What is the difference between workflow automation and AI automation?

Workflow automation handles rule-based processes that follow predictable patterns. If a sales order comes in, route it to fulfillment, send a confirmation email, and update inventory. AI automation handles tasks that require judgment or interpretation, like reading unstructured documents, classifying customer inquiries, or predicting maintenance needs. Many projects combine both: workflow automation orchestrates the overall process while AI components handle the intelligent decision points.

Which processes should we automate first?

Start with high-volume, rule-based processes where errors are common or costly. Good candidates include data entry between systems, document routing and approvals, report generation, customer onboarding, and invoice processing. We recommend choosing processes that are well-understood and relatively stable. Automating processes that change frequently requires more maintenance. The first automation project should deliver clear value while building internal confidence in the approach.

How do you connect systems that do not have APIs?

Legacy systems without APIs require creative solutions. Options include database integration where we read and write directly to the application database, screen scraping using RPA tools to interact with the user interface, file-based integration watching for exports and generating imports, and building custom connectors using available protocols. Each approach has trade-offs in reliability, maintainability, and vendor support. We evaluate options based on your specific systems and long-term plans.

What happens when automated workflows encounter exceptions?

Well-designed automation includes exception handling from the start. When the system encounters something unexpected, it should log the issue, pause that particular item, alert appropriate personnel, and continue processing other items. We build admin dashboards where staff can review exceptions, make corrections, and resume processing. The goal is handling happy paths automatically while surfacing edge cases for human review rather than failing silently.

How do you measure the success of workflow automation projects?

We track metrics that matter to your business: processing time reduction, error rate decrease, volume handled without additional staff, and time freed up for higher-value work. Before starting, we establish baselines for current process performance. After deployment, we monitor actual results against projections. Most automation projects pay for themselves within 6 to 12 months through direct cost savings or capacity gains. We provide detailed ROI reports so you can quantify the impact.