Research analysis
Analysis: transformation fails for organisational reasons, not technical ones
AI Cubed
August 9, 2026
8 min
McKinsey's work on transformation found that around 70% of efforts fail to deliver against their original objectives, and that the causes are most often organisational rather than technical. That finding is theirs.
Our analysis is about the practical corollary: if failure is organisational, then the decisive work happens before anything is built. These are the four conditions we check, and what we do when one of them is missing.
Failure is organisational — so diagnose the organisation
If the majority of failures are organisational, the standard vendor sequence — pick a platform, build, then worry about adoption — is backwards. It resolves the easy variable first and leaves the decisive one to chance.
We invert it. The diagnostic phase exists to test the organisational conditions, and only the processes that pass move into build.
The four conditions we check
- Ownership: one named person is accountable for the system after launch, with the authority to change it.
- Stability: the process has not materially changed in the last two quarters and no change is planned.
- Data: the inputs are consistent enough that a rule can act on them without a human interpreting first.
- Scope: the first build can be proved or disproved inside a quarter.
A process failing any one of these is not disqualified — it is sequenced later, behind whatever fixes the missing condition.
What failure looks like from the inside
Programmes rarely announce their failure. They degrade: an exception appears that nobody owns, someone works around it manually, the workaround becomes the process, and six months later the system is a licence line item nobody can justify. Every step of that is organisational.
This is the reason we run what we build. A system without an operator is a system with a shelf life.
The reframe we recommend
Stop asking whether the technology can do it — with current tooling the answer is almost always yes. Ask instead who will own the output, what happens on the day it breaks, and how you will know it is still working in six months. Those three answers predict the outcome better than any platform selection.
Frequently asked questions
This page is AI Cubed's own analysis of independently published research. The underlying study belongs to its publisher, credited and linked in full below.
Sources
- Why do most transformations fail? A conversation with Harry Robinson — McKinsey & Company
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