Major System Implementation
Improve adoption throughout a major system implementation.
Latent Variables improves execution and adoption through each wave of the rollout: it identifies the conditions affecting adoption before and during rollout, informs adjustments to workflows and rollout decisions, engages the teams whose work changes, and returns through each wave to measure whether adoption is improving.
What the program team has throughout the rollout
A continuing record of conditions across waves
Context carries across waves and go-lives, so each phase begins with the information gathered in previous phases.
Rollout decisions with evidence attached
Findings arrive connected to the workflow, wave, or design decision they affect, translated into a response the program and process owners can execute.
Coordination with the teams whose work changes
Interventions reach the affected teams directly, with the reasoning explained, and their execution constraints are reported back before the next wave begins.
Leading indicators of adoption
Repeat conversations measure whether the new way of working is holding before the effects appear in usage data and support queues.
How Latent Variables supports the rollout through each wave
Establish the baseline
Before or during rollout, conversations across the affected functions test the implementation’s assumptions: how the work currently runs, what the new process will encounter, and where the design needs to accommodate local conditions.
Build the response
Findings are connected to rollout decisions: a workflow adapted, a wave re-sequenced, enablement redirected, or ownership clarified. The response addresses the specific condition identified, rather than applying a general fix.
Engage the teams whose work changes
The affected teams are engaged directly around the response: what changes, the reasoning behind it, and what obstacles remain. Constraints are reported while the wave can still be adjusted.
Measure and adjust
After go-live and between waves, repeat conversations measure whether the new way of working is holding, identify deviations and new workarounds early, and carry what was learned into the next wave. The process continues as the rollout expands.
How findings change rollout and workflow decisions
A process step that conflicted with the workflow
The finding
In two functions, one step of the standard process ran against the order in which the work actually happens, and teams were reordering it locally.
The response
The process owner adapted the step for those functions and updated the rollout design before the next wave repeated the conflict.
The follow-up
Conversations after the change measured whether the adapted flow held, and monitored the remaining waves for the same conflict.
An approval assigned to the wrong role
The finding
The system design assigned exception approvals to a role that did not hold that judgment in practice, so exceptions queued.
The response
Leadership moved the approval to the role that already made those judgments and clarified decision rights before expanding the rollout.
The follow-up
The following conversations tracked exception queues and confirmed the new path was being used consistently.
A requirement the standard configuration could not support
The finding
One region carried a regulatory requirement the standard configuration could not represent, and the local team was preparing its own workaround.
The response
The program built a sanctioned local variant and defined the criteria for where it applies, keeping the standard configuration everywhere else.
The follow-up
Later conversations verified the variant met the requirement and remained limited to the sites that need it.
How the adoption evidence is produced
The same instrument interviews every site, role, and wave against shared campaign context, so the same intended workflow can be compared across the rollout while the fieldwork is still running.
One instrument across sites and waves
Each conversation adapts to the participant's place in the rollout while following a consistent evidence protocol, so accounts from different sites and waves can be compared directly.
Workarounds traced through the workflow
If one site reports that a workflow still requires an old tracker, the system examines the same handoff with the teams before and after it and determines whether the condition is local or built into the implementation design.
Context that persists across waves
Later waves begin from the evidence established in earlier ones, so the program can measure whether the conditions found in one wave were resolved before the next.
What affects adoption during a major system implementation
Implementation programs measure readiness, deployment, and usage in detail. Several conditions that determine adoption are difficult to observe through those measures.
Conditions vary across functions and locations
Workflows, constraints, and capabilities differ across the organization. A standardized rollout design will meet different conditions at each site, and adoption will vary accordingly.
Readiness metrics do not measure changes in daily work
Testing and training measure whether the system can go live. Whether people can run their normal work through it is a separate question that program metrics do not answer.
Adoption patterns form early
Local adaptations, exceptions, and unresolved questions accumulate from the first weeks. They often become established practice before they appear in usage data.
Bring us one live implementation decision.
We’ll describe what we would test before it, what a response could involve, and how you would measure whether the change held.
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