5 Processes Unsuitable for RPA Automation in 2024
Hey there! With robotic process automation (RPA) gaining immense traction, you might be wondering if it can solve all your business process problems. The answer is…not really!
While RPA delivers huge efficiency improvements through automating repetitive tasks, it‘s not a magic wand that can automate just any process.
Implementing RPA without carefully evaluating process suitability will lead to lackluster results at best and process degradation at worst.
Through my experience as an automation consultant, I‘ve found these 5 processes particularly unfit for RPA bots as-is:
- Processes requiring human judgment
- Processes dealing with unstructured data
- Highly complex processes
- Processes with minimal ROI
- Immature processes
Let‘s look at each one in detail, along with tips to transform these processes into automation-ready workflows.
Processes Requiring Human Judgment
RPA tools excel at following predefined rules but come up short when real human discretion and empathy is needed.
An IDC survey found only 25% of organizations even attempt automating processes requiring emotional intelligence or subjective decision making. Why? Because low customer and employee satisfaction usually follow.
For example, chatbots and other customer service automation done poorly leads to 67% of consumers feeling frustrated when their issue is not resolved.
According to Shep Hyken, customer service expert:
"While chatbots can handle common FAQs, complex customer issues require a human agent‘s nuance, emotional intelligence and personalization. Otherwise, customers will become more irritated than satisfied."
Another case is sales processes relying on rapport building, negotiation and situational fluency. As per McKinsey, automating these human interactions often backfires:
"RPA mimics human behavior rather than optimizing it. This results in missed opportunities to enhance decision making, customer experience and profits."
So what do you do? Use RPA to assist humans performing these activities instead of fully replacing them.
For example, for customer service RPA can rapidly compile relevant customer history, transaction details and account info to help agents deliver personalized service.
In sales, RPA can automate repetitive administrative work to free up time for human sellers to focus on relationship building, persuasion and closing deals.
Processes Dealing With Unstructured Data
Here‘s an RPA limitation upfront – it only works with structured, predictable data. But according to Forrester, up to 80% of business data is unstructured or semi-structured.
This includes documents, emails, PDFs, scanned images, audio, video and more. Their free-flowing nature makes them incompatible for RPA without processing.
For instance, say you want to implement RPA for invoice or mortgage processing. The unstructured application letters and supporting documents will cripple automation efforts.
Gartner found only 16% of firms manage to use RPA on unstructured data. The rest struggle to move beyond pilots and proofs-of-concept.
As Mike Varley, automation expert says:
"Attempting to implement RPA without a solution for unstructured data will result in stunted adoption. The volume of unstructured content in most processes makes this a non-starter."
The remedy? Use AI-based techniques like optical character recognition (OCR) and natural language processing (NLP) to structure unstructured data.
OCR extracts text from scanned documents, handwritten notes and even photographs. NLP parses and analyzes free-form human language, extracting relevant entities and relationships.
For example, this insurance firm automated policy cancellation processing by using AI to structure unstructured policy cancellation notices received. This boosted efficiency by over 70%!
The point is – combine RPA and AI to allow your bots to consume all data formats for end-to-end process automation.
Highly Complex Processes
Heads up – extra complex processes with convoluted decision trees and intertwined systems pose automation nightmares.
RPA bots rely on preset rules and logic. So navigating intricate processes with dynamic scenarios and exceptions makes them brittle.
For example, this bank automated over 2000 processes using RPA without optimizing them first. Very soon it backfired badly:
"Interdependencies between the 2000 bots across departments became so complex that the RPA initiative was deemed more trouble than it was worth."
IDC estimates only 19% of firms succeed with automating processes with over 50 decision points and contingencies.
According to Rob Ryan, BPM expert:
"Attempting to layer RPA on convoluted processes creates a mess. First simplify complex processes through visualization, optimization and decomposition before adding automation."
The cure? Begin by simplifying complex processes using techniques like process mapping, simulation and re-engineering.
For instance, create swimlane diagrams to visualize cross-functional workflows and identify complexity drivers. Analyze simulations to isolate bottlenecks and redundant steps. Re-engineer processes using lean or six sigma before automating.
