Human First: How Do We Make AI a Multiplier of Human Impact, Not a Replacement for It?

Posted on Oct 24 • Originally published at scouthub.dev

It's Thursday evening. A scout leader gets home from work, tired, with an activity to run for her troop on Friday morning. A few years ago, she would have spent two hours hunting for an idea and writing a plan. Tonight she opens Najem on Telegram and types two lines: the kids' ages, how many there are, and where they'll meet. A few minutes later, a full draft plan arrives.

That isn't the end of the story. She knows one of the boys is shy and doesn't enjoy competitive games. She knows the yard is smaller this week. So she cuts a section, changes a game, and gives that boy a special role. Then she goes to bed early and shows up the next morning with energy to spare.

Who did the work? The tool wrote the draft. She made the decisions, and she is the one who will stand in front of the kids and lead them. That is exactly what we mean by "human first."

AI is no longer a side experiment. Stanford's AI Index reports that 78% of organizations said they used AI in 2024, up from 55% the year before.

Individuals moved even faster than their organizations. A Microsoft and LinkedIn survey found that 75% of knowledge workers use generative AI at work, and 78% of those users bring their own tools without waiting for a decision from above.

The number that stops us is this one: only 39% of people who use AI at work have received any training on it from their employer.

In other words, the tool came in, but the thinking about how to use it didn't come with it. We see the same thing in associations and volunteer groups. One volunteer uses a tool to write a post, another to translate a message, and nobody has asked: what do we actually want from these tools? And what do we refuse to hand over to them?

There's also a quieter risk than mistakes. Community work runs on meeting in person. Leaders sit together, swap ideas, disagree and then agree. That's where team spirit is built, and where newcomers learn from veterans without even noticing.

When every leader gets their plan alone from their own screen, we can lose that gathering without realizing it. The plan got faster, but the team drifted further apart. So it isn't enough to ask whether a tool is accurate. We also have to ask: does it bring us together, or pull us apart?

One of the clearest studies here is "Generative AI at Work." The researchers followed 5,179 customer support agents who were given an AI assistant. The number of issues they resolved per hour rose by 14% on average.

The more important detail is that the gains were uneven. New and less-skilled agents improved by 34%, while the effect on experienced agents was minimal.

We read that through a scouting lens. The new leader is the one who needs support most. They don't yet have a store of games and ideas, or experience writing a report. A good tool can save them years of trial and error. It does not replace a senior leader who walks alongside them and teaches them.

The second study draws a clear line. Researchers from Harvard and colleagues tested more than 700 consultants at Boston Consulting Group and called the result the "jagged frontier".

On tasks that AI handles well, consultants performed roughly 38% to 42.5% better than those working without it. But on a task just outside the tool's ability, those who relied on it did worse, by 19 percentage points on average.

The lesson is simple. The tool is excellent at some things and weak at others that look very close. And it won't tell you when it's wrong. The person who knows the difference is the one who understands the context. In our work, the context is the shy child, the worried parents, and the neighborhood we serve.

People often ask us: will AI take people's places?

The International Labour Organization published an updated index in 2025 that answers calmly. About one in four workers worldwide is in an occupation with some exposure to generative AI. But only 3.3% of jobs fall into the highest-exposure category.

The ILO's conclusion is that the most likely effect is a change in what jobs look like, not their replacement, because most occupations are made of tasks that still need a human.

The World Economic Forum paints a similar picture. Its Future of Jobs Report 2025 expects about 170 million new jobs to be created by 2030, against roughly 92 million displaced. Employers also expect about 39% of today's skills to change over the same period.

What stands out is that the most in-demand skills in the report are not only technical. They include analytical thinking, resilience and flexibility, and leadership.

So investing in leaders is not a luxury in the age of machines. It is exactly what will stay scarce and valued.

In scouting and volunteer work, this is even clearer. Nobody joins a troop or an association because it has a smart tool. People join because there's a leader who knows their name, notices when they're absent, and is glad when they grow. Any technology that doesn't strengthen that leader, however advanced, stays outside the heart of the work.

When we built Najem, our first question was: what wears a leader out without adding anything to their relationship with the kids? The answer was obvious. Writing plans from scratch.

Today Najem has generated more than 5,500 activity plans for more than 1,350 leaders in over 12 Arab countries, free of charge. But the number we're proudest of isn't the plan count. It's the hours that went back to leaders to spend with their troops.

Every plan carries the same rule: Najem suggests, the leader decides. The plan is a draft, not an instruction.

We always encourage leaders not to use the plan as is. The best thing to do with it is to bring it to the leaders' meeting, talk it through with colleagues, and let everyone add their own touch. Then the draft becomes a reason to talk, not a substitute for talking. That's what we want: a tool that opens the conversation instead of closing it.

We also believe a tool's limits should be clear. When it comes to children's safety or a specific young person's situation, we don't want AI acting like an expert. Its place there is to remind the leader to go back to their supervisor and to the child's family. Technology that knows when to step back protects people better than technology that answers everything.

Smart Scout Manager (SSM) is built on the same idea. Troops run on paperwork: meeting minutes, periodic reports, attendance records. It's necessary work, but it eats a leader's time. So we let AI write the drafts, and the leader reviews and signs. The platform's slogan sums up the goal: "Run your troop with ease, and make time for the youth."

Across 2025 and 2026, the lab's projects together reached about 17,000 people. What that taught us is that the most important decision isn't what to automate. It's what to deliberately refuse to automate.

We don't automate a conversation with a child who is having a hard day. Or assessing a young person's progress. Or resolving a disagreement between two leaders. Or calling worried parents. Those are the moments that build trust, and trust can't be written as a draft.

We're not asking any association or team to reject technology, or to chase it. We only suggest pausing to ask three questions before adopting an AI tool:

If your answers are clear, the tool is probably in the right place.

The technology we want is the kind that sends the leader back out to the field, among their scouts, not the kind that keeps them in front of a screen.