The concept of digital twins—coined by NASA in the 2010s—has now passed through Gartner’s “Innovation Trigger” and hit the “Peak of Inflated Expectations.”
You can see that when, for example, «Digital Twins» catch the attention of companies like McKinsey, which presents an study about digital twins as dynamic virtual replicas of physical assets or systems.
THE GOOD
Using Digital Twins as dynamic virtual replicas of physicla assets or systems allow real‑time monitoring, simulation, predictive maintenance, and process improvement. It’s presented as transformative—across manufacturing, cities, healthcare, and energy.
The problem is, lots of people is scared about the huge notariety AI and tech tools are having and how fast all is evolving.
But how much of this is substantive innovation, and how much is just buzz?
This is the core of the problem: the media seizes flashy concepts, amplifies them, but often skims past the real impact.
At the McKinsey’s overview we can find use cases and benefits in a knowledgeable way—but it also participates in hype culture.
As reader we should wonder: what evidence exists that digital twins consistently deliver on those promises?
The Hype Cycle in Motion
Many articles—including McKinsey’s—highlight investment forecasts (e.g. tens of billions by mid‑decade), but few dive into real ROI or follow‑up stories. Research shows projects often fail because of data inaccuracy, high maintenance costs, fragmented terminology, and lack of strategic alignment.
Reddit engineers echo this frustration: the term has become so diluted that it is applied to nearly any digital model—not necessarily the dynamic, data‑rich systems McKinsey promotes.
One user writes:
“It’s a buzzword… being abused to fit any preconceived idea the implementer has.” (https://www.reddit.com/r/engineering/comments/qlx6bd/this_weeks_annoying_buzzword_digital_twin/?utm_source=chatgpt.com
Why Ghislaine Boddington’s View Matters
While McKinsey focuses on industry use cases, I had the lucky opportunity to share a workshop with Ghislaine Boddington, who offers different lens. Her Internet of Bodies research explores the deeper interface between human identity and digital embodiment.
In her BBC documentary “Me and My Digital Twin,” https://www.bbc.co.uk/programmes/w3ct7hnh?utm_source=chatgpt.com she reflects on creating a personalized AI biotwin—one that can live on, support health, or even outlive its human partner .
Boddington’s work moves beyond industrial simulations. She asks: what does it mean when our digital ‘selves’ carry personal data, emotional patterns, biometric metrics? Are we ready to treat these twins as companions—or even digital immortality?
Her perspective shifts the discussion: digital twins are not just tools for efficiency, but portals to questions about self, embodiment, privacy, longevity.
The Illusion of Engagement
Too often, the media—and consulting firms—highlight “burning news” revolutions. A flashy launch, a big investment, futuristic promises.
But what about follow‑through?
McKinsey lists multiple verticals, but rarely explores failures or setbacks. Actual infrastructure implementations often stall due to cultural inertia. John Ford, digital‑information lead in UK construction, laments that clients don’t even use the digital data they commission—rendering the concept almost fantasy.
We should rise some questions and critical reflections:
What counts as evidence? Are there transparent, documented case studies where digital twins paid for themselves?
McKinsey cites broad trends—but where are the audited ROI figures?
Who benefits—and who risks? Boddington’s biotwin model raises ethical issues: who owns biometric data? Does an AI twin survive to serve corporate interests? Are we trading autonomy for convenience?
Is the media complicit in hype culture? Does coverage revolve more around novelty than substance? Gartner’s hype cycle shows us that most media-led enthusiasm eventually leads to disillusionment—yet we rarely see the trough documented as much as the peak.
When is a twin just a model? The difference between a static simulation and a live, changing, data-fed twin is crucial. How many systems described in trade articles meet that bar? Redditors suggest many don’t: “digital twin” gets slapped on anything remotely digital model‑like
And my favorite question:
What about regulation and ethics? Privacy, security, ethical issues—and call for consent and minimization of data . But McKinsey’s article barely touches on that—even though Boddington foregrounds it as existentia
Toward Responsible Understanding
McKinsey defines digital twins as strategic assets
Boddington pushes us to think beyond capital efficiency, toward digital identity and experience.
A balanced perspective might go like this:
| Dimension | McKinsey’s Tone | Boddington’s Perspective |
|---|---|---|
| Purpose | Industrial optimization, efficiency | Human-centric embodiment and identity |
| Risk awareness | Brief mention of cyber/data risk | Central ethical concern: privacy, autonomy, longevity |
| Evidence | Investment trends, use case lists | Qualitative, speculative, philosophical narratives |
| Hype vs. substance | Promotional, optimistic | Reflective, critical, exploratory |
Digital twins, in McKinsey’s narrative, promise transformation:
better planning, reduced downtime, powerful analytics. But look closer, and you find uncertainty, expense, disconnected stakeholders, and diluted implementations. Gartner’s hype cycle and hundreds of practitioner voices urge caution: many projects fall into the trough between expectation and value.
Ghislaine Boddington’s work forces us to confront a deeper, more provocative question: what if digital twins become digital selves? What does that mean for identity, continuation, and power? Are we ready for that—not just technically, but ethically?
As readers, practitioners, or decision-makers, the challenge is clear: don’t be dazzled by the headline.
Demand the follow‑through.
Probe definitions.
Seek accountability.
Follow us for more to come 🙂