Document Automation with AI: Major Challenges & Opportunities
Go beyond conventional document processing to boost the effectiveness and efficiency of your document workflows with AI and automation.
Download the full guideRecent advancements in OCR and RPA have transformed document processing, but organizations need AI to maintain competitive advantage. AI-powered document processing (IDP) improves efficiency, reduces errors, and increases customer satisfaction while improving profitability.
However, adoption presents challenges requiring strategic solutions. This guide walks through the seven challenges most organizations face and the five opportunities that follow once AI is meaningfully integrated into document workflows.
Breaking through the limits: challenging status quo to prioritize business objectives
In competitive environments, organizations must adopt AI and IDP quickly. The guide addresses seven key challenges with actionable solutions.
- Challenge 01Pressure from Competition
In competitive environments, organizations must adopt AI and IDP quickly to retain a strategic advantage.
Tips on successful adoption- Understand AI/IDP and align C-level stakeholders with implementation
- Identify high-impact use cases (invoice processing, contract management, classification)
- Assess and prepare data through cleaning and categorization
- Train models on diverse datasets
- Monitor performance regularly post-deployment
- Ensure security and compliance with regulations
Vendor evaluation should cover ten key dimensions: expertise, track record, support, customization, security, integration, pricing, training, and roadmap.
- Challenge 02Low Margins and High Operational CostsTips for maximizing ROI
- Identify additional use cases beyond initial implementation
- Continuously improve data quality and accuracy
- Evaluate performance metrics regularly
- Explore integration with RPA, NLP/NLU, and HITL technologies
- Develop change management plans with employee training
- Monitor regulatory and compliance changes
- Leverage analytics insights for decision-making
- Challenge 03Inefficient Document Processing OperationsTips for achieving efficient end-to-end processing
- Standardize document formats and naming conventions
- Automate document routing to appropriate departments
- Implement workflow automation across the data pipeline
- Use NLP/NLU for flexible data extraction
- Continuously monitor and improve accuracy
- Optimize document search capabilities
- Challenge 04Dependency on External ProvidersLong-term strategies
- Build in-house expertise with vendor support
- Invest in internal R&D for custom solutions
- Collaborate with industry partners on datasets and models
- Use multiple vendors to reduce dependency
- Explore open-source alternatives
Knowledge transfer is a must. Your operations and IT personnel need to learn how to make the most out of their new AI tool.
- Challenge 05Poor User Experience Affecting Customer SatisfactionSolutions
- Provide comprehensive employee training
- Simplify interface design for intuitive navigation
- Customize interfaces for different user roles
- Incorporate feedback mechanisms into workflows
- Ensure compatibility with existing systems
- Provide accessible user support resources
- Add AI explainability (XAI) components
- Challenge 06Budget and Resource UncertaintyTips for budget estimation
- Clearly define business objectives and expected ROI
- Identify data requirements and preparation costs
- Assess infrastructure gaps and technology costs
- Estimate skilled resource requirements
- Consider deployment environment needs
- Plan for ongoing maintenance and support costs
- Challenge 07Low Business Agility Affecting CapitalizationTips to improve business agility
- Develop culture of innovation with resource support
- Foster cross-functional collaboration and knowledge sharing
- Streamline decision-making processes
- Invest in modern technology infrastructure
- Develop flexible, adaptive governance structures
- Invest in IDP solutions with seamless integration
Unlocking the power of AI and IDP for enhanced at-scale document processing
- Opportunity 01Addressing Customer Needs Quickly and Cost Effectively
Four drivers of value emerge when AI and IDP are applied to customer-facing document workflows:
- Faster processing time: Process large volumes in seconds or minutes, improving response times.
- Increased accuracy: Reduce errors through automated extraction and processing.
- Cost savings: Reduce manual intervention and operational expenses.
- Compliance: Meet regulations through constantly evolving, retrainable systems.
Approximately 80% of business data exists in unstructured formats like emails, images, business documents, and PDFs.
- Opportunity 02Achieving Business Agility and Scalability
Three primary methods unlock agility and scale:
- Process large volumes quickly and accurately
- Automate manual tasks, freeing staff for higher-level work
- Improve data management and decision-making
AI is not a silver bullet; scaling across the organization requires fundamental infrastructure and cultural changes.
- Opportunity 03Improving the Efficiency of Operations
Operational improvements compound across the document lifecycle:
- Higher automation of manual tasks and processes
- Faster, more accurate processing at scale
- Streamlined workflows from digitization to actionable insights
- Better data quality and governance
Case examples: an engineering firm reduced RFP response time from 3 weeks to 1 week and processed 400% more RFPs; a global life sciences consultancy achieved 70% accuracy in FDA Form 483 classification, decreased manual review time, and optimized costs.
- Opportunity 04Adopting Customer Satisfaction as a KPI
Operationalize customer satisfaction by tracking the metrics that document workflows directly affect:
- Turnaround time: Processing duration and customer wait times.
- Accuracy rate: Documents processed without errors.
- Processing volume: Documents handled in given timeframe.
- Customer complaints: Track feedback on document operations.
- First-time resolution rate: Correctly processed documents on first attempt.
Additional improvements include document accessibility, service personalization, and 24/7 availability.
- Opportunity 05Improving Document Storage and Usage
Move from machine-unreadable to machine- and human-readable data with five core capabilities:
- Document categorization and indexing based on content
- Automated routing to appropriate departments
- Enhanced search capabilities using keywords/phrases
- Compliance identification and flagging
- Analytics on document usage for data-driven decisions
Where to go from here
Successful AI/IDP implementation requires addressing identified challenges proactively by partnering with specialized providers who develop customized, business-objective-focused solutions.
Visit our Intelligent Document Processing page or contact our IDP experts directly to scope an engagement.