What is AI adoption? A comparison to the process of learning to drive a car

AI is a competency—for both people and companies. Just as any individual who wants to learn how to drive a car, every company should approach AI in the same way. To do this effectively, it is important to understand the concept of AI adoption. This is the process by which a company or its employees acquire this skill (think of it as the “learning to drive” process) by integrating technology into tasks, processes, and operations. Most often, this is done using existing solutions (AI products) or in-house solutions (automations/AI agents). Depending on the needs, the result may be practically invisible (such as classifying customer inquiries), or it may completely change the customer experience (for example, by implementing a chatbot to handle inquiries on the website). What does it consist of? In the following sections of the article, I will present the areas of AI adoption, comparing them to the process of learning to drive a car.

First and foremost, people. Think back to when you started your driving course. Every element was new—from the gear shift and the steering wheel to the mirrors. For the first two months, you drove on simple roads, and your instructor regularly supported you as you learned. The same applies to AI—the first three months are critical for a company to “get to know the car,” “learn how to drive it,” and then “be able to tackle more challenging roads.” 

Secondly, technology. Imagine AI as a car. You have the engine, the body, the fuel, and the driver. In AI, the engine is the AI model—the part that predicts and provides answers or recommendations. Then you have the body—think of this as the interface you use to interact with AI, which could be a chat window, a report, or even a pre-written email. Next comes the fuel—in AI, this is typically what powers your tool: company information, sales data, or specific events. Finally, there is the driver—without one, the car won’t move. It’s the same with AI—in the process of adopting AI, the “car itself” is useless if you don’t have a good driver. 

Thirdly, the environment. When you drive a car, you travel on specific roads, follow regulations, and share the road with other drivers (not just cars). It’s the same with AI—for it to work well, you need “roads,” meaning the technical infrastructure, whether it’s a public cloud or your own servers. Next, the regulations—in the AI adoption process, these are your company’s rules (for example, your AI policy), the sector you work in (industry regulations), and national or EU-wide rules (such as the EU AI Act). Finally, there are other drivers—your competitors, clients, or partners—traveling in cars, on bikes, or scooters. You decide what you will drive, and you should be aware that others are also on the move. They don’t plan to stop, they are looking for better vehicles, and with every mile, they’re getting better (which means that AI is becoming a widespread technology, and the budget has an impact on the results of your investment).

Check my client's story - APC 

Only 25% of companies believe, that building digital competencies is easy, while over 40% say they are not implementing AI because they lack the necessary knowledge and strategy. What does this mean? It means we have a shortage of people with AI competencies, and the digital skills gap is widening. 

Imagine switching from a Golf 3 to a Tesla—there are a range of features you need to learn in order to fully utilize the Tesla. In the first months, you get to know new functions: heated seats, the absence of control knobs, or even the ability to control the car with your phone. It’s the same with AI for people—if someone only has basic digital skills, they will need more time to fully leverage the potential of AI in their work. Additionally, it may turn out that they will need to go back and learn other skills, such as copywriting, English, data analysis, or financial reporting (depending on their role).

With this in mind, it’s important to recognize that, in the long run, every employee may need more than just basic AI training. It’s worth paying attention to your teams, monitoring their needs, and analyzing changes in productivity. It’s best to choose those changes that will deliver results in the short term and increase trust in your people. 

The choice of technology in the AI adoption process matters. You can buy premium-equipped cars for your drivers, but they might only use 30% of their features. It’s the same with AI—selecting the right solutions is crucial, whether it’s AI-as-a-service or in-house solutions. What’s critical in the decision-making process is:

• Evaluation of use cases - where and why do we want to use AI? 
• How will people use AI? How will it affect the experience of stakeholders?
• What products / custom solutions can help us? 
• What are we powering these AIs with? "What quality fuel will we be using?"

Decydując się na własne rozwiązania, pamiętaj o infrastrukturze i jej bezpieczeństwie. Nie zapomnij też o kierowcy - to w końcu on będzie używał AI i powinna ona być dopasowana dla niego. Najczęściej w oparciu o zespoły w firmie, dobierane są odpowiednie "AIe" (np. według funkcji jak sprzedaż, marketing czy HR). Nie zapomnij, że w całej tej "jeździe nie jesteś sam" ​

Your organization is not alone in AI. In fact, even in Poland, we have limited influence over the direction in which this technology is developing. ChatGPT alone has over 400 million users, and Microsoft Copilot is used by nearly 60% of Fortune 500 companies. How can we navigate this dynamically changing environment, where our influence on the market at the European level is limited?

First, start by assessing what your clients and employees are using. If you haven't started yet, it's not too late; the first steps in AI Adoption (training employees and selecting tools) can be achieved really quickly (2-3 weeks). It is important to strategically recognize the technology and innovation market that supports your sector. For medium-sized B2B service companies, I have prepared a dedicated workshop that helps address these challenges (for others, I encourage starting with the AI Product Map with use case evaluations). Next, plan to build this competency in-house. AI is a strategic competency and should not be outsourced in the long term. 

Check success story of Urteste

So how can we ensure that all "drivers" drive the same way, follow the rules, and avoid tickets? At the very beginning, employees will have different effects. Some will increase productivity by 5%, while others by 40%. Below are the main tips "to get started":

1. It is important to standardize digital knowledge and competencies. For individuals who are proficient in using a computer, a training package and selected products are sufficient. For others, simplifying interactions with AI, for example through a voice assistant, can compensate for slower typing.
2. As employees become familiar with the new technology, think about how you can further simplify their work. Perhaps some automation can be done without employee intervention (e.g., the first response to customer inquiries or email classification?)
3. Finally, choose those areas that will give you a long-term advantage. For medium-sized B2B service companies, this is:
4. 1. Standardization of knowledge management and supplying AI agents with it,
   2. Building "AI-first" offers and services, where the potential of tools is utilized from day 0 of the project. 
   3. Building specialized solutions based on AI, such as elite training in soft skills or AI agents for ISO certification. 

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• Don't wait - a driver won't learn to drive a car if they just watch. The same goes for companies. Start the pilot as soon as possible, choose 3 products, and offer training to employees. Once they are ready, plan further investments.
• Evaluate / analyze - just because you passed your driving test doesn't mean you can drive exceptionally well. It is important to support and monitor AI competency in the company to achieve success in both the short and long term.
• Scale - as you already know, "which cars" are the best and what works in your company, choose the next teams and "let them start driving too!"