AI Knowledge Center

Start with a process that is frequent, rule-based and document-heavy. Measure how long it takes today, who touches it and where errors occur. Automating a single well-understood step gives a clear baseline and builds confidence before you tackle wider workflows.

Keep a person responsible for exceptions: automation works best when unusual cases have a defined path.

Assistive AI proposes and a person approves. Full automation acts without review. For financial and operational data, assistive designs are usually the safer first step, with automation extended only for categories that prove reliable over time.

What decision or task is being improved? What data is available and how clean is it? Who reviews the output? How will it connect to existing systems? How will you measure success? Clear answers prevent pilots from stalling.

Understand what data is sent to an AI service, where it is processed and how long it is retained. Limit inputs to what the task requires and review provider terms and your own obligations. Specific requirements depend on your jurisdiction and industry.

OCR converts the pixels of a scanned page into text. AI then interprets that text in context, recognising tables, labels and relationships so values land in the right fields. Document quality, layout variety and language all influence accuracy.

Even strong extraction produces occasional errors. Confidence scores let you auto-accept clear results and send uncertain ones to a reviewer, which keeps speed high without losing control.

Use defined APIs or import interfaces rather than writing directly to tables. Validate against master data, log every automated action and require approval for postings that carry financial impact.

Descriptions on statements rarely match internal names. Matching combines text similarity, codes and past mappings to suggest the right product, with confidence indicators so reviewers focus on the doubtful cases.

Reporting quality follows data quality. Once documents are captured as structured, validated records, dashboards and variance views become simple to build and trust.

Off-the-shelf tools suit standard processes. Custom development pays off when your workflow, data or integrations are distinctive and the process is central to how you operate.