Salesforce optimization in Des Moines.

Probably not. A rebuild makes sense only when the core data model is wrong, for example policies stored as opportunities with no way to separate them. If the objects are sound and the problems are cluttered layouts, overlapping automation and unreliable reports, optimization costs far less and keeps your history intact. A focused look inside the org typically tells you which situation you are facing.

Very little, if it is sequenced properly. We build and test in a sandbox, bundle changes into modest releases and schedule them away from renewal season and other peaks. Affected teams get a quick recorded demo or a brief live walkthrough with every release. Users typically notice fewer required fields and faster pages first, which helps win support for the later, larger changes.

It is often the right first step. AI features rely on accurate records, clear field definitions and permissions that reflect real job roles. Optimization delivers exactly those: deduplicated accounts and contacts, retired fields that confuse users and models alike, and consolidated automation that behaves predictably. Once the org is clean, an AI pilot in service or sales has a much better chance of producing useful results.