If you've already rolled out a digital twin, you've probably heard some version of this: everyone was excited at launch, and a few months later, almost no one opens it. A peer-reviewed systematic review published in March 2026, analyzing 160 studies, shows this isn't unique to any one company. This article looks at why an adopted digital twin so often fails to take hold internally, and which kind of tool actually solves that problem.
A systematic literature review published in Buildings (MDPI) analyzed 160 peer-reviewed studies selected from 463 records published between 2018 and 2026, using a PRISMA-guided process with inter-rater reliability testing (Cohen's κ = 0.83). The review cites a three-level maturity framework proposed by Kritzinger et al.
The review states plainly: "Most current construction implementations operate at Level 1 or are transitioning toward Level 2, while actual Level 3 digital twins remain out of reach in most real-world construction contexts."
If you've already adopted a digital twin, this distinction probably sounds familiar. The initial project usually succeeds at scanning the site and building a model (Level 1). The problem shows up afterward — that model rarely becomes a living data layer that gets continuously updated and actually used across teams (Level 3). More often, it becomes a file that one person opens occasionally.
The review's most specific analysis centers on the handover stage — the transition from construction to operations. This is usually where the stalling begins.
Several concrete mechanisms show up repeatedly.
These four factors compound each other, which is why a project that worked well at launch quietly falls out of use over time. The review also notes that these limits become especially visible when organizations try to scale beyond a single pilot — a tool that worked on one site doesn't necessarily scale into the organization's actual workflow.
If you've already adopted a digital twin, check whether three or more of the following apply. If so, you're likely stuck at the capture stage rather than actually adopted.
Each of the four failure patterns above has a specific root cause, and Beamo is built around addressing each one directly.
Updates stop because capture itself is a hassle. Tools that require specialized equipment or a dedicated crew might get budget for an initial pass, but everything after that loses priority. Beamo takes 5 to 10 minutes with just a 360-degree camera, so field staff can recapture regularly without special training. When updating is easy, it actually happens.
Systems don't integrate because each capture ends up as a separate file. Beamo stacks every capture on the same absolute coordinates, so history keeps connecting to the same spatial frame over time. Data created during construction and data created during operations don't scatter into separate systems — they stay in one continuous layer.
Accountability gets fuzzy because there's usually a single "digital twin owner," and when that person leaves, no one picks it up. Beamo is simple enough that it doesn't depend on one specialist. When responsibility changes hands, the next person can pick up capturing and using it immediately.
Organizational discontinuity happens because construction-phase tools and operations-phase tools are usually different products entirely. Beamo runs on the same platform through construction and through post-completion operations and facility management. Teams can change without needing to migrate or convert the data.
If you've already tried capturing with another tool and it didn't stick, the problem was likely never the capture itself — it was what came after: whether the structure supported sustained use.
Companies whose digital twins fail to stick share a clear pattern: they captured the site (Level 1), but that data never became a continuously updated, true data layer (Level 3). As the peer-reviewed literature shows, this isn't primarily an execution failure by individual companies — it's a structural pattern that shows up repeatedly after rollout: undocumented field changes, semantic mismatches between systems, unclear accountability, and organizational discontinuity.
Check how many of the items above apply to what you're using right now. A Beamo demo will show you exactly how your existing data can carry over into a data layer built to keep being used.