The Platform Is Ordinary – the Timing Is Perfect

01 /The Concept

Scholé claims to offer the most effective way to make any team genuinely AI-native – meaning employees actually know how to use the AI tools they already have access to.

The structure is familiar: courses broken into lessons averaging 20 minutes each, available to watch or listen to. Students earn points for completing lessons – and for every 10 minutes of progress – which companies can convert into employee bonuses however they see fit.

Before starting a course, the platform identifies the learner's role and which tools they use day-to-day, then tailors the curriculum accordingly. It also assesses the learner's current skill level – and keeps adjusting throughout the course based on their pace and performance. Move too slowly and the content simplifies. Progress quickly and it gets harder.

Completing a full course earns bonus points and unlocks personalized recommendations for what to take next.

Learners can choose their own courses, but HR managers and team leaders can also assign mandatory courses to specific employee groups or individuals. Dashboards let managers track who's completed what and get a real-time picture of AI tool adoption across the organization.

Scholé is headquartered in Switzerland and was founded just last year. Despite this, the platform has already been adopted by major US companies – including Visa, Walmart, CVS, Nvidia, and Microsoft – which might have something to do with Scholé developing its curriculum in partnership with Harvard University.

The startup recently raised its first funding round at $3 million.

02 /Why It Matters

Nothing about Scholé's platform is technically groundbreaking – except, perhaps, the adaptive content mechanism that tailors lessons to individual roles and skill levels. Though at this point, that should be table stakes for any modern learning platform, and isn't technically difficult to implement.

What makes Scholé interesting is entirely the problem it's focused on. As the startup puts it: "Most companies already have AI. But very few are actually using it."

This is a well-documented reality. A McKinsey survey found that only 7% of companies have fully scaled AI adoption. Another 32% are running initial experiments. 30% are still in pilot mode. The remaining 31% are just beginning the rollout. Companies understand AI is necessary – they're just struggling with what to actually do with it, and how to get employees to use it in practice.

Another detail from the McKinsey data: AI adoption leaders are disproportionately large companies. That's exactly why Scholé has been able to land large enterprise customers.

But large enterprises are a finite pool. There aren't enough of them to support every startup that decides to enter AI workforce training. Does that mean the space is too crowded? Not necessarily.

The DoorDash playbook offers a useful lens here. When DoorDash launched, large food delivery services were competing intensely in dense urban centers – where order density made unit economics attractive. DoorDash deliberately avoided those markets and focused on suburbs, where density was lower and margins initially worse. But DoorDash recognized that most people in the US live in suburbs and only work in cities. As home delivery became habitual, their target market exploded.

The principle: don't compete with incumbents on their terms. Find the geography – literal or metaphorical – they're currently ignoring because it looks unattractive today. Then make the bet that it will grow.

Applied to AI workforce training: large companies are the "city center." Small and medium businesses are the suburbs. Less revenue per client today, but enormously more numerous – and they'll face the same AI adoption pressure as large companies, with fewer internal resources to address it. That's the underserved market that will be very difficult to ignore in five years.

03 /Opportunities

The broad direction is building platforms and services that help companies actually deploy AI in practice.

The simplest starting point is a course-based system like Scholé. That alone creates a foothold – and from the inside, you get a clearer view of what else companies actually need on the path to AI adoption.

From there, the directions diverge.

Workhelix ([covered here](/review/jeto-ne-gemorroj-a-vozmozhnost-eshhjo-bolshe-zarabotat)) raised two rounds totaling $30.3 million for a platform that helps companies identify which employee categories will see the biggest ROI from AI adoption – and matches them with the right tools. The platform maintains knowledge bases on job functions and AI capabilities to power those recommendations.

Hupside ([covered here](/review/kak-ne-stat-odnoj-iz-95-neudach)) took an entirely different angle: the argument that AI effectiveness depends not just on the tool, but on the person using it. Someone with genuine original thinking, combined with AI, will generate far more value than an average user with the same tool.

So Hupside is building a platform that measures what it calls a "human originality index" – identifying the employees most likely to create outsized value in an AI-augmented workplace. It raised $1.7 million at the pre-seed stage last September.

AI adoption is a massive, long-duration opportunity. Its potential has barely begun to surface. And every new wave of AI advancement resets the training and integration cycle – meaning whatever platform wins today will face renewed demand when the next generation arrives.

So: from which angle, with what product, and for which customers can you help companies make AI actually work inside their organizations?