AI Integration with Hyland RPA

Hyland RPA excels at executing predefined sequences across applications, but stumbles on unstructured data and exceptions. AI integration injects intelligence at these decision points, typically via a secure API call from within a bot's workflow. Key integration surfaces include:

• Exception Handling Queues: When a bot encounters an unrecognized document format or data mismatch, it can pause, send the context (screenshot, extracted text, application state) to an AI model for analysis, and receive a recommended action or extracted data to proceed.
• Attended Bot Triggers: AI can monitor a user's desktop activity or a shared inbox, identify a process ripe for automation (e.g., a customer email with an attached invoice), and suggest or trigger the launch of a specific Hyland RPA bot, passing along the initial data.
• Data Validation & Enrichment: Before a bot enters data into a system of record, AI can cross-reference extracted information (like a vendor name from an invoice) against master data lists or external databases to validate accuracy or append missing details.

A practical implementation wires an AI service as a reusable component within Hyland RPA's development studio. For example, a bot for loan application processing might:

1. Log into a core banking system and retrieve an application PDF.
2. Use basic OCR to get text, but then call an LLM via a secured endpoint to classify document type (e.g., pay stub vs. tax return) and extract key financial figures.
3. Based on the AI's structured JSON output, the bot decides: if income meets threshold, proceed to auto-populate fields in the LOS; if not, route the case to an exception queue for manual review.
4. The bot logs the AI's input, output, and the final action taken for audit and model retraining. This moves the process from "bot fails on non-standard forms" to "bot handles 80% of variations automatically."

Rollout focuses on augmenting, not replacing, existing automations. Start by instrumenting your most common RPA failure points with AI-based exception handling to increase straight-through processing. Govern this integration by:

• Implementing strict input/output logging for every AI call to monitor accuracy and cost.
• Building a human-in-the-loop review queue in Hyland RPA for low-confidence AI decisions, allowing supervisors to correct outcomes and feed data back for improvement.
• Using Hyland RPA's credential management to ensure AI API keys are secured and access is controlled, keeping sensitive data within compliance boundaries during processing.

The integration connects a secure AI inference layer to the Hyland RPA bot runtime, typically via REST API calls from within bot workflows. The core pattern involves the RPA bot capturing a document (e.g., from a desktop scanner, email, or a Hyland OnBase/Perceptive queue) and sending it to an AI service for analysis. The AI service, built on models like GPT-4 Vision or specialized document understanding LLMs, returns structured data (extracted fields, classification, validation flags) and a confidence score. The bot then uses this structured output to populate forms, make routing decisions, or flag exceptions for human review, all within the same attended session.

A typical high-value workflow is exception handling in front-office processing. For example, in loan origination, a bot can capture a borrower's uploaded bank statement, send it for AI analysis to verify income, and then either proceed with automated data entry into the LOS or, if the AI flags a discrepancy or low confidence, immediately prompt the loan officer with the extracted data and the specific issue for a quick override. This moves the human-in-the-loop from manual data entry to focused validation, cutting process time from hours to minutes. The architecture must account for state management—the bot holds the session open while awaiting the AI response—and fallback logic to handle API timeouts or low-confidence results gracefully.

Rollout focuses on piloting specific, high-volume document types where exceptions are common. Governance is critical: all AI interactions should be logged with the original document, the prompt/query sent, the full AI response, and the bot's subsequent action for auditability and model retraining. Implement human review queues within the RPA console for low-confidence outcomes. This pattern transforms Hyland RPA from a rigid rules executor into an adaptive, intelligent assistant that handles the variability of real-world documents without breaking the automation flow.

Integrating AI with Hyland RPA introduces new decision points where bots interact with unstructured data and user prompts. Governance starts with the automation object model: defining which RPA tasks, queues, and attended sessions are eligible for AI assistance. We map AI calls to specific process templates and exception workflows within Hyland RPA Studio, ensuring each AI action is logged against a bot session ID, user ID, and source document. All AI prompts, context data (like extracted invoice amounts or customer names), and model responses are written to a secure audit log linked to the RPA job history, creating a complete chain of custody for compliance reviews.

Security is enforced through a gateway architecture. AI models (like GPT-4 or Claude) are never called directly from the RPA bot. Instead, bots send requests to a secure middleware layer—often deployed as an Azure API Management or Kong Gateway instance—that handles authentication, prompt sanitization, PII redaction, and rate limiting. This layer validates the bot's identity via service principals, checks that the requested document data is permitted for AI processing based on its classification (e.g., no PHI), and routes the call to the appropriate, approved model. Outputs are validated against business rules (e.g., extracted dates must be in the future) before the bot proceeds, preventing hallucinated data from entering core systems.

A phased rollout mitigates risk and builds trust. Phase 1 targets a single, high-volume attended process—like invoice exception handling—where a bot presents a confidence score and AI-suggested action (e.g., 'Route to AP Clerk Jones') to the human operator for approval. This 'human-in-the-loop' mode collects performance data and user feedback. Phase 2 enables auto-resolution for high-confidence cases (e.g., PO-matched line items), logging the decision for weekly review by a supervisor. Phase 3 expands to multiple processes and introduces continuous monitoring dashboards that track AI suggestion accuracy, bot efficiency gains, and any drift in model performance, allowing for prompt retraining or rule adjustment. This controlled approach ensures the integration delivers operational lift without introducing unmanaged risk into critical front-office workflows.