Training—but training for what? The strategic skills of the next decade

Strategic skills training for the future of work

The future of work is increasingly uncertain, shaped by rapid technological, economic, and demographic change. Rather than trying to predict the exact jobs of 2035, organizations should focus on developing strategic skills that help people adapt: curiosity, the ability to make decisions in uncertain contexts, and the capacity to take action. These future-ready skills will be essential for employees, managers, and industries seeking to thrive in the age of artificial intelligence and continuous transformation.

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We forget 80% of what we learn within two days!

Your Memory Has a Plan. And That Plan Is to Forget.

You sometimes walk out of a conference with the feeling that you understood everything. You finish a book telling yourself that this time, it’s going to stick. Two days later, almost nothing remains. Do you know what that means? It means your brain is functioning exactly as intended.

The Forgetting Curve

In 1885, German psychologist Hermann Ebbinghaus demonstrated what he called the forgetting curve: within one hour, we lose approximately 50% of newly acquired information. Within 24 hours, 70%. Within a month, up to 90%. In 2015, a replication study published in PLOS ONE confirmed these findings using modern methodology. One hundred and forty years later, the human brain has not changed.

Why Do You Think You’ve Learned?

Daniel Kahneman, in Thinking, Fast and Slow, distinguishes between two modes of thinking. System 1 is fast, intuitive, and automatic. System 2 is slow, deliberate, and effortful. When you leave a conference, your System 1 sends you a reassuring signal: I understood. The information feels familiar and fluent. But you confuse familiarity with mastery. Psychologists call this the illusion of competence.

Retaining information requires active recall — a System 2 activity. And that takes work. And it’s uncomfortable. And your brain doesn’t like it.

Five Minutes a Day Is Enough

Ebbinghaus had already discovered the antidote: the spacing effect. Revisiting information at increasing intervals — one day later, one week later, one month later — radically transforms retention. Microlearning is the everyday translation of this principle: short, repeated, spaced learning experiences that are accessible on your phone between meetings.

A meta-analysis published in MATHEMA: Jurnal Pendidikan Matematika in 2025 compiled studies conducted between 2020 and 2025 in higher education. The conclusion: sessions lasting 5 to 10 minutes, repeated 3 to 5 times per week, increased retention rates by 87% and produced superior learning outcomes. The recipe is simple, accessible, and costs almost nothing.

Application Is What Matters

In the 1980s, Morgan McCall, Michael Lombardo, and Robert Eichinger, from the Center for Creative Leadership, asked nearly 200 senior executives to identify what had truly developed their skills. The findings, published in The Career Architect Development Planner, led to what is now known as the 70-20-10 model: 70% of learning comes from direct experience, 20% from interactions with peers and mentors, and only 10% from formal training. Most of what you know how to do, you owe to experience, to your colleagues, and to the application of what you have learned.

What You Need to Remember

Your brain forgets the majority of what you learn. This is not a flaw — it is a mechanism you can outsmart. Review tomorrow what you learned today. Five minutes, not two hours. And above all, apply it: it is through action, through conversations with colleagues, through mistakes corrected in real time that competence is truly built. You do not need a budget or permission to get started. You just need five minutes tomorrow morning.

Gaëtan Namouric

Founder and Strategist at Perrier Jablonski.

Teacher, Speaker, Columnist, and Author.

To listen to the podcast episode featuring Gaëtan Namouric:

https://info.illuxi.com/en/podcast

Bibliography:

BOOK · Hermann Ebbinghaus (1885). Über das Gedächtnis: Untersuchungen zur experimentellen Psychologie. Duncker & Humblot. The pioneering study on memory and forgetting, which introduced the forgetting curve and the spacing effect.

BOOK · Daniel Kahneman (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. The distinction between System 1 and System 2 thinking, and the cognitive illusions that arise from them — including the illusion of competence.

SCIENTIFIC STUDY · Jaap Murre and Joeri Dros (2015). Replication and Analysis of Ebbinghaus’ Forgetting Curve. PLOS ONE. A modern replication confirming the validity of the original curve 130 years later.

META-ANALYSIS · Microlearning Effectiveness in Higher Education: A Systematic Review and Meta-Analysis of Student Retention and Learning Outcomes (2025). MATHEMA: Jurnal Pendidikan Matematika. A compilation of studies conducted between 2020 and 2025 showing that 5- to 10-minute sessions, repeated 3 to 5 times per week, increased retention by 87%.

BOOK · Michael M. Lombardo and Robert W. Eichinger (1996). The Career Architect Development Planner. Lominger Press. Research conducted at the Center for Creative Leadership involving nearly 200 senior executives, which gave rise to the 70-20-10 model.

Leroy: When AI stops being a buzzword… and becomes a real lever!

Last January, we launched an internal AI agent at illuxi that we call Leroy.

The goal was simple: respond faster, better, and at scale to our learners’ requests.

With close to 290,000 learners on the platform over the past year, one thing is clear: support requests are not decreasing—they are structurally increasing.

Industry benchmarks are well known:

  1. A high-performing SaaS platform typically keeps support volume below 10% of active users per month (Fullview)

  2. Around 8% of ARR is spent on support and customer success (SaaS Capital)

    The real question wasn’t “should we automate?” The real question was: how do we do it without degrading the customer experience?

 

The Real Challenge: It’s Not AI, It’s Data

An AI agent is not intelligent by default.

It’s only as good as what you feed it.

Since November, the real work has been: structuring our answers, documenting frequent cases, standardizing processes, and continuously enriching our knowledge base.

Leroy isn’t “smart.” He’s well trained.

 

The True Cost of Support

Take a simple example: “I forgot my password.”

It takes only a few minutes to answer.

But the real cost lies in the interruption.

A study by Gloria Mark (University of California) shows it takes on average 23 minutes to regain focus after an interruption.

Internally, we use a conservative assumption of 15 minutes per request.

At a fully loaded cost of $75/hour, that’s $18.75 per 15-minute block.

 

Our Reality After One Month of Optimization (February 2026)

We handled 810 support requests.

Without AI (100% human model): 810 × 15 minutes = 202.5 hours, 810 × $18.75 = $15,187, for an estimated total of ~$15,200.

And that’s theoretical. In reality, no employee is 100% productive—interruptions, meetings, and cognitive load increase the real cost.

 

Cost with Leroy + 1 Human

Breakdown: 421 requests handled by Leroy, 389 escalated to a human.

AI portion: $345 USD (support software) + $370 USD (AI usage) ≈ $950 CAD.

Human portion: 97.25 hours at $25/hour (outsourced) ≈ $2,500 CAD.

 

Total Cost with AI

≈ $3,450 CAD

 

Results

Cost without AI: ~ $15,200

Cost with AI: ~ $3,450

Savings: over $11,700 in a single month (February 2026)

And that doesn’t include 24/7 availability, faster response times, reduced friction, and freed-up time for the team.

 

Leroy, in Practice

This wasn’t a complex project.

No massive budget, no oversized team, no endless timeline.

Just a real need, existing data, and disciplined execution.

 

The Real Question

It’s not “how do we do AI?”

It’s: where are your simplest operational frictions—and how do you eliminate them?

 

What’s Next

Leroy is just the beginning.

Next steps: enrich the knowledge base, improve resolution rates, optimize human escalation, and expand use cases.

 

The Goal

Not to replace humans, but to free up time where it creates the most value.

 

Conclusion

Applied AI is not a promise.

It’s a concrete, measurable, and accessible lever.

Leroy is a simple example—and most importantly, a repeatable one.