LinkedIn, Frankfurt, and the Industrialization of “Professional Bullshit”

I planned to write a year end note on RTLS and Digital Twins. What is real, what is hype, where the market is actually going, and what I would bet on in 2026.

Then I opened LinkedIn. And I remembered, again, that LinkedIn has become a strange place to publish serious work.

Not because serious people aren’t there. They are. But because the platform increasingly rewards a kind of speech that Harry Frankfurt described with uncomfortable accuracy, Speech that is neither true nor false, because it simply doesn’t care. A liar must track reality to distort it; the bullshitter isn’t even constrained by reality. (“On Bullshit” is a good short read by Frankfurt.)

So yes: I’m going to say the quiet part out loud and then move on.

The cringiest artifact of this system is the “Executive Selfie” post, the one everyone “likes” and almost nobody respects.

• A leader in an airport.
• A leader in a customer lobby.
• A leader beside a booth.

Behind them, employees arranged like proof-of-life. The caption says “energy,” “alignment,” “grateful,” “momentum.” The like count surges, especially from inside the company. Not because the post is valuable, but because hierarchy has learned how to wear an algorithm as a costume.

It’s a soft pressure, and everyone feels it.

Publications like the Financial Times and The Economist have described how LinkedIn has drifted into a theater of self promotion. Earnestness, performance, and even humor are now tactics for breaking through the sludge. Cringeworthy corporate content is not an accident. It is a structural outcome.

The predictable result is not outrage.

• It’s something colder.
• Quiet disengagement.

The most competent operators I know, plant leaders, hospital admins, warehouse directors, quality heads are still “on” LinkedIn for cosmetic presence. But they increasingly conduct their real conversations elsewhere: private groups, closed communities, internal channels, direct peer networks.

Meanwhile, the public square fills with “LinkedIn’s Top Voices” who are fluent in BS and thin on consequences. That’s enough about LinkedIn!

Because RTLS and Digital Twins are one of the clearest places where the gap between performance and proof finally gets expensive.

And 2025 was the year a lot of executives stopped paying for dioramas.

The Year RTLS and Digital Twins Stopped Being Indulged

RTLS and Digital Twins are not “new.” But the market’s expectations have changed.

As someone who has spent years working directly with operations teams across manufacturing, healthcare, and logistics, I have seen this pattern repeat often enough to recognize when the tone shifts..

In 2025, we worked on RTLS and Digital Twin Technologies across three continents, spanning manufacturing, healthcare, and warehousing/logistics. What stood out was how little patience organizations now have for clever pilots and aspirational dashboards.

• Not because anyone suddenly became cynical.
• Because the world got tighter.
• In hospitals, staff safety and patient flow aren’t theoretical.
• In warehouses, labor variability and throughput pressure punish every assumption.
• In regulated manufacturing, traceability is not a “nice to have”, it’s existential.

This pressure is especially visible in RTLS driven smart manufacturing, where digital models are expected to hold up against physical reality every day, not just during demos. Buyers are no longer impressed by what could be done in a controlled environment. They want systems that behave like systems. Predictably. Operationally. Under stress. Over time.

This is the transition from interesting technology to operational infrastructure.

It’s also the phase where many single-technology emerging vendors and resellers hit the wall. Selling a pilot is not the same thing as owning the accuracy envelope, up-time reality, integration burden, and governance model for five years.

The market is beginning to reward accountability more than novelty.

Market Reality in Numbers (and Why the Range Matters)

If you follow market research, you have probably noticed something. Everyone agrees these markets are growing fast, but the absolute numbers vary widely. That is normal in emerging categories with fuzzy boundaries.

Directionally, though, the trend is clear.

