Implementation of AI products or services in the organization

Implementation of AI products or services in the organization

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Artificial intelligence is currently one of the most evolving branches of IT science and services. We are living in a time of a real revolution in terms of the number of potential solutions that will work for an enterprise. To benefit from this, it is good to have a partner to lead the implementation process.


In my article you will find a description of where I start this process for my clients:

  1. Information about what makes up artificial intelligence, its components, and whether they apply to your business.
  2. Trends that indicate the direction of development and the question of whether it is worth following them or creating new ones.
  3. Lessons learned from AI implementations in the organization, because the experience of others can help you too.
  4. Summary.


Artificial intelligence – AI


Artificial intelligence – AI for short – means giving machines human-like behavior through a process of learning from algorithms, experience and access to large data sets.

Weather forecasts are a good illustration – statistics have been collected for almost 200 years. Even so, forecasts are subject to the risk of error, although statistically they are getting better.


The basic components of AI, are:

– Learning data – datasets that are benchmarked for our model. They are a basic component of artificial intelligence systems. The data must correspond to a real phenomenon, process, market. The degree of their completeness will affect the effectiveness of the entire AI system, which you can later use.


– The right to use and technical access to the data – this is one of the absolute conditions that will ensure safe and effective analysis. Accordingly, the algorithms created for the model and the software by which we are able to use them are the second pillar of an effective AI system.


– IT environment – the effectiveness of a given AI system is influenced by how open and accessible the data resources are, where they come from, the cost associated with data verification, and the extent to which they can be updated. However, to be able to do this effectively you need hardware resources, dedicated and specific (GPU instead of CPU) for the solution. Consequently, an increase in energy consumption, carbon footprint, or hardware operating costs should be considered a fixed budget item.


– Competency market – whether there are people qualified in a given IT environment, with the access and knowledge necessary to analyze the set of values of interest.


From a business point of view, an important part of deployment planning is competition. Acquiring new customers using AI can be easier, so when planning an implementation pay attention to the behavior of your competitors. How they approach the subject, whether they have already implemented the right tools, what has had a positive or negative effect.


In summary, the application of artificial intelligence in business is made possible by analyzing and understanding the processes in which a company is capable of implementing automation. Every company relies on internal and external data to help make decisions, set the pace for change. In the course of consultation, key elements can be extracted, on the basis of which an AI system can be erected. By defining the goal, it is much easier to achieve final success.


Trends that point the way forward. Is AI worth following?


In 2017, the trend related to artificial intelligence could be described as a curiosity. Companies began to take an interest in the issue and look for the best solutions for themselves.

AI has taken on tactical importance in 2019. The advantage that can be gained is beginning to take hold and can be an important element of growth for companies.

As of 2021, it is safe to say that the importance of AI has risen to the level of a strategy element, which should be understood as a major direction for the company[1] .


However, hopes are not yet fully reflected in real-world use of the technology. Over the past three years, the level of successful implementation of AI in organizations in the US has increased only from 52 to 54%. While on the one hand that’s more than half of the companies surveyed, the dynamics seem meager. This is a direct result of the still small group of individuals and companies capable of coordinating the effective implementation of AI-based solutions.


The trend could gain momentum in the next 2-3 years. For example, a forecast for 2025 says that the 10% of companies that successfully implement artificial intelligence mechanisms by then will increase the value of their operations threefold, compared to the other 90% of orgs that will not reap the benefits of using AI.


For the time being, solutions based on artificial intelligence are tailor-made. Based on analysis, the needs and goal to be achieved by implementing the appropriate technology can be determined. As a result, companies developing new algorithms, software and organizational solutions are implementing dedicated systems effectively.


To understand the scale of the dynamics of the artificial intelligence development trend, I will cite the example of ChatGPT. The solution was made available for public testing, which resulted in the registration of one million users in the first five days. Currently, about 25 million people use the system daily. So is it worth following the trend and implementing ChatGPT in your business? Maybe it’s better to use other solutions that are better proven? How do you measure the effectiveness of using artificial intelligence in a business? To decide whether to chase trends, or perhaps, however, to quietly look for solutions that suit the nature of your business, I suggest starting with data.


I will divide them into external and internal data sources.


In the first category I include data from the market. Processed, analytics-based and verified sources that are available through certain regulations, as well as paid subscriptions. They are authoritative and allow to draw specific conclusions about the company’s environment.


