What University of Latvia course participants built with AI

The programme was developed in collaboration with Mikus Losāns, founder of miniAkselerators, fellow instructor Roberta Grīga, and LUMA, the University of Latvia's Lifelong Learning Academy.

The wider programme covers digital marketing. In my classes, I used the Arete AIOS business-brain approach to connect the technology with participants' own businesses and professional work. The autumn programme was still running when this retrospective was prepared.

I wrote about the start of our collaboration in the earlier FullMarketing retrospective (in Latvian). The LUMA course page (in Latvian) describes the wider programme.

The opening classroom question about ChatGPT accounts revealed different starting points.

People brought different levels of experience and access. The starting point needed to make sense to someone who had rarely used the tool, too.

A subscription alone does not tell us how well someone understands their business or evaluates an AI answer. These figures do not establish that paid-account users progressed faster.

Marketing professionals learned alongside people in health and beauty, manufacturing, crafts, education and retail. Some represented their own businesses; others brought their professional responsibilities or plans for a career change.

That variety was reflected in the questions. How do I attract customers? How do I explain my offer more clearly? How do I create content and understand what advertising has delivered? How do I find a useful AI application for my work?

A separate survey of an earlier programme group offers a view of why people choose this kind of training.

The desire for a system stands out to me in these answers. People need a clear way to fit new possibilities into their existing working day.

A business brain is organised company context: what the business offers, whom it helps, its goals and the information that matters to its work. This foundation helps AI answers stay closer to the company's actual situation.

A more useful conversation begins when AI can work with the real offer, customer questions, documents and priorities. That is why we also focused on what participants put into their own working environment.

Participants' answers showed different stages of progress.

85.2% of answers indicated at least a folder and the first files. 55.6% indicated the addition of business context or the highest self-reported stage.

These are concrete early results. A fully functioning system can be assessed when we can also see how it works and how useful it is in everyday practice.

Some participants needed more support to get started. Setup was difficult, demonstrations moved quickly, and people needed the option to revisit the materials. Clearer explanations helped them cross that first threshold.

The memorable examples involved familiar business needs. Participants described:

• using sales information to clarify product priorities;
• drafting an offer page and newsletter for their own business;
• writing product-label copy and posts, and improving websites;
• setting up an agent for recurring data collection;
• creating a Canva flyer from brand guidelines, followed by further editing;
• scheduling a task to find relevant job opportunities.

These are individual outputs and participants' descriptions of their systems. They do not establish the proportion of all learners who completed an agent, increased sales or saved a measured number of hours.

They do show a range of applications. For one person, the priority is text; for another, data; for someone else, a recurring task. AI's value becomes clearer when people find an application that matters to them.

In class, we also explored AI for research and marketing analysis. Company context helps frame a more precise question. Sources and data help evaluate the answer. Human judgement connects the two.

That was visible even in design work: a participant received an initial result, recognised what did not work and improved it. To me, that evaluation is an important part of progress.

Another programme survey asked which of the activities covered in the training people used regularly.

When asked about activities they had started or improvements they had made, 49.2% selected using AI in marketing, 49.2% selected creating content faster or more regularly, and 20.3% selected putting a new business, product or service idea into practice.

Respondents could choose several options, so these percentages should not be added together. “Faster or more regularly” cannot be converted into a measured number of hours saved, either.

The gap between content creation and data use raises a useful next question. If someone already creates content regularly, might a greater benefit come from understanding which offer, product or audience deserves attention? The survey helps us ask that question. It does not establish why usage differs or which changes were caused by the course.

International work featured in participants' plans. One was already developing exports of interior products. Another wanted to understand how a small Latvian business or artist could reach the right customers around the world.

These ambitions connect AI with a wider business question: how to understand another market and make an offer clear beyond Latvia. They were part of participants' business context. New export sales were not measured in these sources.

That interest itself shows that the conversation about AI also reaches into how Latvian people and businesses can work in a wider market.

In their closing messages, participants spoke about understandable examples, new ideas, motivation and business experience. They also spoke about the courage to try unfamiliar technology. Alongside the thanks came an honest reminder about the pace.

Read the testimonials in English*

Dina

Egils, heartfelt thanks for these lectures! ❤️ Honestly, this course exceeded all my expectations! I admit I hadn’t expected to learn so much that was new and practically useful in such a short time. 🤭 I especially appreciated your ability to explain complex things simply, engagingly and with real examples 🤗 The lectures not only broadened my perspective, but also gave me many new ideas and the motivation to put them into practice in my business 💪 Thank you for your energy, enthusiasm and willingness to share your knowledge! This is definitely one of those courses that makes you want to keep learning! 😊🌷

Eižens Leimans

Heartfelt thanks, Egils, for your openness and for sharing your knowledge and experience! It was fascinating to learn how to work with AI, and it was also very useful to hear about your business experience and approach. Thank you for reminding us of the importance of warm human connections, both in everyday life and in business! Wishing you successful projects and kind, considerate students! 🙂☀️👍

Agnese Jesse

Thank you for the course! 🙏 The words that stayed with me most, and a reminder for life: think ambitiously! Your example is like a starting signal, encouraging me to dive into a world that is still unfamiliar to me, with the ambition to get to grips with it and use it to my advantage! Thank you, thank you, thank you! 🌷🙏

Ainārs Tamisārs

Thank you for helping us take those first steps towards clearing the fog in the “dangerous and rapidly changing AI swamp forest” 👻. Even for those of us in the 56+ age group, it is exciting, although it is hard to keep up with the pace 😇

Ilze

Egils! You were especially motivating and, in my view, very open and authentic! Thank you for helping me see so many possibilities in an area I previously understood nothing about. I gained so much of value. Keep inspiring! Wishing you success in all the good things you create and do 🙏💫

Zane Vilcina

Thank you so much, Egils! Alongside the main AI topic, you also shared a range of sales and marketing tips — it was really great! 🫶

*Translated from Latvian. The screenshots show the original messages.

These messages also remind me of the teacher's job. Inspiration helps people start. To keep going, they need a clear next step and the chance to learn at their own pace.

In class, people asked questions, made choices, challenged results and improved them. That human part remains important as the tools become more capable.

I see several valuable beginnings in this experience: clearer priorities, useful drafts and attempts to make recurring work easier. A business brain becomes meaningful when it helps with a task that matters to the owner.

The quality of a first result can vary. Sometimes the context needs clarifying; sometimes a design needs editing or the data needs checking. Value emerges when people can continue the work and understand which parts of the result to use.

To explore this approach for your own company, see the Arete AIOS business-brain guide.