The analogy of space: fly now or wait?
Recently, I learned an intriguing thought experience by astronomers who, in my opinion, perfectly illustrates the dilemma confronted with the learning of businesses today.
Imagine this scenario: in 2100, astronomers discovered a planet in the Alpha Centauri system (at only 4.4-light years) where life could exist. Humanity decides to send an expedition to it. 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. Long, but achievable.
However, technology does not remain motionless. Scientists predict that in 20 years, more advanced engines will emerge, reducing the trip from 200 to 150 years. Should the shipment are launching now, investing in huge resources, if the wait could make it faster and more efficient?
What if, in 50 to 70, technology improves so much that the trip is shortened at 100? Or, conversely, progress slows down and waiting is in vain?
Possible strategies:
- Wait for the perfect moment, but when will it come?
- Send ships after each breakthrough, but it's extremely expensive.
- Send a ship now and do not repeat it – but could we miss something important?
This dilemma is surprisingly similar to the one who faces the learning of the company today: implement the AI now or wait?
Corporate learning and AI: the same dilemma
Today, artificial intelligence transforms education. Generative models (Chatgpt, Gemini, Claude) already write training equipment, create tests and adapt content to the needs of employees. But technology progresses quickly:
- The computing power becomes cheaper (Moore's law, although slowing down, is always standing).
- Language models become smarter. The GPT-4 is already much better than GPT-3, when will it happen in a year?
- Ready -to -use tools appear faster. What recently required months of development can now be done in a few hours.
If we implement the AI now, we can get an advantage over competitors. But there is a risk that in a year or two, more advanced (and cheaper) solutions emerge, making early investments sub-optimal.
If we are waiting for the “perfect moment”, we could go late.
What strategies are possible in business learning?
1. Put it gradually, starting with low -risk solutions
We don't have to replace the entire learning system at the same time. We can start little:
- Automation of routine tasks (generate tests, answer frequently asked questions).
- Personalization of learning (adaptive courses adapted to the level of an employee).
- Use of assistance chatbots (instead of FAQ).
This approach minimizes risks and allows progressive integration of new technologies.
2. Flexible architecture: give way for updates
If AI solutions are implemented with a modular structure, they can be refined as new emerging technologies. For example:
- Use of API instead of hard coded models.
- Development of easily scalable platforms.
This reduces the risk that the system will become obsolete.
3. Parallel strategies: experience and test
We can launch several pilot projects with different technologies:
- A group of employees trains using Chatppt.
- Another through traditional LMS.
- A third through hybrid solutions.
After 6 to 12 months, we can compare the results and choose the best option.
4. Monitor the trends and be ready for quick implementation
Instead of waiting passively, we can:
- Create an internal team that follows Edtech Innovations.
- Form partnerships with suppliers to obtain early access to new developments.
- Hold the hackathons to test new tools.
This prevents us from late without immediately investing in obsolete technologies.
What if the wait is too risky? History has many examples of companies that have lost due to indecision:
- Kodak invented the digital camera but did not develop it and went bankrupt.
- Nokia dominated the phone market but could not follow smartphones.
On the other hand, there are examples of failing early adoptions: Meta (Facebook) has invested billions in the metavese, but technology is not yet ready for mass adoption.
5. The most important thing: innovative products require more than technology
The team experience and the team's internal expertise are much more critical.
If “perfect time” happens, you will need employees who know exactly what to do and how. Those who have already “learned errors” and include all the traps. Such expertise will only emerge if your organization is actively working on the development of 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” – he might never come.
- Start small – swivel projects, experiences.
- Build flexible systems so that they can be easily updated.
- Watch the trends and be ready to evolve quickly.
As with space shipping, the best option is not extremely, but a reasonable balance between action and adaptation.
The AI must be implemented in the learning of companies now, but with flexible, with the possibility of updating quickly. Otherwise, there is a risk of late forever or waste resources.
What strategy do you choose?