Space Analogy: Want to fly or wait now?
Recently I learned about interesting thought experiments by astronomers. In my opinion, it perfectly illustrates the dilemma facing today’s corporate learning.
Imagine this scenario. In 2100, astronomers discovered an Alpha Centauri (just 4.4 light-years) planet where life could exist. Humanity decided to send an expedition there. Current technology allows the construction of a vessel that takes 200 years to reach, travelling at 2.2% of the speed of light. It’s a long time, but it’s achievable.
However, the technology is not stationary. Scientists predict that more advanced engines will emerge in 20 years, reducing travel between 200 and 150 years. You need to launch an expedition now and invest a huge amount of resources.
What if 50-70 years has seen a huge improvement in technology and travel is reduced to 100 years? Or, on the contrary, does it turn out that progress is slower and waits are wasted?
Possible strategies:
We wait for the perfect moment, but when will it come? Send the boat every breakthrough. But it’s very expensive. Don’t send one ship right now and repeat it. But can you miss something important?
This dilemma is surprisingly similar to what we face today in corporate learning. Are you waiting to implement AI now?
Corporate learning and AI: The same dilemma
Today, artificial intelligence is transforming education. Generation models (ChatGpt, Gemini, Claude) have already written training materials, written tests, and adapted content to the needs of employees. However, technology is rapidly advancing:
Computing power is becoming cheaper (Moore’s Law is slower, but still retained). Language models are becoming smarter. The GPT-4 is already significantly better than the GPT-3, so what happens in a year? Ready-made tools are displayed faster. The latest months of development can be done in just a few hours.
Currently, AI can be implemented to have an advantage over its competitors. However, there is a risk that more sophisticated (and cheaper) solutions will emerge in a year or two, with early investments being the best.
If we wait for the “perfect moment,” we may be delayed forever.
What strategies are possible in corporate learning?
1. Start with a low-risk solution and implement it gradually
There is no need to replace the entire learning system at once. We can start small:
Automating routine tasks (generating tests, answering frequently asked questions). Personalising your learning (adaptive course tailored to the level of your employees). Using a chatbot for support (instead of FAQ).
This approach minimizes risk and allows for the gradual integration of new technologies.
2. Flexible architecture: Leaves room for updates
If AI solutions are implemented in modular structures, they can be refined as new technologies emerge. for example:
Use an API instead of a hard coding model. Easily develop a scalable platform.
This reduces the risk of your system becoming obsolete.
3. Parallel Strategy: Experiments and Testing
You can start several pilot projects with a variety of technologies.
One group of employees trains using ChatGpt. Another through traditional LMS. Third through hybrid solutions.
After 6-12 months, you can compare the results and choose the best option.
4. Monitor trends and prepare for quick implementation
Instead of waiting passively, we:
Create an internal team to track Edtech Innovations. You can form partnerships with vendors to access new developments early. Keep the hackathon and test the new tool.
This will prevent you from quickly investing in outdated technology and not being able to delay.
What happens if waiting is too dangerous? History knows many examples of businesses lost due to indecision:
Kodak invented a digital camera, but did not develop one and went bankrupt. Nokia dominated the phone market but was unable to keep up with smartphones.
On the other hand, there have been cases where early adoption has failed. Meta (Facebook) has invested billions in Metaverse, but technology is not yet ready for mass adoption.
5. Most importantly: Innovative products require more than technology
Team experience and internal expertise are far more important.
If “the perfect time” arrives, you need employees who know exactly what to do. Those who have already “learned from mistakes” are those who understand all the pitfalls. This type of expertise only manifests when an organization is actively working on AI development in its learning.
The balance between innovation and pragmatism is the key to success.
Conclusion: The best strategy
Don’t wait for the “perfect moment” – it may never come. Small pilot project, starting experiments. Build a flexible system to make it easy to update. Be prepared to monitor trends and scale quickly.
Like space expeditions, the best option is not extreme, but a rational balance of action and adaptation.
AI needs to be implemented in corporate learning right now, but it has the flexibility and quick updates. Otherwise, you risk staying behind forever or wasting resources.
Which strategy are you choosing?
