How AI Turns Dark Data into Actionable Automation

Research shows global data volume doubles every two years; enterprises handle massive structured, semi‑structured and unstructured data. Understanding data types helps assess value and risk.

Structured data has fixed format and length, e.g., database tables.

Semi‑structured data includes XML/HTML that can be treated as structured or unstructured.

Unstructured data is variable‑length, no fixed format, such as web pages, emails, documents, audio, video, images; it now dominates.

For convenience, semi‑structured and unstructured data are grouped as “dark data”, a term coined by Automation Anywhere (AA). AA reports dark data accounts for about 80% of enterprise data, forming the hidden part of an iceberg that traditional automation cannot reach.

Traditional approaches to dark data

Companies often try to convert unstructured data into structured form or leave it in data lakes, wasting storage and yielding poor results.

AI‑driven methods

AI can process images, emails and other dark data, and adapt to unexpected workflows, unlike RPA which handles only structured, rule‑based tasks.

RPA: mimics user actions, handles structured/semi‑structured data, rule‑based, high determinism.

AI: mimics human cognition (vision, language, pattern recognition), handles all data types, learns and adapts, probabilistic determinism, narrow AI solutions.

Key AI technologies used

Speech recognition – processes dialogs, recordings, audio files.

Natural Language Processing (NLP) – handles text, emails, documents.

Computer vision – extracts information from images and embedded PDFs.

Machine learning & deep learning – learns from data to handle anomalies and enable flexible process flows.

AA’s IQ‑Bot solution

IQ‑Bot™ is an AI‑powered solution that lets business users quickly read and process complex documents and emails. It integrates with IBM Watson, Google Cloud AI, Microsoft Cognitive Services, bridging RPA and cognitive platforms.

Examples show IQ‑Bot acting as a bridge between RPA and AI, enabling end‑to‑end automation such as automatically generating confirmation letters or emails after AI extracts and understands content.

Embedding AI into front‑office RPA processes

1. Use NLP to understand semantics and intent of text, converting it to data for downstream automation.

2. Apply computer vision to extract and interpret images or scanned documents.

3. Leverage machine learning to provide flexible, learning‑based handling of exceptions without fixed workflows.

Broader implications

Combining AI with RPA can unlock hidden “dark data”, improve efficiency, and enable new use cases such as constructing cross‑vendor network topologies by stitching together publicly available data, similar to how Google Maps builds its maps.

IQ‑Bot aims to open a window for organizations to discover and automate hidden data, reducing manual effort and errors.