The Space Analogy: Fly Now Or Wait?
Recently, I learned about an intriguing thought experiment by astronomers that, in my opinion, perfectly illustrates the dilemma facing corporate learning today.
Imagine this scenario: the year 2100, astronomers have discovered a planet in the Alpha Centauri system (just 4.4 light years away) where life could exist. Humanity decides to send an expedition there. Current technology allows us to build a ship that would take 200 years to reach it, traveling at 2.2% of the speed of light. A long time, but achievable.
However, technology does not stand still. Scientists predict that in 20 years, more advanced engines will emerge, reducing the journey from 200 to 150 years. Should we launch the expedition now, investing enormous resources, if waiting could make it faster and more efficient?
What if, in 50–70 years, technology improves so much that the trip is shortened to 100 years? Or, conversely, progress slows down, and the waiting turns out to be in vain?
Possible strategies:
- Wait for the perfect moment—but when will it come?
- Send ships after every breakthrough—but that's extremely expensive.
- Send one ship now and not repeat it—but might we miss something important?
This dilemma is strikingly similar to the one facing corporate learning today: implement AI now or wait?
Corporate Learning And AI: The Same Dilemma
Today, Artificial Intelligence is transforming education. Generative models (ChatGPT, Gemini, Claude) already write training materials, create tests, and adapt content to employees' needs. But technology is advancing rapidly:
- Computing power is becoming cheaper (Moore's Law, though slowing, still holds).
- Language models are getting smarter. GPT-4 is already significantly better than GPT-3, so what will happen in a year?
- Ready-made tools are appearing faster. What recently required months of development can now be done in a couple of hours.
If we implement AI now, we can gain an advantage over competitors. But there's a risk that in a year or two, more advanced (and cheaper) solutions will emerge, making early investments suboptimal.
If we wait for the "perfect moment," we might fall behind forever.
What Strategies Are Possible In Corporate Learning?
1. Implement Gradually, Starting With Low-Risk Solutions
We don't have to replace the entire learning system at once. We can start small:
- Automating routine tasks (generating tests, answering frequently asked questions).
- Personalizing learning (adaptive courses tailored to an employee's level).
- Using chatbots for support (instead of FAQs).
This approach minimizes risks and allows for gradual integration of new technologies.
2. Flexible Architecture: Leave Room For Updates
If AI solutions are implemented with a modular structure, they can be refined as new technologies emerge. For example:
- Using APIs instead of hardcoded models.
- Developing platforms that are easily scalable.
This reduces the risk of the system becoming obsolete.
3. Parallel Strategies: Experiment And Test
We can launch several pilot projects with different technologies:
- One group of employees trains using ChatGPT.
- Another through traditional LMS.
- A third through hybrid solutions.
After 6–12 months, we can compare results and choose the best option.
4. Monitor Trends And Be Ready For Rapid Implementation
Instead of passively waiting, we can:
- Create an internal team that tracks EdTech innovations.
- Form partnerships with vendors to get early access to new developments.
- Hold hackathons to test new tools.
This keeps us from falling behind without immediately investing in outdated technologies.
What if waiting is too risky? History knows many examples of companies that lost due to indecision:
- Kodak invented the digital camera but didn't develop it, and went bankrupt.
- Nokia dominated the phone market but couldn't keep up with smartphones.
On the other hand, there are examples of failed early adoptions: Meta (Facebook) invested billions in the metaverse, but the technology isn't ready for mass adoption yet.
5. The Most Important Thing: Innovative Products Require More Than Just Technology
Far more critical is the team's experience and internal expertise.
If the "perfect time" arrives, you'll need employees who know exactly what to do and how. Those who have already "learned from mistakes" and understand all the pitfalls. Such expertise will only emerge if your organization actively works on developing AI in learning.
The balance between innovation and pragmatism is the key to success.
Conclusion: The Optimal Strategy
- Don't wait for the "perfect moment"—it may never come.
- Start small—pilot projects, experiments.
- Build flexible systems so they can be easily updated.
- Monitor trends and be ready to scale quickly.
Just as with the space expedition, the best option is not extremes but a reasonable balance between action and adaptation.
AI must be implemented in corporate learning now, but flexibly, with the ability to update quickly. Otherwise, there's a risk of either falling behind forever or wasting resources.
What strategy are you choosing?
