Sitting down to write this summary on July 3rd, 2025, I realize that the past six months have been one of the most intensive educational periods of my career. While it might seem paradoxical that the creator of the AI for Everyone blog and advocate for democratizing artificial intelligence knowledge was himself intensively studying, I see this as the key to an authentic educational mission - only someone who continuously deepens their knowledge can share it with genuine passion and integrity.
Navigating the Educational Maze of AI
The beginning of 2025 brought me an unexpected opportunity to participate in Google's "Skills for Tomorrow AI" program, which launched on January 27th. This five-week intensive course proved to be a fascinating journey through practical AI applications in business, with a scope that far exceeded my initial expectations.
Deep Dive into Google's AI Ecosystem
The program consisted of five modules, each opening new dimensions of artificial intelligence utilization:
Module 1: Personal Productivity & Fundamental Principles of Working with AI This is where I truly began to understand the potential of generative AI. I learned not only the basics of prompting but, most importantly, how to think in terms of creating "AI experts" - specialized agents tailored to specific business tasks. Particularly fascinating was discovering ElevenLabs and voice synthesis capabilities, along with deepening my knowledge of NotebookLM as a tool for intelligent document work. AI integration with Google Workspace showed me how artificial intelligence can be seamlessly incorporated into daily workflows.
Module 2: AI Applications in Business Development and Marketing This module completely transformed my perception of AI's role in business strategy. I learned how AI can support not only operational aspects of business but also strategic thinking for CEOs and managers. Working with AI-powered graphics and video creation tools opened entirely new possibilities for content marketing. Performance marketing with AI was a true revelation - I understood how algorithms can optimize campaigns in real-time.
Module 3: Data Analysis Using AI As someone with an IT background, this module was particularly interesting. Working with GA4 using AI showed me how dramatically analytical processes can be accelerated. RFM analysis with AI was a practical lesson in how artificial intelligence can uncover patterns in customer data. But the real breakthrough was applying AI in Google BigQuery and SQL - this opened possibilities for data analysis at a completely new level.
Module 4: AI Solutions in Google Cloud Here I felt like a child in a toy store. Vertex AI and applied AI in marketing are tools with incredible potential. But the most exciting experience was building my own AI agents and chatbots. For the first time, I could not only use ready-made solutions but create my own, tailored to specific needs.
Module 5: Organizational Transformation with AI The final module was a synthesis of all previous experiences. I learned to think strategically about implementing AI in organizations - from individual perspectives through creating company value to managing entire AI projects. This was a lesson in transforming technical knowledge into real business impact.
I remember how during the first sessions I realized how much I still didn't know about practical aspects of implementing AI in business environments. This program systematically filled these gaps while simultaneously showing me new areas to explore.
Almost by chance, while the main course was still running, I decided to take on an additional challenge - the Google AI Essentials certificate on Coursera, which I completed on February 15th. This was a spontaneous decision I made when I felt I needed deeper theoretical foundations to fully understand the practical aspects of the main program.
Foundation of AI Understanding from the Ground Up
Google AI Essentials proved to be much more than just a supplement - it was a systematic course building solid foundations for understanding artificial intelligence from the fundamentals. The program guided me through key concepts of generative AI, teaching not just how to use tools, but primarily how to think about AI strategically.
One of the most important discoveries was a deep understanding of how large language models work. The course detailed how model training, fine-tuning for specific tasks, and the inference process work, allowing me to better understand the limitations and possibilities of various AI tools. This knowledge completely changed my approach to creating prompts.
Particularly valuable was learning about differences between various types of AI models - from text-to-text to multimodal systems. The course also covered practical aspects of AI safety, ethical challenges, and responsible technology use. This was a lesson that extended far beyond technicalities - it taught me to think about AI as a tool that can have real impact on people and society.
Working with various Google AI tools - from Bard (today's Gemini) to programming tools - gave me practical experience that perfectly complemented theoretical knowledge. I could experiment with different prompting approaches, test system limitations, and learn to recognize situations where AI can and cannot be applied.
This decision to take the additional course proved crucial - it gave me solid theoretical foundations that perfectly complemented the practical skills gained in the main program. Moreover, it allowed me to speak about AI with full understanding not only of "how" but also "why" and "when.".
The conclusion of the Google program on March 3rd coincided with when I began to feel a hunger for even deeper knowledge. That's when I discovered the AIDEAS program - AI Competency Development Program, funded through EIT Deep Tech Talents Initiative support, which I started on June 6th. This course lasting until August 2nd proved to be much more than I expected - a true journey from theoretical foundations to practical skills in creating advanced AI systems.
Two-Stage Program Structure: From Theory to Practice
The AIDEAS program was intelligently designed as a two-stage educational adventure. The first part focused on solid theoretical foundations, covering key areas:
Distinguishing AI Types - understanding applications and limitations of different algorithms, allowing me to better match tools to specific business problems.
