Why the future of work will change

The future of work started long before AI

The future of work did not start with AI.

Technology innovation has always been a continuous process that helps organisations work faster, safer and smarter.

It started the day we put machines between people and their tasks. Ford’s assembly line transformed car building from a craft into a repeatable, standardised process. From that moment on, “technology-scaled labour” became just as important as human skill.

Over the last century, every major wave of technology did something similar: mechanisation, electrification, computers, robotics, software, and now AI. Each wave changed what “productive work” looks like, and rewarded the organisations that adopted it early.

Today we are at a new turning point. AI, combined with augmented reality (AR), is changing how we:

• Capture and reuse corporate knowledge
• Train and support technical employees
• Scale expertise across sites, shifts and even countries

Below I outline four trends that together explain why “faster, safer, smarter” is no longer a nice slogan, but the new baseline.

Trend 1: technology has changed how we communicate and solve problems

Most work-related communication no longer runs via the local expert walking over to a machine.

Technicians, planners and engineers now use:

• Email and messaging apps
• Teams/Zoom and remote support tools
• Ticketing systems and digital service portals

Diagnostics that used to be done by a local mechanic are now partly performed by factory analytics systems. The factory learns from usage data, decides when to trigger a recall, and pushes standardised fixes to dealers at scale.

In many companies, more communication with colleagues, suppliers and OEMs runs through digital channels than via face-to-face conversations or phone calls. For complex problems, several experts (often outside the company) need to be involved at the same time.

This has two important effects:

1. Knowledge scatters
   Expertise spreads over inboxes, chat histories, meeting recordings and external partners. The better you are at capturing and structuring that knowledge, the faster, smarter and safer you can work.

2. Speed becomes a differentiator
   The company that can route a question to the right expert in minutes will beat the one that needs days of internal emails and coordination.

To keep up, organisations need systems that not only connect people, but also capture and structure what is being said, so it can be reused at scale.

Trend 2: technology has changed how we learn

For decades, learning at work meant classroom training, binders and thick manuals. We learned by reading text.

We now know that learning improves when multiple senses are involved: seeing, hearing, movement and touch. That helps explain the rise of online video as a preferred learning format.

YouTube has quietly become one of the largest learning platforms in the world. In one survey, 71% of tech employees said they use YouTube as a learning resource, and more than half of B2B buyers use video, including YouTube, to research purchases before making a decision. :contentReference[oaicite:0]0 Video is also one of the fastest-growing formats for internal training and knowledge sharing. :contentReference[oaicite:1]1

In other words: traditional manuals are being replaced by searchable, visual, on-demand instructions.

This again has two effects:

1. Technology enables faster learning
   People are used to instant access to knowledge. They expect short, visual, contextual explanations instead of long documents. Training that uses more senses helps them learn faster and retain more.

2. Training becomes more individual
   Employees expect to access exactly the instruction they need, the moment they need it, on the device they have in their hand.

These shifts fit modern learning behaviour, but they introduce a new risk: if you do not structure your own company knowledge, your people will fall back on “random internet advice”. That is a problem for quality, safety and compliance.

Trend 3: technology adoption is accelerating

A third trend is the speed at which new technology spreads.

Historically, it took decades for technologies like the telephone, electricity or television to reach tens of millions of users. Today, this happens in months, sometimes weeks.

ChatGPT is a clear example. It is estimated to have reached around 100 million users within about two months of launch, making it one of the fastest-growing consumer applications in history. :contentReference[oaicite:2]2

Why do new tools spread so quickly?

• Digital infrastructure (devices, networks, cloud) is already in place
• People are used to learning new apps and interfaces
• Business models are designed for rapid global scaling
• Network effects reward early adopters

For companies, this creates a new operational question:

Are we able to adopt new technology fast enough to keep our competitive position?

If your organisation needs years to standardise a new way of working, while competitors roll out AI- or AR-based training and procedures in months, you risk falling behind on productivity, safety and quality.

Trend 4: a new labour market reality

The workforce itself is changing.

Many experienced technicians and operators are retiring, taking decades of tacit knowledge with them. At the same time, manufacturers and technical sectors across Europe report persistent difficulties in finding qualified staff, especially for technical and engineering roles. :contentReference[oaicite:3]3

Younger generations entering the labour market bring different expectations:

• They value flexibility, learning opportunities and work-life balance
• They are more willing to change jobs if they feel unsupported or stuck
• They are comfortable learning from video and interactive content, and expect modern digital tools

Demand for skilled technicians is growing (more automation, more regulation, energy transition), while supply is constrained and more mobile than before.

The result:

• Tenure in technical roles decreases
• Vacancies stay open longer
• Corporate knowledge leaks away as people move on

“Hiring more people” or “organising more classroom training” is not enough anymore.

