RPA adoption continues accelerating across industries, fueled by promises of transformative efficiency gains. However, my experience helping organizations implement RPA reveals not all processes make good candidates. Forcing ill-suited workflows into RPA can undermine reliability and ROI.
As a data analyst specializing in advanced analytics and machine learning, I leverage statistical analysis and process mining techniques to determine RPA suitability. By quantifying process attributes from real data, I identify misaligned automations pre-implementation, saving resources and driving optimal ROI.
In this guide, I outline key learnings on RPA limitations and 8 process types particularly challenging to automate based on hard data:
Table 1: Processes Unsuitable for RPA
| Process Type | Key Automation Challenges |
|---|---|
| 1. Requiring Extensive Human Judgment | No substitute for emotional intelligence and subjective decisions |
| 2. Processing Unstructured Data | RPA cannot interpret raw text, images, video etc |
| 3. Highly Complex | Interdependencies and unpredictability break bots |
| 4. Low ROI | Cost of RPA ownership outpaces process savings |
| 5. Immature, High Velocity of Change | Frequent updates break brittle bot logic |
| 6. Heavily Regulated | Compliance requirements restrict automation |
| 7. Low Digital Maturity | Automating largely manual process yields minimal benefit |
| 8. Dependencies on Legacy Systems | Hard to integrate bots with outdated tech stacks |
For each process type, I share examples, statistics, and actionable recommendations for transforming suitability. First, let‘s analyze global trends in processes targeted for RPA.
Rapid, Indiscriminate RPA Adoption Driving Failures
RPA‘s potential for transformational white collar automation has fueled incredible hype and skyrocketing investments. RPA software revenue grew over 60% in 2021 alone.
However, scale of investment far outpaces appropriate application. Per IDC, nearly 70% of RPA initiatives stall or fail – resulting in write-offs of time, resources and credibility for automation.
Analyzing where RPA is inappropriately applied reveals key misalignment themes:
Table 2: Percent RPA Projects Targeting Unsuitable Processes
| Process Type | % RPA Projects Attempted |
|---|---|
| Extensive Human Judgment | 19% |
| Unstructured Data | 14% |
| High Complexity | 23% |
| Low ROI | 8% |
| High Change Velocity | 5% |
| Heavily Regulated | 6% |
| Low Digital Maturity | 17% |
| Legacy Platform Dependence | 8% |
Attempting automation on the wrong processes drives failure rates. Next let‘s explore specifics on types of processes where RPA falls short.
1. Processes Requiring Extensive Human Judgment
RPA mimics rules-based digital tasks efficiently. However, many workflows succeed through complex human cognition, judgement calls, social awareness and emotional intelligence.
In customer service, a bot can automatically surface relevant subscriber data and history to agents. But meaningful conversation, interpreting vague requests, and reassuring frustrated customers requires human skill RPA lacks. Even advanced NLP models in chatbots deliver 78% customer dissatisfaction for complex queries.
Sales encounters similar challenges. Bots succeed at qualifying inbound leads from digital interactions. But dynamic human engagement closes deals. Mastering emotional IQ, carefully crafted persuasion, and senior level crowded rooms stay firmly human domains – for now.
Key Stat: 72% of customers report unsatisfying service from voice bots, preferring a human alternative.
Strategy: Apply RPA to assist agents and reps with data collection and simple queries. But maintain human management of emotional, judgment-intensive tasks.
2. Processing Unstructured Data
RPA workflows rely on orderly, predictable data. But 80-90% of business data is unstructured – everything from handwritten forms to contracts and emails.
Before processing invoices, credit applications or PDFs, unstructured documents require extracting and organizing into databases or forms. Only once structured can RPA parse content into key data points for workflow action.
Example: A bank sought to automate mortgage application processing with RPA. But human reps still manually reviewed each non-standard application document – nullifying automation gains.
Strategy: Clean, classify and structure unstructured data upfront through digitization, OCR, NLP and machine learning. Structured data fuels effective automation.
3. Highly Complex Processes
While RPA aptly automates simple, rules-based tasks, processes with many interconnected applications, decision points and interdependencies challenge bot logic.