Ethical and Safe AI Use - considering legal aspects and risks, becoming my foundation for responsible AI implementation approaches.
Practical GenAI Applications - creating prompts for content, graphics, video, and audio in projects, where I could experiment with different formats of AI-assisted creativity.
Problem Solving with AI - implementing agents for task automation, opening completely new possibilities for process optimization.
Data Quality and Anonymization - ensuring AI project security, a crucial skill in the era of GDPR and growing privacy awareness.
AI Implementation Planning - adapting to professional needs and environments, teaching me strategic thinking about AI projects.
The second part of the program was a true practical laboratory where theory met reality. This is where my project of three cooperating AI agents for Saint-Gobain was born - a system that became the culmination of all acquired competencies.
Practical Project: Saint-Gobain AI Agent Team
The program's culmination was creating an advanced system of three cooperating AI agents:
Agent-Orchestrator - an intelligent concierge using Gemini 2.0 Flash, who greeted customers, provided initial FAQ responses, and directed inquiries to appropriate specialists. This was my first contact with designing conversational systems with complex architecture.
Product and Sales Agent - a versatile commercial advisor using GPT-4o, who analyzed product and pricing databases, delivering precise recommendations. Working on this agent taught me how important precision in instructions is and how to manage model temperature for optimal results.
Analytics-Feedback Agent - a business strategist operating in the background with Gemini 2.5 Pro, who analyzed interactions, collected NPS data, and generated reports. This project element showed me how AI can support not only front-end but also strategic management and process optimization.
This project was a true breakthrough for me - for the first time I designed and implemented a system where different AI models cooperate with each other, each with its own specialization and temperature adjusted to specific tasks. This experience taught me to think about AI not as a single tool, but as an orchestra of cooperating systems.
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Simultaneously, on June 6th, I started the AI Heroes training, which I completed a few days ago. This was a particularly exciting experience - a program focused on Microsoft products allowed me to look at AI from a completely different perspective than previous Google training.
Microsoft AI Ecosystem - A New Perspective on Artificial Intelligence
The AI Heroes program proved to be a fascinating journey through Microsoft's rich AI solutions ecosystem. Unlike Google's approach, which focused on accessibility and democratization, Microsoft emphasizes deep integration of AI with existing business workflows. This philosophical difference was a true discovery for me.
Azure OpenAI Service became the heart of my learning - I discovered how enterprises can implement advanced language models in secure, corporate environments. Particularly fascinating was understanding how Azure allows model fine-tuning while maintaining full data control and regulatory compliance.
Microsoft Copilot in various versions opened a new dimension of productivity. From Copilot for Microsoft 365, which revolutionizes document and communication work, through GitHub Copilot supporting developers, to Copilot Studio enabling creation of custom AI assistants. Each tool showed a different approach to integrating AI with daily work.
Power Platform AI Builder was a true revelation - the ability to create AI solutions without deep programming knowledge while maintaining professional quality and scalability. Working with this tool taught me how AI can be democratized at an organizational, not just individual level.
Azure Cognitive Services showed me a broad spectrum of ready-made AI services - from text analysis and speech recognition to computer vision and translations. It was like discovering a toolbox where each had its specific application in various business scenarios.
Microsoft Fabric as an integrated data and analytics platform with built-in AI was a completely new approach to working with big data for me. I understood how AI can be naturally woven into the data analysis process from the beginning, rather than added as an external element.
The program also covered practical aspects of implementing AI in organizations - from security and compliance management to cost optimization and solution scaling. This was a lesson in thinking about AI not just as technology, but as a strategic element of enterprise digital transformation.
Microsoft Learn - Discovering a New Ecosystem
One of the most unexpected discoveries of recent months was the Microsoft Learn platform. As part of the AI Heroes program, I completed a series of specialized training courses that opened Microsoft's wealth of AI solutions. From Azure OpenAI Service to Power Platform AI Builder - each tool represents new possibilities I can now present to my blog readers.
Systematic Exploration of Microsoft's AI Ecosystem
My Microsoft Learn adventure evolved into systematic exploration, resulting in numerous certificates and badges confirming acquired competencies. Each represents not only technical knowledge but primarily practical understanding of how different Microsoft solutions can support real business needs.
I'm particularly proud of achievements in Azure AI, where I gained solid foundations for working with Microsoft cloud services. Power Platform proved to be a fascinating world of low-code/no-code AI solutions that democratize access to advanced functionalities for non-technical users. Microsoft 365 Copilot showed me how AI can naturally integrate with daily work tools used by millions worldwide.
Particularly valuable was learning about Microsoft Fabric - an integrated data and analytics platform where AI isn't an add-on but a natural element of the entire data pipeline.