Why faster, safer, smarter is the new baseline

If we combine these trends, a clear picture emerges:

• Work is more complex and regulated
• Technology cycles are shorter
• Fewer experienced people stay long enough to pass on their knowledge
• Learning is just-in-time, mobile and video-first

In this context, competitive advantage will belong to companies that:

1. Capture knowledge systematically instead of letting it live in people’s heads, inboxes and chat histories
2. Deliver knowledge in context, at the moment of need, in a format that fits the task
3. Use technology to make average employees perform like experts on complex work

That is what “faster, safer, smarter” means in practice:

• Faster: employees find and apply the right knowledge immediately
• Safer: instructions are standardised, up-to-date and auditable
• Smarter: the system learns from every task, error and improvement

Why organisations embrace AR and AI to get there

All four trends point in the same direction:

We need better ways to capture, structure and deliver corporate knowledge if we want people to work faster, safer and smarter.

This is where the combination of AI and augmented reality becomes relevant.

We already see organisations using AR and AI together to:

• Turn expert know-how into structured procedures
• Guide people through complex tasks in the real environment
• Support technicians remotely, without flying experts around
• Measure training speed, quality and error patterns across sites

Adopting new technology has created competitive advantage for decades. Today, adopting AI-driven SOPs and AR-based guidance is simply the next step in that same story.

Turning new technology into standard work

The key question is not “Should we use AI and AR?” but:

Are we able to turn new technology into standard work, at scale?

Practically, that means building a repeatable loop:

1. Use AI to draft SOPs and training flows
   Start from existing manuals, videos or expert interviews. Let AI propose the first version of a work instruction or training path.

2. Use AR to guide people through tasks in the field
   Instead of only sending a video or PDF, overlay clear, step-by-step instructions in the real environment. This makes execution more intuitive and reduces interpretation errors.

3. Use analytics to refine and standardise
   Measure how long steps take, where people get stuck, which errors occur and which variants are used. Update the instructions based on real-world usage.

4. Measure learning speed and performance
   Track how quickly employees reach competence and where additional support is needed. Use these insights to improve both training and process design.

Companies that build this “adaptation muscle” can roll out new processes in months instead of years, and capture more value from AI, automation and new equipment.

Reducing pressure on scarce experts

The shortage of senior experts will not disappear. But we can change how we use their time.

AI and AR can help by:

• Handling routine guidance so experts can focus on truly complex cases
• Allowing mid-level technicians, supported by AR instructions, to safely execute tasks that previously required a specialist
• Embedding remote expert support into AR, so you can “borrow” expertise across sites instead of staffing every location with every skill set

In other words: AI and AR do not replace experts, they amplify them.

You move from “we depend on John who knows this machine” to “we depend on a living knowledge system that John helped create”.

Building company knowledge on a global scale

Once procedures are captured in a structured digital format, they become:

• Searchable: people can find the right step or variant instantly
• Translatable: instructions can be reused across languages and regions
• Measurable: you can see who used which procedure, when, and with what result
• Reusable: the same knowledge can support plants, service teams and partners

For global organisations, this is crucial. You can roll out the same standard to multiple sites, adapt for local regulations, and still compare performance.

The knowledge of your best expert becomes available in all languages, 24/7, instead of being tied to one person in one location.

How AI improves learning speed and reduces errors

AI capabilities that are already practical today include:

• Suggesting the next best instruction step based on similar jobs and histories
• Translating SOPs into other languages
• Turning raw expert video into structured work instructions
• Flagging where errors occur most often, so you can redesign the process or add extra checks

Combined with AR, this means a new technician can learn while doing, guided step by step in the real environment, instead of spending days in a classroom and then trying to remember the manual.

The impact is tangible:

• Faster onboarding
• Better retention of knowledge
• Fewer mistakes in the field

Which is exactly where “faster, safer, smarter” becomes visible in day-to-day work.

What competitive advantage looks like in practice

Organisations that get this right typically see advantages in five areas:

1. Productivity
   More work done with the same or fewer people, because less time is wasted searching for information or waiting for an expert.

2. Quality and safety
   Standardised digital instructions reduce variation, error rates and incidents, and make audits more straightforward.

3. Scalability
   New lines, plants or services can be launched without needing a full team of senior experts at each location. Knowledge scales faster than headcount.

4. Talent attraction and retention
   Younger employees prefer employers who offer modern tools and clear learning paths. A strong digital knowledge environment is a real asset in the labour market.

5. Resilience
   When key people retire or leave, their knowledge is not lost. It lives in your system and can be improved by the next generation.

Where ActARion fits in

At ActARion, we see these trends every day in manufacturing, logistics and energy.

Together with our partner DeepSight, we help organisations:

• Capture expert knowledge once and reuse it across sites
• Turn that knowledge into AI-supported SOPs and AR instructions
• Support technicians in the field with guided workflows and remote experts
• Measure how training and execution actually perform in practice

Not every company is ready to redesign all their processes at once. Often, the best next step is simple:

• Pick one high-impact use case
• Capture the current best way of working
• Turn it into an AR-guided, AI-supported instruction
• Measure the effect on speed, safety and errors

From there, you can decide how fast you want to scale.

If you recognise the trends described in this article and want to explore what “faster, safer, smarter” could look like in your own operations, a short discovery workshop or proof of concept is usually the most concrete way to start.