To the second category I assign data, for example, from production and logistics systems, provided that the data of suppliers, subcontractors and other entities involved in these processes are legally secured. The same applies, for example, to CRM systems, where, with anonymized data and in compliance with regulations on personal data, it is possible to analyze consumer behavior that directly translates into the development or decline of the company.


What the data have in common is their objectivity. The numbers are irrefutable. Market information, charts, legislation. An in-depth analysis of how a company uses data and what data it makes available in market processes will help determine at what pace you can implement AI in your company.


What are the fears associated with implementing AI in an enterprise?


I take cultural barriers as the basis – opposition to the new technology is evident especially in the area related to decision-making and lack of faith in the outcome. Here, managers still want to leave the dominant role to themselves. Trust in AI systems is still shaky and treated as a suggestion rather than a decision that obliges concrete action.


Another element is fear – of new competencies and the changing environment. Already, from time to time, there are reports of downsizing in companies due to the implementation of AI. I consider this an abuse, because AI brings new opportunities to people who can use them effectively. For the time being, shifting all responsibility to AI for performing specific tasks is reckless, not to say stupid.


This is a consequence of the lack of human resources – people whose experience allows them to successfully implement AI in an organization. For example, a well-developed automated system for studying customer behavior on a website will acquire data for analysis, which an engineer will be able to understand, but a UX and UI specialist will, on the basis of this data, propose solutions to increase the effectiveness of the website.


However, what could I point to as the most disruptive? The lack of a strategy for approaching AI. How artificial intelligence can affect a company depends on what that company needs and whether there is such a tool on the market. If a manager can pinpoint which areas need to implement AI to improve their performance, it will be much easier for them to communicate with consultants for appropriate solutions, or convince investors to put up the funds necessary to develop a specific system from scratch.


However, if you need help in defining your goal, creating a roadmap and getting the right companies and specialists to prepare and implement artificial intelligence sytems, let’s look for effective solutions together.



But are these concerns justified?


Based on my own experience of implementing AI in companies, I see that the biggest concerns are related to a lack of proper understanding of the role the company has in analyzing data and how to use it to achieve its goals. That’s why it’s worth going deeper into this subject to avoid systuuations when, for example, during the sale of shares in a company, we fail to secure the possibility of legitimate use of internal data, or the need arises to incur additional costs associated with the need to purchase new technology capable of processing AI systems.


What is the rationale for taking action after consultation?


First, organizations that work on artificial intelligence are increasing by leaps and bounds the number of models that are then used to analyze and exploit the data. The pace of new developments is tremendous, so the chance of acquiring a specific system without having to incur the cost of designing everything from scratch increases. One must, however, be quick to orient oneself to the activities of developers.


Second, the optics of looking at the value that AI tools add are changing. You no longer ask whether this value can be added at all, but how it can realistically impact the company. Successful implementation of AI takes several months, it’s a multi-faceted process, but the desire to improve end quality among entrepreneurs will only grow. Thus, the way they compete in the market will change.


Third, it turns out that there is no special problem with getting people with the right skills for projects. Already in 2019, among the companies surveyed, optimism related to successfully finding competent people was 70%. This is a huge opportunity for IT specialists, analysts, but also psychologists or marketers who help systems understand market dependencies and prepare meaningful results.


Fourth, companies are beginning to see AI as an element of prestige. They are increasing budgets that allow them to hire security, legal, and experienced implementers who will boost brand perception by doing the right thing. AI is no longer just an incomprehensible invention of science-fiction creators. It’s a real value for any company that implements high-level artificial intelligence.




  1. Artificial intelligence ceases to scare and begins to tempt. The ever-lengthening list of potential benefits of implementing AI in an organization definitely makes it easier to decide and invest in this area.
  2. Despite the growing trend, the dynamics of AI implementations in organizations are still at a low level. This is due to several components, among which the lack of an action strategy should be considered a key one, and that the implementation process itself takes at least several months.
  3. The development of artificial intelligence is, in fact, an opportunity to combine the once-distant fields of technology and humanities. The result can be the development of a company in a new, profitable direction.
  4. Defining a roadmap and success criteria with the participation of people and companies experienced in implementing AI projects significantly increases the chance of a positive outcome and reduces the risk of incurring additional costs.









[1] Source: Gartner 2022

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