When My Knee Injury Revealed the Future of AI in Manufacturing

A few weeks ago, I hurt my knee playing tennis.

A routine visit to the orthopaedic clinic unexpectedly turned into something else:

a quick peek into how AI is quietly transforming healthcare, and what manufacturing can learn from it.

This experience stayed with me, not because of the diagnosis, but because of the system around it.

And the more I observed it, the more I realised:

Healthcare is imperfect, fragmented, and full of contradictions — just like manufacturing.

But some parts of it are already ahead.

Here’s what I learned.

The Surprising Mix of High-Tech and Low-Tech

My journey started with an RTG and an MRI. Both were straightforward, fast, and supported by impressive technology.

But then the nurse handed me the MRI results… on a CD-ROM.

In 2025.

My car doesn’t even have a CD player anymore.

I instantly recognised a familiar pattern: a technologically advanced core wrapped in outdated processes.

Manufacturing has the same contradictions:

• state-of-the-art machines running next to paper logbooks

• real-time sensors connected to spreadsheets

• expensive systems fed by manual data entry

The first similarity was already there.

Inside the Doctor’s Office: A Glimpse of What’s Possible

Once I finally met the doctor, things got interesting.

He opened my digital case file, and within seconds, he skimmed through:

• summaries of previous visits

• AI-generated notes

• diagnostic context

• next-step recommendations

I instantly understood the value:

speed, clarity, and memory — all the things humans struggle with in high-pressure environments.

Then he opened the MRI file, activated a voice-recognition tool, and started recording his observations.

As he spoke, the system:

• transcribed his words

• translated where needed

• summarised key points using AI

• attached the notes directly to my file

This was workflow design done right:

Humans focus on interpretation, and AI takes care of routine documentation.

I didn’t expect to see this in a standard clinic, yet it worked beautifully.

Where the System Breaks Down

Then the inconsistency hit again.

My first visit with the same doctor a month earlier was very different:

• He was stressed

• He misjudged me

• He scolded his assistant in front of the patients

• Communication was rushed and defensive

A completely different human experience. 

The reception processes were also chaotic:

• phone calls unanswered

• chatbot unable to handle basic tasks

• overloaded staff trying to catch up

• long waiting times

In other words:

AI was helping inside the office, but the overall patient journey was still fragmented.

And this reminded me of factories where:

• machine data is accurate, but planning is outdated

• shop-floor insights are strong but customer service is weak

• equipment is connected but workflows are not

• people depend on mood, stress, and fatigue

The most critical touchpoints are often the ones least supported by technology.

Would AI Replace the Doctor?

At one point, I asked myself the uncomfortable question:

Could this entire diagnostic process be done by AI?

All the data is already digital.

AI can interpret scans more accurately than the human eye.

A connected knowledge base could compare my case against millions of others.

It could identify patterns in seconds, not minutes.

It would never overlook a detail, never get tired, and never have a “bad day.”

But then I arrived at the answer:

AI can diagnose.

AI can support decisions.

AI can even propose a treatment plan.

But when someone must cut into my knee, I want to look a human in the eyes.

This distinction matters — in healthcare and in manufacturing.

Humans bring:

• empathy

• reassurance

• trust

• accountability

• moral responsibility

AI brings:

• precision

• consistency

• memory

• speed

• objectivity

The future is not one or the other.

It’s a collaboration.

Where Routine Tasks Shouldn’t Be Done by Humans

After the diagnosis, I had to choose a clinic for surgery.

I checked websites, marketing material, and Google reviews.

The pattern was clear:

Positive reviews were about the technical treatment

Negative reviews were about reception, communication, and scheduling

The same difference you see in manufacturing:

• quality of the product = strong

• quality of the process around it = weak

When I contacted the clinics:

• one clinic never answered the phone

• the second offered a terrible chatbot

• but a human called me back, which immediately earned my trust

Just picking up the phone determined my choice.

A reminder that customer experience is still a human job and often the weakest link.

At the clinic itself, I was handed an iPad questionnaire instead of pen and paper.

Simple, effective, not the perfect UX, but clearly a step forward.

Then I met the surgeon.

He examined my knee, confirmed the plan, explained the surgical process step by step, and earned my trust.

This is where AI could contribute massively:

• simulate the surgery

• personalise the recovery path

• visualise the impact

• identify risks

• automate documentation

But it cannot replace the surgeon’s presence.

What Healthcare Teaches Us About AI in Manufacturing

The more I think about this experience, the more I see the same patterns in factories.

Both industries struggle with:

1. Fragmented workflows
   High-tech tools next to outdated processes.

2. Human variability
   The system works great on a “good day,” and collapses on a “bad day.”

3. Routine tasks are draining skilled people
   Doctors shouldn’t fill out forms.
   Operators shouldn’t copy data manually.

4. Lack of interoperability
   In healthcare: scattered patient records.
   In manufacturing: machines that don’t talk to each other.

5. Customer experience gaps
   Whether you’re a patient or a manufacturing client.
   You feel everything that happens around the “technical core.”

6. AI is already making a difference, but only where workflows allow it
   Diagnosis in healthcare.
   Predictive quality and decision support in manufacturing.
   Both are limited by system design, not by technology.

The Human–AI Balance We Need

My injury reminded me that the future is not about replacing humans.

It’s about designing workflows where:

• AI handles routine, repeatable, error-prone tasks

• Humans focus on empathy, decisions, creativity, and trust

This is as true for doctors as it is for:

• operators

• maintenance teams

• planners

• sales engineers

• customer support staff

AI won’t replace your best people.

But your best people will outperform others if they work with AI as an assistant.

And this is where modern manufacturing can learn something from healthcare.

Your Turn

My knee will eventually heal.

But this experience reinforced the way I think about human-AI collaboration, not in theory, but in everyday practice.

If you’re responsible for digital transformation in manufacturing, ask yourself:

Where in your workflows do humans still perform routine tasks that AI should take over?

And where do humans add value in ways AI never will?

If you’re exploring how to redesign these workflows with AI in a human-centric, Industry 5.0 way, here’s how I would approach it.

Happy to share more if helpful.