• Digital Twin forecasts routinely show explosive expansion. McKinsey has cited an analysis projecting the digital-twin market reaching $73.5B by 2027 (and growing ~60% annually over a five-year period). McKinsey & Company
• Other market estimates differ on baselines but land on the same conclusion: steep growth through the late 2020s and early 2030s. Fortune Business Insights+1
• For RTLS, MarketsandMarkets projects growth from $6.68B in 2025 to $15.67B by 2030 (18.6% CAGR). MarketsandMarkets
• In healthcare RTLS, Grand View Research estimates $2.46B in 2024 growing toward $9.94B by 2033. Grand View Research These forecasts aren’t gospel

But they reflect a truth we see in the field.

Budgets are moving from experiments towards rollouts, especially where location data directly affects safety, compliance, and throughput.

Digital Twins: The Concept Won. The Reality Still Lags.

Here’s the cleanest way I can say it:

Most “digital twins” in production today are not twins.

They’re digital dioramas.

Visually impressive. Operationally shallow.

Many implementations still do three things well:

• summarize the past,
• visualize the present,
• and inspire PowerPoints.

They struggle with harder questions that actually matter:

The core failure is not modeling.

It’s ground truth.

Many Digital Twin programs, including those branded as digital twins technology driven by IoT, are built without deep location intelligence. The spatial layer is fragmented, outsourced, or treated as a procurement item instead of a foundational design decision.

Worse, some programs choose inexpensive RTLS simply to support visualization. You get moving dots. Heatmaps. A demo that sells.

What you do not get is trust.

You do not get the consistency, temporal fidelity, and contextual intelligence required for decision confidence, control loop execution, and compliance grade evidence.

• When location becomes a bolt-on instead of the substrate, nobody owns the end-to-end behavior:
• The Twin vendor points at the RTLS vendor.
• The RTLS vendor points at the environment.
• The integrator points at scope.

Operations quietly stop trusting the system.

That’s not a technology problem.
It’s a systems-thinking problem.

And it’s exactly why “Digital Twin ROI” has been such a noisy conversation. The ROI is real when the twin is anchored in reality. When it isn’t, you get a diorama that looks like insight.

RTLS: From “Asset Tracking” to Operational Intelligence

RTLS has been both helped and harmed by its early success.

Asset tracking was easy to sell, easy to explain, and easy to justify:

• “We lose stuff.”
• “We waste time looking.”
• “We’ll buy tags.”

And in many environments like hospitals, warehouses, maintenance operations the simple tracking pays for itself.

But asset tracking also narrowed how RTLS was perceived. It framed RTLS as a utility for “finding things,” not as infrastructure for “running things.”

The real inflection happens when RTLS stops being a scavenger hunt tool and becomes a truth layer:

• an always-on event stream,
• a spatial record of work,
• a system that exposes queues, constraints, and interactions.

When done right, RTLS isn’t “where is the thing.”
It’s “what is actually happening.”

That distinction is foundational for digital twin driven smart manufacturing and other high reliability environments.

Warehousing and logistics: We are seeing real-time visibility into pallet dwell and choke points, especially as automation and robotics scale. Walmart’s plan to track 90 million grocery pallets isn’t a vanity project. It is operational leverage. Financial Times

Healthcare: RTLS is increasingly justified not just by asset utilization, but by staff safety and workflow orchestration. One Georgia health system deployed location-tracking badges for 10,000 staff for duress response and operational automation again, not a toy use case. Business Insider

Regulated Manufacturing: This is where RTLS becomes powerful when it generates objective evidence: who interacted with what, when a critical step occurred, and whether assets stayed in controlled zones.

Why Executives Are Finally Prioritizing Location Data

• For years, executives could afford to ignore RTLS. Not because it lacked value, but because organizations had buffers.
• labor stability,
• manageable variability,
• and enough margin to tolerate blind spots.

Those buffers have thinned.
By the end of 2025, three executive realities are converging:

1) Throughput is now constrained by variability more than capacity
In warehouses and plants, the limiting factor is often not equipment, but coordination. And coordination requires shared, real-time truth.

2) Risk has moved closer to the floor
Safety, compliance, and security are no longer abstract reputational risks. They are daily operational risks. RTLS turns risk into measurable events and auditable history.