Discovering Diversity in AI Approaches
An interesting observation was the opportunity to see how technology giants approach AI differently. While Google focuses on accessibility and democratization, Microsoft emphasizes deep integration with existing business workflows. Google says "Give AI to everyone," Microsoft responds "Make AI a natural part of what you already do."
This philosophical difference is particularly visible in enterprise implementation approaches. Where Google offers powerful but configuration-requiring tools, Microsoft provides solutions that seamlessly integrate with Office 365, Teams, or SharePoint - tools most companies already use.
Wealth of Perspectives for an Educator
This diversity of approaches gives me as an educator an enormous wealth of perspectives to present. I can now show readers not only "how to do something in AI" but "how to choose the right tool for a specific problem." Need a quick prototype? Google might be better. Implementing AI in a corporate environment with existing Microsoft infrastructure? Then the Redmond ecosystem might prove more practical.
Each badge earned on Microsoft Learn is not just a technical achievement but primarily a new perspective I can share with students - from students to experienced managers seeking ways to digitally transform their organizations.
List of Training Completed on Microsoft Learn Platform
Silent Work for Better Content
I realize this intensive learning period affected the regularity of publications on my blog and social media activity. This was a difficult experience for me - on one hand, I felt the need to share what I was learning in real-time, but on the other, I knew true value would emerge only when I reflected on and systematized all acquired knowledge.
This "silent work" behind the scenes of my educational mission reminds me of a theater director who spends months preparing a performance. Audiences see only the final result, but those invisible preparation hours determine the show's quality. Similarly with AI education - to convey knowledge in a truly valuable way, I must be several steps ahead myself.
New Horizons for Training Programs
All these educational experiences weren't ends in themselves. I can already see how to use acquired competencies to create new, even more valuable training programs. Combining Google and Microsoft perspectives, knowledge of strategic AI aspects with practical implementation skills - all this creates a much richer educational toolkit.
I'm already thinking about new training formats that will consider the diversity of available AI solutions. Instead of limiting myself to one ecosystem, I can now present participants with a full picture of possibilities, helping them make informed tool choices tailored to their specific needs.
Reflections on Learning in the AI Era
This intensive learning period also taught me something deeper about education in times of rapidly developing AI technology. Each course I completed showed AI from a different angle while making me aware of how dynamically this field develops. What I learned in January already required updates with new functionalities and possibilities by June.
This dynamic forces continuous learning but also provides incredible energy. In no other technology field have I felt such a sense of witnessing history being written. Each new model, each new functionality is not just technical progress but also new possibilities for people I want to help embrace this technology.
Authenticity in Technology Education
One of the most important lessons from this period concerns authenticity in education. I cannot teach what I don't fully understand myself. I cannot promote tools I haven't tested "on my own skin." This five-month educational marathon gave me not only theoretical knowledge but primarily practical experience with a broad spectrum of AI solutions.
Now, when someone asks me about differences between Azure OpenAI and Google Cloud AI, I can answer not based on articles I've read but from my own experience. When I talk about Google AI Studio capabilities, for example, I'm discussing a tool I've used to solve specific problems myself.
Looking Forward
Concluding this intensive learning period, I feel like a mountaineer who has just reached another peak and can now look at the panorama stretching before him. Acquired competencies open new possibilities while simultaneously showing how much still lies ahead.
In the coming months, I plan to translate all this accumulated knowledge into practical benefits for my blog readers and training participants. I want to create materials that reflect the wealth of AI solutions available today while remaining understandable and accessible to those just beginning their AI journey.
Thanks to the AI Community
This learning period wouldn't have been possible without support from the entire AI enthusiast community. From course instructors through fellow program participants to my blog readers who patiently waited for new content - everyone contributed to my ability to dedicate myself to such intensive education.
I especially want to thank the organizers of "Skills for Tomorrow AI," "AIDEAS," and "AI Heroes" programs for creating learning spaces at such a high level. Each program brought something unique to my educational journey.
Conclusion - Investment in Mission's Future
Looking back at these six months, I have no doubt this was the best investment I could make in developing my educational mission. Yes, it meant temporarily limiting blog and social media activity. Yes, it required dedicating hundreds of hours to learning.
But now, looking at the spectrum of knowledge and skills I've acquired, I know each of those hours will translate into dozens of hours of better training, hundreds of pages of valuable content, and thousands of people who will benefit from a more competent guide through the AI world.
The mission of democratizing AI knowledge requires not only passion and commitment but primarily deep, authentic competence. This intensive learning period was precisely an investment in that competence - an investment whose fruits you'll be able to experience in the coming months.
Note!
This article is a personal reflection on a period of intensive AI learning. All mentioned educational programs were actually completed by the author, and the certificates obtained form the foundation for future educational activities within the AI For Everyone project. The article was developed with support from Claude Sonnet 4, an advanced AI language model that helped organize and present content while maintaining authentic messaging and educational value in accordance with the mission of democratizing AI knowledge.
