The problems you already know about
A useful automation plan starts with the shape of the work, not a tool. These four workflow patterns cover many common back-office candidates and the controls each one needs.
Document intake and structured extraction
Invoices, forms, claims, applications, and supplier documents arrive in inconsistent layouts. Teams open each file, find the relevant fields, and rekey the result into another system.
Use document AI to classify the file and propose structured fields. Validate required fields and business rules deterministically, then route low-confidence or material cases to a reviewer before writing to the system of record.
Reconciliation and exception review
Finance and operations teams compare records across bank feeds, invoices, orders, and ledgers. Most items are routine; a smaller set needs investigation and judgement.
Match the deterministic cases with rules, use AI to classify ambiguous descriptions or supporting evidence, and send unresolved differences to an exception queue with the source records attached.
Inbox, request, and case triage
Shared inboxes and request queues mix routine work with urgent or specialist cases. People spend time reading, tagging, and forwarding work before anyone starts resolving it.
Classify intent and urgency, extract the details needed for routing, and draft the next action. Keep escalation rules deterministic and let a person approve responses where risk, tone, or material value requires it.
Recurring reporting and data consolidation
Weekly and monthly reports often require exports from several systems, manual normalization, commentary, and repeated checks before distribution.
Automate extraction and normalization first, calculate figures with deterministic code, and use AI only for classification or draft commentary. Preserve source links and require approval before a report is published.
Plan the workflow before choosing the tool
Planning examples, not client averages or guaranteed results. A first workflow is easier to evaluate when its baseline, decision gates, and ownership are explicit.
How to use this workflow library
Map one workflow end to end
Record its trigger, inputs, systems, handoffs, decisions, exceptions, and finished output. Measure the current volume and handling time so there is a baseline.
Separate rules, AI, and human judgement
Use deterministic logic for calculations and fixed policy. Use AI for language-heavy classification or extraction. Put review gates wherever a mistake is material or hard to reverse.
Pilot, measure, then expand
Start with a bounded slice of real work. Compare quality, exceptions, and cycle time with the baseline, document failure modes, and expand only when the process owner accepts the evidence.
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Common questions
How is this different from RPA?
Robotic Process Automation is well suited to stable, structured, deterministic steps. AI is useful when a step involves unstructured language or variable documents. A robust workflow often combines both: AI proposes a classification or extraction, deterministic rules validate it, and a person reviews exceptions. Deciding that split for a specific workflow is part of our AI workflow automation consulting.
Which workflow should we automate first?
Start with work that is frequent, repetitive, measurable, and safe to reverse. Avoid beginning with the most complex or highest-risk process. A narrower workflow with known exceptions gives you a cleaner way to test quality and operational fit. Sector examples include permit and change-order handling in AI for construction companies, reconciliation and invoice coding in AI for accountants and accounting firms, and candidate document handling in AI for recruitment and staffing agencies.
Where should a human stay in the loop?
Keep review where a decision is material, regulated, hard to reverse, or dependent on context the system cannot observe. Also route low-confidence and out-of-distribution cases to people. Thresholds should be tested against real examples rather than chosen by intuition. Claims decisions in AI for insurance brokers and client-facing proposals in AI automation for professional services firms are examples where the gate stays with a person.
What should we measure?
Measure the current volume, handling time, error rate, exception rate, and cost per transaction before the pilot. During the pilot, track the same measures plus model confidence, reviewer overrides, failure categories, and any downstream correction work.
When do we need specialist implementation help?
Specialist help is useful when the workflow crosses several systems, handles sensitive data, needs a formal audit trail, or has enough edge cases that an off-the-shelf tool cannot be evaluated safely. A scoped planning phase, such as the AI discovery sprint, should still come before a larger build. Implementation support is described under back office automation.
Need help implementing one of these workflows?
Explore our back-office automation service or book a short feasibility call to pressure-test the workflow, review gates, and integration scope.