You should also pilot RPA on fragmented processes in phases versus end-to-end automation all at once. This prevents unmanageable complexity from accumulating as you scale.
Processes With Minimal ROI
Let‘s be honest – RPA is not a magic wand for boosting productivity and cutting costs. Implementing RPA without a solid business case leads to lackluster ROI.
As per McKinsey, over 60% of RPA initiatives fail to clear ROI hurdles for automating rarely performed, low-value tasks. The costs of bots, infrastructure, maintenance and governance outweigh the economic benefits.
For example, this bank automated over 50 processes without quantifying their value. Later they found RPA delivered little tangible ROI for those processes.
Research shows only 21% of companies do an adequate cost-benefit analysis before automating. The rest risk poor ROI by blindly targeting technically viable but economically unviable processes.
According to Martin Kornev, RPA expert:
"I‘ve seen companies get enamored with automating their processes without checking the numbers first. This wastes resources that are better applied to high-impact processes providing sufficient payback."
The solution? Do your automation homework through detailed ROI analysis beforehand.
Calculate full process costs using top-down benchmarking data. Factor in ongoing RPA costs like licensing, infrastructure and governance. Estimate automation effort based on process complexity.
Compare costs versus expected benefits – things like FTE savings, error reduction and faster processing. This will reveal the processes that justify RPA investment versus low-ROI automation candidates.
Ongoing governance helps cull out automations not meeting ROI thresholds. This prevents the RPA "bot sprawl" plaguing many companies.
Immature Processes
Here is a reality check – RPA success relies on stable, consistent processes. But many processes undergo frequent changes due to evolving systems, regulations and even company reorganizations.
Automating such immature processes almost guarantees maintenance headaches when (not if) underlying drivers change.
For example, many banks had to scrap finance process automation initiatives due to regulatory changes. New data privacy laws like GDPR alter information flows, breaking existing automations.
According toIDC, 75% of companies suspend RPA bots due to underlying process changes. The time spent reconfiguring and testing bots significantly erodes ROI.
As Uday Chinta, automation architect says:
"I advise clients to wait at least 12 months before automating any process undergoing changes. Otherwise change management costs exceed any efficiency gains from RPA."
The treatment? Postpone automating unstable processes until they mature sufficiently.
Processes reaching consistency for 12+ months are less likely to require rework after automation. Adopting low-code RPA also provides flexibility to reconfigure bots faster when needs be.
Regularly review automated processes for change indicators – like new regulations, updated systems, modified KPIs. This allows proactively modifying bots before disruption strikes.
Assessing Process Automation Readiness
Before proceeding with automation, assess process suitability thoroughly using steps like:
Map current processes – Visualize as-is workflows through process mining to identify problem areas.
Measure KPIs – Establish process KPIs like cost, quality, speed for pre/post analysis.
Analyze ROI – Estimate full costs and benefits to build a concrete business case.
Check process stability – Ensure minimal process changes over the last 12-18 months.
Validate technical feasibility – Confirm existing systems can integrate with RPA without significant rework.
Interview process participants – Get staff insights into potential automation risks or gaps.
Skipping validation and blindly rushing into RPA leads to lackluster results. But carefully transforming processes unfit for automation today can provide big payoffs down the road.
Key Takeaways
- Processes needing emotional intelligence like customer service require RPA assistance, not replacement.
- RPA struggles with unstructured data like images or emails. Structure data first using AI techniques like OCR.
- Highly complex processes become unmanageable when automated as-is. Simplify first.
- Quantify costs and benefits to avoid low-ROI automation traps.
- Automating constantly changing, immature processes multiplies rework. Wait until processes stabilize.
While not a panacea, RPA can drive huge efficiency gains for the right processes. With diligent validation and transformation, you can make even challenging processes shine through intelligent automation.
I hope these tips help optimize your automation success. Feel free to reach out if you need help assessing process automation potential for your company. I‘m always happy to help fellow automation enthusiasts!
