The advantage is no longer having more people. It’s knowing how to use AI better.
Not long ago, in a network operation, the usual happened.
Degraded service. Alerts. An urgent meeting.
Three senior engineers reviewing logs (technical system event records). Hours cross-checking information. Hypotheses that didn’t add up.
Two days later, the real pattern of the problem appeared.
Months later, something similar happened.
But this time, someone wrote:
“Look for trends of error X in the last 30 days. Identify if it matches traffic spikes. Check whether other services show a similar pattern. Compare it with previous incidents.”
Within minutes, a clear correlation appeared.
It didn’t solve the problem automatically. But it dramatically reduced the search space.
That changes the conversation.
What used to take days can now take hours.
According to data published by GitHub, more than 45% of code in companies that use AI assistants already includes some degree of automatic generation.
McKinsey & Company estimates that generative AI could contribute between USD 2.6 and 4.4 trillion annually in global economic value, with strong impact in software development, marketing, and operations.
That doesn’t mean teams disappear.
It means the type of work changes.
Production and incidents (MTTR, explained simply)
In operations, one of the simplest questions is: how long does it take to recover after an incident?
A common metric is MTTR (Mean Time To Repair) — the average time it takes to restore service.
If your MTTR is 4 hours and you reduce it by 30%, you go down to 2.8 hours.
In telecom or corporate IT, that implies:
- Fewer SLA penalties (Service Level Agreement).
- Fewer affected customers.
- Better reputation.
- More operating margin.
AI does not replace the senior engineer.
But it can:
- Detect patterns in millions of log lines.
- Group similar errors.
- Find invisible correlations.
- Compare historical incidents in seconds.
That frees time to think better.
And for management, that’s strategic.
Development: fewer lines of code, more architecture
Today, tools like:
- GitHub Copilot
- Cursor
- ChatGPT
- Claude
allow you to generate code, tests (testing), documentation, and refactoring.
Before, a medium module might require 4 or 5 developers.
Today you can move forward with fewer people… if someone knows how to break the problem down and control quality.
The winner isn’t the one who writes more code. The winner is the one who:
- Designs better architecture.
- Orchestrates agents.
- Detects inconsistencies.
- Maintains a global view.
This isn’t blind automation.
It’s supervised automation with judgment.
But it’s not magic
Adopting AI isn’t just using it.
In real production you must comply with:
- GDPR (General Data Protection Regulation).
- Internal security policies.
- Access control.
- Clear governance.
According to the World Economic Forum, AI governance is one of the biggest challenges for companies trying to scale adoption.
AI accelerates hypotheses.
But responsibility remains human.
Product and marketing also change
Today you can:
- Detect churn risk.
- Simulate pricing scenarios.
- Analyze behavior in real time.
- Optimize campaigns automatically.
But if everyone uses the same tools, the differentiator becomes strategy again.
AI standardizes execution. It does not standardize vision.
The interesting part is that this isn’t limited to big corporations
The shift isn’t only for telecom, banks, or multinationals.
It also reaches SMEs.
In Chile, more than 98% of companies are small and medium-sized (according to figures from the Ministry of Economy). Most operate with tight margins and little structured information.
That’s where AI starts to change the game.
NeuraPRO: when AI accelerates what used to feel impossible
While building NeuraPRO, I’ve lived it directly.
AI didn’t design the product for me.
It didn’t decide which metrics to show. It didn’t understand that an agricultural producer needs something simple, clear, and actionable.
I defined that.
But thanks to tools like ChatGPT, GitHub Copilot, and Cursor I was able to:
- Build complex financial logic faster.
- Iterate margin calculations.
- Refactor without stopping progress.
- Adjust validations until they were solid.
Without being a traditional programmer.
Today an agricultural client can see in seconds:
- Margin per sale.
- Operating margin.
- Most profitable product.
- Where they’re losing money.
Not because AI is answering them.
But because AI reduced the barrier to building the system that makes those calculations possible.
That’s the real change.
And this doesn’t stay in a single industry
Agriculture was the starting point.
But the logic is replicable.
In Chile there are thousands of small restaurants, food shops, and people who sell lunches to workers every day.
Many operate for years without knowing precisely:
- What the real margin per dish is.
- Which recipe actually generates profit.
- How much they’re losing due to bad cost estimation or waste.
Not because of lack of effort.
But because of lack of simple tools.
The same happens in minimarkets, hardware stores, and retail.
Each industry has its own dynamics.
Today, with a solid base architecture and AI support in development, it’s possible to adapt specialized solutions for each sector at a speed that used to be unthinkable.
It’s not about making generic software.
It’s about translating the logic of each industry into operational clarity.
That’s where the change really begins.
The human part
Working with AI isn’t easy.
It requires:
- Patience.
- Iteration.
- Tolerance for frustration.
- Structured thinking.
- Character.
If you don’t know how to frame the problem well, you won’t get a good answer.
AI doesn’t replace talent.
It amplifies it.
And it also amplifies weaknesses.
Conclusion
AI doesn’t change who works harder.
It changes who builds better.
Whoever can:
- Reduce friction.
- Design clear systems.
- Translate complexity into simple decisions.
- Adapt technology to each industry.
will have a real advantage.
Not because they have more people.
But because they understand what to do with this new capability before the rest.
And that, in any industry, changes the game.
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ViaMind Ecosystem
- ViaMind — The brand hub for ideas, technology and strategy.
- NeuraPRO — Practical software for managing real operations.
- Consulting — Advisory for complex AI and telecom decisions.