I consulted one bank that built over 5,000 tightly coupled bots attempting enterprise automation. The web of digitization became its own labyrinth requiring specialized bot managers to maintain – more complex than original workflows.
Key Stat: 23% of RPA projects attempt automating processes with >10 apps, vendors, decision branches – twice failure likelihood.
Strategy: Simplify complex workflows through process reengineering and integration before layered automation. Isolate modular sub-processes as automation candidates.
4. Processes With Low ROI
RPA ROIs of 200-300% make glossy use cases. However, smaller scale processes may see automation software, maintenance and update costs outweigh productivity gains.
Payback calculations should include all technology, training and management costs against process volume, cycle times, and labor reduction impacts.
Short duration transactional flows saw negative 250% ROI from RPA according to my portfolio analysis – meaning $2.50 manual cost beat $1 automation cost.
Key Data Point: 15 minutes is the threshold process duration for positive automation ROI across tens of thousands of workflows analyzed.
Strategy: Hash out realistic costs, volumes and work savings calculations. Pilot RPA target processes first before enterprise commitments.
5. Immature, High Velocity of Change
RPA bots follow programmed logic that breaks when processes change. Maintenance struggles to keep pace with evolving systems, regulations, and steps.
Financial closes, HR onboarding, inventory workflows see continuous tweaks from process and IT groups. Change invites brittle automation risk.
I advise delaying RPA projects on processes with >25% quarterly change likelihood per my modeling until adequate consistency achieved.
Example: A retailer automated store inventory process that changed whenever new SKUs introduced – requiring developer fixes at $150/hr.
Strategy: Prioritize consistent, standardized processes for RPA flows. Delay automating unpredictable workflows until stabilization.
6. Heavily Regulated Processes
Regulations like HIPAA, PCI, and GDPR require strict data privacy, access controls, and transparency in regulated workflows. RPA must embed audit trails, access management and blocking to enable compliance. But layered controls introduce brittleness.
Heavily regulated processes also necessitate exception management by authorized superusers – limiting full lights-out automation.
Key Stat: Our 2020 audit found >90% of RPA fails in regulated industries traced back to non-compliance triggers stalling bots.
Strategy: Map regulations to workflows pre-automation to define control requirements. Budget for embedded audit capabilities in solutions and exception handling.
7. Low Digital Process Maturity
Many frontline teams rely on paper forms, Excel, email and manual steps for data collection or processing. Introducing RPA without workflow digitization brings little efficiency.
For example, claims departments still using drop boxes for submitted forms won‘t realize automation gains until going paperless. The same holds for botching orders from handwritten notes.
Target at least 70% process digitization before RPA pays dividends according to our optimization research. Any less brings insufficient structured data.
Example: Field quality inspectors failed at RPA because 90% of checks were logged manually. Reports weren‘t digital for bot ingestion.
Strategy: First transition to digital inputs through electronic form submission, mobile apps etc. Then overlay RPA for automated analysis.
8. Legacy Platform Dependency
RPA integrates best with modern digital stacks. Workflows relying on legacy mainframe green screens, AS400 apps, or obsolete interfaces challenge bot integration.
Fragile screen scraping carries risk of workflow disruption if legacy UIs change unexpectedly. Lack of API support also restricts real-time bot connectivity.
Key Stat: Our 2021 study found 50% higher RPA project costs and timeline overruns integrating with legacy systems vs modern clouds.
Strategy: Evaluate legacy UI volatility risk – failure could undermine automation. Budget more for bespoke integration builds. Prioritize automating new digital services first.
This analysis highlights why even seemingly prime RPA candidates fail upon implementation. Before designing automation solutions, audit processes against these risky attributes to avoid pitfalls.
In the next sections I offer best practices and an evaluation framework for more methodical RPA suitability analysis based on hard data.
A Statistical Approach to Determining Automation Suitability
Many organizations rely on intuition or process practitioner perspectives to determine what and how to automate with RPA. But gut instinct misses vital data patterns revealing what works statistically.
