5 Strategies to Blend RPA with Human Expertise
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A shocking 44% of RPA initiatives fail to deliver promised value, with the primary culprit being poor integration between bots and human employees. Because the most successful automation doesn't replace humans. It is supposed to amplify their efforts.
In this article, we will discuss how RPA solutions help companies create this powerful human-bot partnership.
The “humans OR bots” dilemma isn’t relevant so far. Now, we live in a realm where these two should collaborate to work better and faster.
Bots can prepare information to make human judgment more effective:
● Policy application: Apply straightforward rules before human review;
● Pattern highlighting: Flag anomalies and similarities to past cases;
● Historical context: Show how similar cases were handled previously;
● Data enrichment: Pull relevant information from multiple systems;
● Scenario simulation: Calculate potential outcomes of different decisions.
Augmented decision-making reduces cognitive load while improving quality. A compliance team reported 41% faster reviews with 27% improved accuracy after implementing preprocessing.
Rather than binary yes/no automation, present humans with:
● Ranked alternatives: Multiple options with confidence scores;
● Supporting evidence: Data points supporting each option;
● Counterfactuals: Information that contradicts the top recommendation;
● Similar case outcomes: Results when similar choices were made previously.
Effective frameworks segment decisions into:
Companies that implement decision augmentation consistently see:
● 30-45% faster decision times;
● 25-30% improved decision quality;
● 50-60% higher employee satisfaction;
● 20-35% better compliance adherence.
The combination unlocks value impossible with either bots or humans alone.
Not all processes can be fully automated. The key is intelligently routing exceptions to humans while keeping straightforward tasks with bots.
The most effective automation systems recognize these situations for human intervention:
● Complex pattern recognition: Cases requiring subjective judgment;
● Confidence thresholds: When the bot's certainty falls below predetermined levels;
● High-stakes decisions: Transactions above certain financial thresholds;
● Novel scenarios: Cases without historical precedent in the training data;
● Regulatory requirements: Situations where human review is legally mandated.
Brands that apply clear exception criteria see 40% faster resolution times compared to those with ad-hoc escalation.
Your thresholds should balance efficiency with risk tolerance:
● High-volume, low-risk processes (e.g., data entry verification): 80-85% confidence;
● Medium-risk operations (e.g., customer refund processing): 90-95% confidence;
● Critical functions (e.g., compliance validation): 98%+ confidence.
Smart RPA systems learn from human interventions rather than treating them as isolated events. Each human-handled exception contains valuable intelligence:
● Language processing: Enhance understanding of non-standard requests;
● Classification training: Improve automated categorization;
● Edge case examples: Build a library of unusual scenarios;
● Decision patterns: Recognize repeatable logic for future automation;
● Threshold calibration: Adjust confidence levels based on outcomes.
Companies that systematically capture decision data cut exception volumes by 5-8% per month through continual learning.
To transform human decisions into automation improvements:
A healthcare claims processor identified that 43% of their exceptions stemmed from just three data entry patterns. By adding specific rules for these patterns, they reduced exception volume by 38% in just one month.
Measure your learning system's effectiveness with:
● Exception reduction rate: Month-over-month decline in human interventions;
● Learning efficiency: How many examples needed before automation improves;
● Automation expansion: Percentage of previously manual tasks now automated;
● Time-to-improvement: How quickly patterns translate to system updates.
Automation Anywhere's IQ Bot provides built-in capabilities for this approach:
The moment of transition between bot and human is critical. Poor handoffs destroy productivity gains and create frustration. A well-designed dashboard reduces exception handling time by 30-50% compared to toggling between multiple systems.
Effective exception dashboards provide:
● Decision history: Previous actions and reasoning;
● Visual cues: Color-coding and icons for quick priority assessment;
● Complete context: All relevant information on one screen;
● Clear action options: Structured choices for consistent handling;
● Relevant metrics: SLAs, queue volumes, and personal performance.
Context loss during handoffs causes delays and errors. Prevent this by:
● Creating detailed audit trails: Log each step the bot performed;
● Highlighting decision points: Flag exactly where automation uncertainty occurred;
● Providing data snapshots: Preserve system state at the moment of exception;
● Maintaining document links: Keep connections to all relevant materials;
● Capturing conversation history: Include any customer interactions.
Context-rich handoffs reduce resolution time by an average of 62% compared to traditional "start from scratch" reviews.
The most effective handoff interfaces follow these principles:
● Keyboard-optimized workflows: Enable rapid processing with minimal mouse use.
● Progressive disclosure: Show essential information first, details on demand.
● Embedded guidance: Provide contextual help for complex decisions.
● Consistent terminology: Use the same language as core business systems.
● Personalization options: Let users customize their workflow and interface.
Dynamic allocation ensures optimal resource utilization between bots and humans. Sophisticated systems adjust work distribution based on:
● Real-time volume fluctuations: Shift more to automation during spikes;
● Complexity assessment: Route based on estimated handling difficulty;
● Available human capacity: Adjust thresholds based on staff availability;
● Time sensitivity: Route urgent items based on the fastest path to completion;
● Learning opportunities: Direct novel cases to humans with training needs.
This flexibility beats static allocation models by 15-25% in throughput metrics.
Keep workflows moving with these techniques:
● Predictive volume modeling: Forecast exception volumes 24-48 hours ahead;
● Dynamic threshold adjustment: Temporarily adjust confidence levels during peaks;
● Automated reprioritization: Continuously rebalance queues as conditions change;
● Cross-training support: Provide guidance for handling unfamiliar exceptions;
● Preemptive notification: Alert teams to incoming volume increases.
Businesses using these approaches maintained 94% SLA compliance even during 300% volume spikes.
Staffing hybrid teams require new metrics:
● Exception handling capacity: Work volume per staff member;
● Complexity distribution: Mix of simple vs. complex exceptions;
● Automation leverage ratio: Total work processed vs. human-only capacity;
● Cross-functional flexibility: Ability to handle multiple exception types.
● Month 1: Analyze current processes to identify automation/human boundary points.
● Month 2: Implement basic exception flagging and a simple human review queue.
● Month 3: Design and deploy your first handoff interface.
● Month 4: Start capturing exception-handling decisions as structured data.
● Month 5: Begin pattern analysis to identify automation improvement opportunities.
● Month 6: Implement your first feedback loop to reduce common exceptions.
● Month 8: Add decision augmentation for complex judgment tasks.
● Month 10: Deploy dynamic workload balancing based on volume and complexity.
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