
InSight DXP
Unlock new levels of enterprise productivity with agentic AI
Transform manual processes into seamless AI-powered workflows
Agentic AI: Your digital workforce for business acceleration
Businesses often struggle with scaling operations, disconnected information, and maintaining compliance due to manual, repetitive tasks. Tackle your most pressing challenges in document-intensive workflows with Iron Mountain InSight® DXP. Its AI agents automate tasks, streamline entire document workflows, and accelerate insight discovery with a powerful search that understands context.
By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.

Streamline operations with intelligent automation:
Go beyond simple task automation. Our agentic AI framework delivers workflow automation by orchestrating a powerful blend of AI agents, human expertise, API-based automation, and your core business applications. By autonomously managing intricate workflows, these AI agents enhance accuracy, accelerate processes, and enable employees to dedicate their expertise to high-value, strategic work.
Frequently asked questions
What is an AI agent?
AI agents are transformative software systems built to interact with their environment, analyse data, and make decisions to achieve specific goals. Businesses are being revolutionised with AI agents that streamline document management, reduce errors, and improve efficiency. Leveraging machine learning (ML) and large language models (LLMs), these AI agents automate tasks, classify documents, extract information, and optimise routing. By taking on repetitive tasks and collaborating with human teams, AI agents free up employees to concentrate on strategic work, fostering a more intelligent and productive workforce.
Why is Iron Mountain introducing AI agents?
Iron Mountain is integrating AI agents into the InSight DXP platform to meet the growing customer demand for natural language interaction with their data. These agents go beyond simple search functionality, offering the capability to handle complex, end-to-end automation processes. By autonomously completing and assisting with tasks, our AI agents significantly improve speed while reducing errors, allowing them to move beyond simple rules-based automation to:
- Surface potential issues in data quality: AI agents can identify inconsistencies, anomalies, and exceptions in content (e.g., mismatched fields in invoices, missing information in HR onboarding files).
- Enable human-in-the-loop (HITL) remediation: When potential discrepancies are detected, the AI agent routes them to a human operator for validation and correction. Over time, the AI agent learns from these interventions to improve accuracy.
- Support end-to-end workflows: AI agents assist with processes like invoice reconciliation or contract review, executing steps that would otherwise require manual effort.
- Strengthen trust: By combining AI with embedded compliance, security, and HITL learning, the system ensures customers can trust outcomes in the same way they trust Iron Mountain with physical records.
What makes agentic AI different from traditional, rules-based workflow automation?
Traditional rules-based automation relies on rigid "if/then" statements and strict templates; if a document format changes or a data type is irregular, the workflow breaks. Agentic AI is context-aware and goal-oriented. Instead of following a fixed script, our AI agents use large language models (LLMs) and advanced machine learning to analyze the layout, language, and context of an unstructured document. They can identify anomalies, handle complex multi-page tables or handwriting, and collaborate sequentially as a team to address variations in a workflow that often interrupt traditional software.
What is AI search?
AI search solves common data access challenges like fragmented content, low relevance from old search engines, and slow indexing. Built into InSight DXP and offering powerful features, AI search includes a smart, conceptual search, generative AI summaries, personalised result ranking, and comprehensive analytics for operational efficiency.
How does workflow automation work?
- Discover and analyse: We partner with you to analyse your existing workflows, mapping out each step to identify inefficiencies, compliance gaps, and automation opportunities that will deliver the greatest impact.
- Design and configure: Our experts design and configure intelligent workflows tailored to your specific needs. We connect your applications and set up business rules to govern how information is processed.
- Automate data handling: The solution automates the capture, extraction, and validation of data from any source, such as emails, forms, and documents, significantly reducing manual data entry and associated errors.
- Orchestrate and integrate: Workflow automation orchestrates the flow of data, triggers actions and updates records automatically in your integrated core business systems (ERP, CRM, etc).
- Monitor and optimise: You gain full visibility into your automated processes through real-time dashboards. We help you monitor performance and use the insights to continuously refine and improve your workflows over time.
How is InSight DXP’s agentic AI different from traditional agentic AI?
| Feature / Capability | Traditional AI | InSight DXP Agentic AI |
|---|---|---|
| Primary Objective | Data Extraction: Focuses on extracting key-value pairs and structured fields from documents based on rigid templates. | Holistic Automation: Focuses on end-to-end document automation. Agents understand context, reason over data, and act by triggering workflows, while operating under established governance, compliance, and human-in-the-loop guardrails. |
| Workflow Execution & Orchestration | Static & Sequential: Workflows follow pre-defined, linear rules. If an exception occurs, the process halts and waits for human intervention. | Dynamic & Orchestrated: Uses a multi-agent runtime with self-reflection and planning loops. Agents can generate explicit execution blueprints that users can review, edit, or approve before execution. |
| Decision Making & Reasoning | Rules-Based & Isolated: Relies on hard-coded logic and deterministic rules. Cannot easily analyze context across multiple documents. | Multi-Document Reasoning: Agents evaluate conflicting data, analyze context across a cluster of documents, and execute complex, multi-step logic. |
| Learning & Adaptability | Manual Retraining Required: Data scientists or IT teams must manually collect new sample documents, label data, and retrain models when document layouts change. | Continuous & Self-Healing: A background HITL Feedback Agent continuously observes human corrections in real-time, detects error patterns, and proactively suggests new rules or prompt updates without engineering involvement. |
| Governance & Oversight | Reactive QA: Relies on manual quality control after data is extracted. Security is generally limited to basic role-based access. | Bounded Autonomy & Tracing: Agents operate with infrastructure-enforced guardrails (Plan -> Review -> Execute -> Observe -> Audit). Every agent's "thought process", tool usage, and execution cost is recorded in a tamper-proof audit history with rollback mechanisms. |


