System groundmap definition

What do you get?

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• As-is system landscape: Clear inventory of tools, data sources, integrations, access and roles, manual touchpoints, and pain points across priority workflows.

• To-be architecture: Target system design with recommended integrations, automation candidates, AI agent roles and guardrails, human-in-the-loop checkpoints, and a lightweight governance model for safe, responsible autonomy.

• Execution plan: A prioritized backlog with quick wins, a 90-day action plan, success metrics, and risk/assumption log—ready for implementation.

• Business case: Cost-saving levers (licensing consolidation, cycle-time compression, error reduction), indicative effort, and KPIs for ongoing performance monitoring.

Why does it matter?

Most teams operate in tool sprawl and manual handoffs—hidden costs accumulate in delays, rework, and avoidable errors. This definition creates a single source of truth for how your back office should run, then charts a responsible integration and automation path to reduce operating costs by:

• Eliminating redundant systems and shadow processes to shrink total cost of ownership.

• Automating routine tasks to free up capacity and shorten cycle times.

• Lowering error rates with clearer interfaces, data contracts, and human-in-the-loop checkpoints.

• Choosing right-sized, AI-enabled solutions aligned to your constraints and compliance needs.

. You get accountable oversight of autonomous AI and continuous improvement, not just a one-off diagram.

What do you need to bring?

• Your top 2–3 priority workflows (e.g., lead-to-invoice, onboarding, support) and desired outcomes.

• Access to your current stack (apps, integrations, data sources), sample artifacts (SOPs, exports), and known constraints (security, compliance, budget).

• Operational KPIs you care about (cost per transaction, cycle time, error rates) and any service-level expectations.

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• Decision rights (who approves changes) and an implementation window for quick wins.