The enterprise automation industry is approaching a paradigm shift. Industry experts predict that within five years, standalone solutions in the IDP (intelligent document processing) and RPA (robotic process automation) categories will cease to exist as independent products, turning into baseline features within agentic systems.
Why Document Recognition Is Ceasing to Be a Standalone Product
For a long time, extracting text and tables from scans into JSON or XML format was a core value that businesses were willing to pay for. Today, basic OCR has become a standard feature. The focus has shifted to end-to-end intelligent workflows:
- automated reconciliation of amounts and line items against associated contracts and registers;
- counterparty due diligence assessment using open sources and databases;
- drafting clarification request letters when discrepancies are detected;
- dynamic management of approval workflows based on context.
Technology Convergence and Challenges for LLMs
Although large language models (LLMs) and vision-language models (VLMs) demonstrate outstanding results in document understanding, classic OCR still holds its ground due to lower hardware requirements and the absence of "hallucinations." In complex documents (for instance, universal adjustment documents), pure neural networks may misalign line-item relationships, whereas classic solutions reliably flag low-confidence characters.
Additionally, the corporate segment in Russia is strictly constrained by regulatory requirements and security standards (Federal Law No. 152-FZ), necessitating on-premises deployment and precluding the use of public cloud models.
Agentic Automation as the Future of Enterprise Software
The gradual convergence of IDP and RPA is merely an intermediate stage. The end state will be a transition to comprehensive agentic orchestrators. Such platforms combine three levels of logic:
- Deterministic rules for standard, strictly regulated operations.
- AI models for semantic extraction and processing unstructured content.
- Autonomous agents capable of making independent decisions and dynamically adapting process branching.
What Is Holding Back Large-Scale Adoption
Experts highlight five key obstacles to the widespread adoption of agentic platforms:
- High infrastructure costs: On-premises GPU-based computing capacity remains too expensive for many companies.
- Regulatory barriers: Standards and regulations governing the use of generative AI in business processes are still evolving.
- Inaccuracy risks: The essential need for human-in-the-loop verification for critical data.
- Integration challenges: The persistence of legacy enterprise systems lacking modern APIs.
- ROI assessment: A lack of transparent ROI calculation methodologies prior to launching pilot projects.
The transformation of automation tools reflects the maturation of the market: winners will be those developers and enterprises that transition from point-solution bots and OCR tools to end-to-end intelligent scenarios.