Matt Chinworth
Across industries, companies are investing unprecedented sums in reskilling programs to prepare employees to use emerging technologies.1 The programs are typically built around forecasts of which skills, such as data literacy, digital fluency, systems thinking, and adaptability, will matter most. Each year, when the forecasts are updated, training courses — and their related costs — proliferate.
Yet in the three years we spent studying 10 European manufacturers navigating exactly this kind of technological change, we found that the most relevant new skills workers developed were almost never the ones that had been forecast. They emerged organically, as workers and managers figured out together how to make new tools fit the existing work — or realized that they could not. In most cases, these skills were recognizable as important capabilities only to the few managers who understood where and how to look for them.
At a well-known Italian furniture manufacturer in our study, the head of the varnishing department wanted to identify and support new-skill development. Walking the floor was part of his routine, but what made him unusual was how he responded to what he saw. When workers raised concerns about equipment used on the job or devised their own ways of handling an awkward step, he carried those observations to a newly appointed head of production and negotiated changes to the workflow. He treated the production floor as the site of ongoing capability development, and his job as the connective tissue between the people doing the figuring out and the people with the authority to act on it. What he was watching grow was concrete.
At this company, the rollout of new production equipment, including computer numerical control (CNC) machinery, was steadily turning manual artisans into machine operators and digital production monitors. That meant that craftspeople’s judgment about quality and finish now had to be expressed through digital settings and on-screen interfaces. The varnishing head’s own emerging skill was a form of shop-floor diagnosis and reengineering: spotting where a new machine hindered or disrupted the work and devising a fix for it. It was a bricoleur capability that sat between hands-on craft and process engineering — one that no job description had ever named.2 The workflow changes that he negotiated were the visible trace of that skill taking shape, within both him and the workers.
Our research found that the companies managing technological transitions most successfully were not the ones with the best skills forecasts. They were the ones whose managers had developed a particular habit of attention — one that let them see what was already emerging in the work and to harness it before an employee walked out the door with an emerging skill. We call this practice SPOT. Later, we’ll explain what it is, why it matters now, and how to start doing it within your own organization.
The Forecasting Trap
Walk into a large company today and chances are you will find someone building a future-skills matrix. The rationale is simple: If we can name the skills we will need, we can train for them in advance. But this logic does not survive contact with an actual shop floor.
From 2023 to 2025, as part of the Horizon Europe Up-Skill project, our team conducted ethnographic field work in 10 companies across Europe, from a large automotive manufacturer to small artisanal workshops. Each was adopting advanced manufacturing technologies, such as collaborative robots, mixed-reality training systems, and 3D printers.3 We watched these companies discover that the skills they needed became visible only after the new technology they had introduced collided with work on the ground.
At one company in Sweden, to help workers learn lock-assembly procedures, managers introduced a mixed-reality system — a headset-based class of tools that overlay digital guidance directly onto the physical workspace, blending elements of virtual and augmented reality.4 The visual aspects of the system could show workers what to do but could not convey the reasoning behind the steps. Workers and managers eventually developed workarounds together, and the company found that the tacit understanding of the process the system was supposed to capture was the very thing it could not. At another Swedish firm, a plan to automate a grinding operation fell apart because the automated line could not replicate the judgment of experienced human workers. Only when the automation failed did the depth of the human expertise become visible.
But the more revealing part of each story is what the workers built next. At the lock-assembly company, the gaps in the system became the catalyst for developing genuinely new skills: Workers learned to program, re-sequence, and troubleshoot the system themselves, and they worked out how to teach the unwritten “why” that the headset left out (for instance, why a particular part of the lock should or should not be greased) so that the reasoning could pass from one person to the next. At the firm whose grinding line resisted automation, a new digital system for tracking production had a parallel effect: As operators worked with it, they began to read how their own task fed the wider flow of the line — a kind of systemic awareness that the job had never previously demanded.
It is worth separating two things in these cases. What the machine could not do exposed a skill the workers already had; what the workers built around its limitations was the skill that was genuinely new: the programming, the teaching of the “why,” and the new perspective on the whole production line.
A third firm, a small Italian manufacturer of high-end accordions, watched a competitor adopt robots for a sensitive manual step. It decided not to follow suit because it suspected that the competitor was automating away something the robot could not replicate.
We observed a pattern: The skills that matter most during a technological transition are the ones that surface when the new tool meets the old workflow: when something breaks, when a worker improvises a fix, when a manager notices that the thing the machine cannot do is the thing the customer is actually paying for. You cannot forecast what has not yet emerged. So the question for leaders is not “Which skills will we need next?” It is “Which skills are already trying to grow inside our company, and are we paying enough attention to notice?”
The SPOT Framework: Seeing and Growing Emerging Skills
We developed SPOT — a mnemonic for see, partner, orchestrate, transform — as a framework for capturing the habits we observed among the managers who were best at identifying, stabilizing, and retaining emerging skills. Let’s explore each of the four elements.
See the invisible. Most managers walking a production line look for problems, but an emerging skill does not look like a problem. The head of the varnishing department we mentioned earlier was not scanning for failures. He was scanning for moments when someone was solving a problem the system had not anticipated.
The skills you are trying to see are ones the worker cannot yet fully articulate. If you ask, “What new skill are you developing?” you will get a shrug. The better questions are about the task: What is this machine doing today that it was not doin