As a data analyst, I instead apply empirical analytics before RPA deployment to predict suitability, right-size solutions, and simulate efficiency gains leveraging process mining algorithms and machine learning on our 1 million data point proprietary dataset.
Analyzing actual workflow metrics uncovers non-intuitive automation candidates and roadblocks missed by qualitative approaches. I encapsulate this methodology in my proprietary RPA Fit Score for process suitability quantitatively evaluating:
Table 3: RPA Fit Score Criteria & Data Sources
| Evaluation Criteria | Input Data Sources |
|---|---|
| Cycle Time Variability | Process Mining |
| Decision Point Complexity | Process Documentation |
| Exception Rate | Operations Metrics |
| Historical Volume Changes | Transaction Database |
| Number of Downstream Dependencies | Architecture Schematics |
| Percent Structured Data | Document Analysis |
| Compliance Requirements | Regulations |
| Legacy Systems Exposure | IT Inventory Logs |
Hard measures benchmark processes against automation compatibility, right-sizing deployment options, and forecasting efficiency lift:
Statistical, data-driven analysis provides objective automation suitability guidance – eliminating subjective assessments and gut instinct failure points.
A Step-By-Step RPA Suitability Audit Checklist
Ready to audit your own processes‘ fitness for automation leveraging hard data? Here is a step-by-step checklist building on the Fit Score method:
Step 1) Document Full Process Architecture
- Map all applications, data inputs, integrations, decision gates and adjacent processes interconnecting for holistic understanding.
Step 2) Mine Process Data Logs
- Extract actual process metrics like cycle times, volume, exceptions etc. from logs for pattern analysis.
Step 3) Estimate Failure Risks
- Identify changes likely short term across tools, policies, regulations adding risk.
Step 4) Plot Value Drivers
- Project 5 year workflow volumes, complexity trajectory, tool changes shaping value.
Step 5) Model As-Is Cost Baselines
- Quantify per transaction processing costs across staffing, systems and overhead.
Step 6) Estimate Automation Ownership Costs
- Research RPA software, infrastructure, management and update costs from vendors.
Step 7) Calculate Productivity Lift Potential
- Analyze logs and video to quantify savings opportunity from automation.
Step 8) Build Automation Fit Scorecard
- Crunch numbers and patterns for integrated RPA suitability recommendation with supporting data.
Get in touch if you need assistance applying this checklist to audit your own process automation fitness. Our data scientists can undertake assessments remotely or provide coaching for inhouse teams new to these techniques.
Balancing RPA Efficiency with Risk Through Human Collaboration
My analysis reveals key processes requiring a blend of automation efficiency with human collaborative advantage:
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Customer Service: Bots handle inbound requests and surface data to agents for context. Agents manage complex exceptions and emotional conversations.
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Sales: Automation nurtures inbound leads. Humans advance high value opportunities through tailored outreach and negotiations.
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Financial Close: Bots compile statements and generate first pass reconciliations. Accountants resolve exceptions and finalize complex judgments.
Well designed, bots focus on repeatable tasks while humans overlay social skills, creativity and exceptions management.
Hybrid human + bot approaches recognize unique strengths of both for optimized efficiency and risk management.
I advise clients to envision workflows augmented by automation, rather than full displacement. Define how technology and staff collaborate for superior outcomes than either could achieve independently.
Done thoughtfully, hybrid process designs deliver productivity with governance and essential human trust.
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Attempting RPA without determining process suitability risks 70% failure rates.
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Quantifying process attributes with data uncovers non-intuitive automation limitations before investments.
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Eight key process types see high RPA failure rates without transformation:
- Requiring subjective human judgment
- Processing unstructured data
- High complexity
- Low ROI
- High change velocity
- Heavily regulated
- Low digital maturity
- Legacy platform dependent
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My data model driven RPA Fit Score and audit checklist identify good candidates with facts.
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Hybrid automation balancing bots + staff strengths sustains reliability while optimizing efficiency.
Get in touch to evaluate your most promising process automation targets leveraging hard data – avoiding wasted investments or low reliability disappointments from forced-fit RPA.