness in the area of AI:
The first is to explore products or services available on the market that can be applied as ready-made solutions. The decision to implement them will depend on the extent to which these solutions can be tailored to specific processes that require optimization and automation. However, it’s important to be aware that such software is delivered in a certain “bulk” that needs to not only be implemented but also integrated with internal organizational processes to effectively cooperate with the newly implemented product.
The alternative is creating in-house AI-based solutions, which large companies are already opting for. This includes, for example, designing their own language models tailored to the individual needs and requirements of a particular enterprise. These are usually multi-million dollar projects, but some can be completed within a smaller budget.
One of the global brands that decided to create its own LLM model is Amazon. The purpose of the “Rufus” model is to support potential customers at every stage of the purchasing path. Its implementation aims to assist users and optimize the order placement process.
“At Inero, we apply a product-service approach. Having knowledge on how to solve certain problems, using our proprietary systems like DocQuality or DeliverM8 – Freight Matching Platform, we aim to present a certain solution template and its potential. Then, we tailor it to the specific needs and processes carried out within the client company’s structure, rather than the other way around. Our products provide a template of possibilities that new technologies offer.” – highlights Andrzej Chybicki.
This solution combines the good features of a product approach with the flexibility of a service approach, while the cost is more affordable. For example, the DocsQuality application utilizes machine-learning techniques for document categorization and the recognition of illegible sections of files. This program can be easily and quickly integrated with a client’s internal infrastructure, optimizing the document circulation process by detecting problematic documents.
In summary, the development of artificial intelligence in business management opens up space for innovation. The challenges associated with adopting new technologies require a strategic approach, both in the context of implementing ready-made solutions and creating internal AI systems. Success in the coming decade will depend on managerial abilities to understand and utilize technological potential, as well as skills in managing a changing team that will require the implementation of new tools, tailored to new tasks.
