Major system changes are some of the riskiest moves a company makes.
ERP, HRIS, CRM, ticketing, data platforms – when they go wrong, cash flow stalls, teams lose trust, and leadership burns political capital that is hard to win back.
The numbers are ugly:
- In a recent process trends report, 72 percent of IT leaders said system migration projects always take longer than expected.
- McKinsey analysis cited in a migration failure review found 75 percent of cloud migrations ran over budget, and earlier work suggested three quarters of ERP projects failed to stay on time or within budget, with around two-thirds delivering negative ROI.
- An Experian backed study on data migrations found 64 percent of projects went over budget, only 46 percent were delivered on time, and fewer than 70 percent were considered successful.
- A separate summary of Gartner research notes that nearly 70 percent of data migration projects fail to meet their objectives due to underestimated risks and poor planning.
This is not a tooling problem. It is an operations and workflow problem.
This post is about how to de-risk those big moves.
1. When “Go Live” Breaks The Business
You have probably heard or lived some version of this story:
- New ERP goes live
- Invoicing slows down, finance cannot close on time, and nobody trusts the reports
- Support tickets spike, people revert to spreadsheets, and leaders ask why the old system was turned off
The research behind that pain looks like this:
- System migration projects routinely take longer than expected, and downtime and delays are often treated as an unavoidable tax on modernization.
- Cloud and ERP migrations frequently run over budget and miss performance goals, with a large share failing to meet the original business case.
- Data migration specialists point out that projects often suffer from data loss, corrupted records, and broken reports when teams skip proper planning, testing, and validation.
The pattern: the tool changes, the underlying workflows and data do not, and the business pays the price.
2. Why Big System Changes Go Wrong
Under the hood, most failed migrations share a similar set of operational mistakes.
Tech-led, process blind
- Projects are scoped around modules, features, and vendor timelines
- Legacy processes are poorly understood, and edge cases are not mapped
- Celonis highlights “poorly understood legacy processes” and lack of pre-standardization as core migration risks.
Too much change, all at once
- Teams try to redesign processes, switch systems, clean data, and change org structure in one leap
- Migration research notes that long, complex programs delay ROI and increase disruption to day-to-day operations.
Weak data foundations
- Dirty, conflicting data is lifted and shifted into the new system
- Data teams warn that migration mistakes lead to lost records, broken reports, and compliance risks when validation and reconciliation are underfunded.
Underestimated adoption and training
- Users are trained on “how to click” rather than “how the new workflow actually works”
- Celonis and others list poor user adoption of new systems and processes as a major reason migrations fail to deliver value.
When you stack these together, you get exactly what the stats describe: long, expensive projects that destabilize operations instead of strengthening them.
3. Operational Principles For Safer Migrations
You cannot make large system changes risk-free, but you can stop treating them as pure IT events.
Four principles show up again and again in research and in the migrations that actually work.
1) Map how work really runs before you touch the tool
- Use process mapping and, where possible, process mining or task mining to see real flows, rework loops, and exceptions
- Analysts covering ERP and system modernization argue that process mining should be standard practice to de-risk migrations, because it replaces arguments about how work should happen with evidence of how it does happen.
2) Treat data quality and governance as scope, not a side wish
- Define systems of record and data owners up front
- Clean and reconcile key data sets before migration, not after
- Data migration checklists from specialist firms emphasize planning, phased moves, backup and rollback, and continuous validation to avoid hidden integrity issues.
3) Stage change by value stream, not by software module alone
- Plan cutovers around end-to-end journeys like order to cash, hire to retire, or case to resolution
- Limit day one changes to what the business can realistically absorb
- Use pilots or phased rollouts to learn before forcing the entire organization onto the new platform
4) Design for adoption, not just go live
- Co-design workflows with the people who actually do the work
- Provide training that explains why the process is changing, not just which button to click
- Monitor conformance and adoption post go-live using the same process intelligence tools you used for design
The migrations that go relatively smoothly are usually the ones that follow these principles, whether they use that language or not.
4. A Practical Migration Readiness Checklist
If you are looking at a major ERP, HRIS, CRM, or ticketing change, use this as a preflight check.
Process readiness
- Do we have clear maps for the end-to-end journeys this system touches?
- Have we identified the common exceptions and workarounds, not just the happy path?
- Is there a named process owner for each major flow, not just a project committee?
Data readiness
- Do we know which system is the source of truth for key data objects like customer, worker, product, and contract?
- Have we profiled data quality and resolved obvious duplicates and conflicts?
- Is there a plan for reconciliation and validation during and after migration?
Scope and phasing
- Have we decided what will change on day one versus later phases?
- Are we trying to redesign org structure, incentives, and process at the same time as tools, or can some of that follow?
- Is cutover planned around lower-risk windows for the business, not just vendor convenience?
People and adoption
- Do teams have time and support to learn the new workflows, or is training squeezed into already overloaded weeks?
- Is there clear guidance on what old tools or processes will be retired and when?
- Do we have a post-go-live support and feedback plan, not just a launch date?
If you cannot answer these questions with confidence, your risk profile is higher than the project plan probably admits.
How Scale Crew’s Systems & Workflow Optimization De-risks Big Changes
This is exactly the gap Systems & Workflow Optimization is designed to fill.
When we support leaders through big system changes, we focus on the parts vendors and pure IT roadmaps often gloss over:
- Pre-migration workflow and system review
- Map real-world processes across HR, CX, finance, and operations
- Surface hidden complexity, shadow steps, and fragile workarounds before you design the new system
- Data and tool rationalization before you move
- Clarify system roles and systems of record
- Retire or consolidate tools that will undermine the new platform if they stay in the mix
- Phased, operations-aware scope
- Help sequence changes by value stream so you are not flipping everything at once
- Align project milestones with operational capacity, not just vendor timelines
- Adoption design, not just training
- Co-design new workflows with the people doing the work
- Build feedback loops and simple metrics, so you see where things are breaking and can respond fast
We are not there to choose a logo for you. We are there to protect your business from avoidable self-inflicted damage while you modernize.
One Question Before You Sign The SOW
If you have a major system change on the horizon, ask this in your next leadership or steering meeting:
Who owns the process side of this, not just the tool side?
If there is not a clear answer, you are betting a lot of money and reputation on luck.
Start with a migration readiness assessment that looks at workflows, data, and people, not just feature lists and licenses. That is the difference between an upgrade that strengthens your operating engine and a “go live” that quietly breaks the business for six months.