3) AI and optimization finally have something to stand on
AI in operations is hungry, but it’s also fragile. It can only optimize what it can observe.

McKinsey’s Technology Trends Outlook highlights how digital and physical systems are increasingly intertwined, while also calling out the messy constraints that slow real deployment. Labor shortages. Supply chain delays. Infrastructure limitations. That observation matters because it’s exactly where location intelligence lives: in the messy intersection of digital ambition and physical constraint.

Put differently, executives did not “discover” RTLS in 2025.

They discovered that without real time ground truth, many of their biggest bets on automation, AI driven decisions, and digital twins remain expensive theater.

What Is Actually Working in RTLS and Digital Twin Deployments

A pattern is emerging across manufacturing, healthcare, and logistics:

1) Systems owned by operations, not innovation teams
Projects succeed when ownership sits with the people accountable for outcomes, not experimentation. The strongest deployments feel almost boring. They run. They hold up. Over time, they become infrastructure rather than initiatives.

2) RTLS integrated into real workflows and control points

• The value does not live in dashboards. It lives in alerts, gates, triggers, and decisions that happen automatically, without meetings or manual intervention.

3) Digital twins that respect the hierarchy of truth
The most effective twins are hybrids:
Models + Rules + Realtime Signals
Not “AI everywhere.” Just enough intelligence, anchored in reliable inputs.

Why RTLS and Digital Twin Deployments Still Fail

RTLS and Digital Twin failures are depressingly consistent:

1) Treating RTLS like plug-and-play IT
Factories, hospitals, and warehouses are not blank white rooms. RF does not politely behave around metal, liquids, motion, dense racks, forklifts, and human bodies.

2) Confusing precision with trust
Operations will take consistent 1–2 meter truth over occasional 10-centimeter miracles that fail unpredictably.

3) Fragmented accountability
When the twin is one vendor, the location is another, the install is a third, the network is “someone else,” and operations are asked to “adopt,” the system has no spine. It will not survive.

This is why LocaXion’s stance has stayed consistent. Location data must integrate directly into proven processes and make them real time. Otherwise, you are producing another static, delayed representation that looks modern and behaves old.

Implementation details matter more than most demos admit. Install constraints. Anchor layouts based on real conditions. Network and security finalized upfront. Rigorous accuracy validation across operational areas. This is where many impressive pilots quietly fail.

What Changes for RTLS and Digital Twins in 2026

Here’s what I expect to gain momentum in 2026:

1. RTLS as infrastructure, not a project
RTLS will increasingly be treated like a plant utility. Designed once, implemented correctly, and expanded over time rather than repeatedly re-piloted.

2. Digital twins shifting from “visualization” to “execution support”
The organizations that win will treat digital twins as operational systems, not executive dashboards.

3. Consolidation pressure on single-point vendors
The market will be less patient with “just tags” or “just software” providers who can’t own outcomes end-to-end.

4. Location intelligence becomes the differentiator inside “AI for operations”
AI will not replace ground truth. It will amplify it. Without trusted spatial-temporal data, AI becomes a sophisticated way to be confidently wrong.

Ending the Conversation with Less Theatrics, More Truth

If LinkedIn were a serious professional venue, it would reward the people who can say:
“This is what broke. This is why. This is what we changed. This is what held up six months later.”

Instead, it often rewards the people who can say:
“Thrilled to share…”

So I’ll leave you with a simple position, one we live by at LocaXion:

Digital Twins don’t fail because the idea is wrong. They fail when they’re not anchored in operational truth. And operational truth, more often than executives want to admit, is spatial. It’s time-stamped. It’s messy. It’s physical. It’s location intelligence. We’ll keep building from that systems-thinking foundation, quietly, rigorously, and in ways that survive contact with reality.

And yes, I genuinely hope to see fewer executive selfies with politely smiling hostage employees in the background.

The work deserves better than theater.