The industry failure rate for large-scale legacy migrations sits around 70% (Gartner). They run massively over budget, get abandoned mid-flight, or deliver something that doesn’t actually replicate what the old system did.

The root cause is almost always the same: the organisation doesn’t know what its own system does.

The discovery trap

Discovery phases run 12 to 18 months. Teams of consultants read code, interview the last people who remember how it works, and produce 400-page specification documents that are outdated by the time they’re written. Then the actual build begins — based on specs that are already wrong.

What’s been tried

• Rehosting (“lift and shift”): moves the code to cloud. Doesn’t solve the comprehension problem.
• Replatforming: containerise the runtime. The code is still a black box.
• Manual rewrite: highest ambition, highest failure rate. Teams discover mid-project they don’t understand enough of the original system.
• Third-party consultancies: expensive, slow, and the knowledge leaves when the consultants do.

None of these solve the core problem: you cannot migrate what you do not understand.

How Atlas changes the equation

Atlas is our modernisation accelerator. It approaches the problem differently — starting with deep system analysis rather than manual documentation. The result is a level of understanding that traditional discovery processes take months to approximate, delivered in weeks.

From there, the full transformation runs through a structured, validated pipeline. Every step is tracked. Every output is verified against baseline behaviour.

What normally takes 18 months of discovery takes weeks. The output is reliable because it’s derived from the system itself — not from what someone remembers about it.

The numbers

• $1.5T global technical debt (McKinsey, 2023)
• 70% migration failure rate (Gartner)
• 12–18 months typical discovery → weeks with Atlas
• 40–60% reduction in overall migration programme cost