Artificial Intelligence
AI Workflow Automation
Business processes that combine deterministic automation with AI judgment steps, so unstructured inputs, exceptions and routing decisions no longer stall the work.
Overview
Traditional automation handles predictable, structured work well and stops at the first free-text email, scanned form or ambiguous request. AI workflow automation inserts model-driven steps, such as classification, extraction, summarization and routing, into otherwise deterministic processes, extending automation to work that previously required a person to read and interpret.
We keep the process itself explicit and auditable. Models handle the judgment steps, orchestration engines handle sequencing, retries and approvals, and people remain in the loop wherever confidence is low or the stakes are high.
Business applications
- 01
Intake and routing
Classify inbound emails, forms and requests by intent and priority, extract the relevant details, and route each item to the correct queue or system, with a confidence score that triggers human review when it is low.
- 02
Order-to-cash and procure-to-pay
Match invoices, purchase orders and receipts, flag discrepancies with an explanation, and prepare exceptions for finance staff, connecting AI steps to ERP platforms such as SAP, Oracle or Microsoft Dynamics.
- 03
Marketing and sales operations
Enrich and score leads, personalize outreach drafts, update CRM records from call notes and trigger lifecycle campaigns, with marketing and sales teams approving messaging before it reaches customers.
- 04
Compliance and quality checks
Review submissions, contracts or operational records against defined policies, highlight potential issues with supporting evidence, and assemble review packages so specialists spend their time on judgment rather than searching.
Engineering approach
- 01
Map the process as it really runs
Document the actual steps, systems, handoffs, exception paths and volumes, including the informal workarounds that rarely appear in official process diagrams but drive much of the manual effort.
- 02
Separate rules from judgment
Implement deterministic logic as code or workflow definitions, and reserve model calls for the steps that genuinely require interpreting language, documents or ambiguous situations.
- 03
Orchestrate durably
Build the workflow on an engine that handles retries, timeouts, compensation and long waits for approval, with each AI step returning structured output and a confidence signal.
- 04
Design the human checkpoints
Define thresholds and review queues so that low-confidence or high-impact items reach the right person with the context needed to decide quickly and record their reasoning.
- 05
Measure and refine
Track throughput, exception rates, reviewer overrides and model quality over time, and use reviewer corrections to improve prompts, thresholds and the evaluation set.
Technologies and platforms
Workflow orchestration
- Temporal
- Apache Airflow
- AWS Step Functions
- Azure Logic Apps
- Camunda
- n8n
AI services
- OpenAI
- Anthropic Claude
- Google Gemini
- Azure AI Document Intelligence
- Amazon Textract
Integration
- Apache Kafka
- MuleSoft
- REST and webhook APIs
- Salesforce
- SAP
- Microsoft Dynamics 365
Monitoring
- OpenTelemetry
- Prometheus
- Grafana
- Langfuse
Technology names are trademarks of their respective owners and do not imply partnership or endorsement.
What you receive
- Current-state and target process maps with automation candidates
- Production workflows integrating AI steps with your systems of record
- Human review queues with confidence thresholds and audit trails
- Evaluation set and quality measures for each AI decision point
- Operational dashboards for throughput, exceptions and overrides
- Runbook and handover documentation for your operations team
Questions leaders ask
How is this different from RPA?
Robotic process automation replicates clicks and keystrokes on structured screens. AI workflow automation adds the ability to interpret unstructured content and make bounded judgment calls. The two can coexist: we integrate through APIs where they exist, keep stable RPA bots where they work, and add AI steps where interpretation was the bottleneck.
What happens when the AI is unsure?
Each AI step returns a confidence signal alongside its output. Items below a defined threshold, or above a defined impact level, go to a human review queue with the source material and the model's reasoning attached. Reviewer decisions are recorded and used to refine the system over time.
Can you work with our existing automation platform?
Usually, yes. We integrate with established orchestration and integration platforms rather than replacing them by default. If a platform cannot support durable, long-running processes with approval steps, or cannot call AI services securely, we explain the limitation and recommend options for addressing it.
Related solutions
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