Today, we live and work in a digital-first business environment, flooded with vast amounts of information daily — email correspondence, scanned PDFs, handwritten notes, mobile photos, uploaded documents, or system-generated files. This data is highly valued, but it is dirty, turbid, and unstructured. And that is precisely where most organisations get stuck.

The complexity here is not something that traditional automation is designed to address. This is why today organisations are moving towards RPA services for workflow automation combined with AI/ML and building effective intelligent processes with respect to real-world messy data, which are scalable and reliable.

In this article, it spells out – clearly—why RPA + AI/ML is needed, how it works, its benefits, the stats behind it and why organisations not adopting intelligent automation fall behind.

Drawback — RPA Alone Is Not Enough

Robotic Process Automation (RPA) is great for repetitive and rules-based processes. It is most effective when inputs are:

• Organized
• Structured
• Predictable
• Template-based

For example: Copy-paste data from Excel to ERP, update CRMs, generate reports, validate entries, etc

But natural business data is not structured. Let’s look at typical examples:

• Sending free text emails to the customers
• Vendors are sending invoices in multiple formats
• People upload scanned PDFs
• Photos are submitted by agents from a mobile device
• Employees attach handwritten notes
• Clients share IDs as images

RPA bots are unable to “comprehend” natural language, handwritten text, document format variance, low-quality scans or images because they react only to fixed rules.

This is why RPA by itself cannot automate:

• Email classification
• Content extraction
• Image reading
• Scanned document interpretation
• Intent analysis
• Complex document workflows

This is where AI/ML fits in as the layer of intelligence that is missing.

Why RPA + AI/ML Is the Only Real Solution

Together, RPA manages the process steps and fax workflows, while RPA services for workflow automation with the help of AI/ML brings in the needed intelligence to process unstructured data.

1. AI + OCR Converts Scanned Documents & Images into Text

AI-powered OCR greatly enhances accuracy in extraction.

Today, a modern (AI-enhanced OCR) system achieves high accuracy, even for:

• Low-quality scans
• Photos taken at an angle
• Handwritten notes
• Multi-format invoices
• Stamped or noisy images

According to statistics from Sci-Tech Today, a study on intelligent document automation has found that IDP — an advanced AI system — can deliver up to 99% accuracy for document extraction and minimise data-entry errors.

2. NLP Parses and Understands Email

Natural Language Processing (NLP): NLP assists in automating systems:

• Identify the intention (requesting a refund, submitting an invoice, making a complaint, etc.)
• Identify details (dates, prices, titles, IDs)
• Classify emails automatically
• Detect urgency and sentiment
• Route messages to the correct departments

You cannot automate emails, without NLP.

3. ML Makes Decisions and Handles Variations

Machine learning helps bots:

• Recognise document types
• Then it would predict fields even if the layouts change
• Flag abnormalities
• Identify duplicates
• Improve with feedback
• Code-Free handling of new versions of a document

It is this constant learning that makes simple automation intelligent automation.

4. RPA Executes Actions at Scale

Now that the content is interpreted by AI, RPA comes into play:

• Entering extracted data into systems
• Updating CRM/ERP fields
• Triggering workflows
• Sending acknowledgement emails
• Archiving processed documents
• Creating tickets
• Validating entries

It offers this unique combination that allows for end-to-end automation.

Why Companies Are Turning Towards Intelligent Automation? The Business Case

Massive Growth in RPA Adoption

A rise in global RPA adoption is observed through studies that illustrate:

With stats collected by SEOSandwitch, over 73% of organisations are using or will start using RPA by 2025.

Quick Growth of Intelligent Document Processing (IDP)

According to a 2025 survey published by BusinessWire, it revealed that: 

• 65% of companies are accelerating IDP projects
• 50% cited reduced processing time as their biggest gain
• 30% cited headcount optimisation
• Paper and scanned documents remain prevalent despite digital transformation

Reduce efforts and enable more profitable returns to the business with AI-driven automation. Based on a study from DoIT Software:

• Automation helps companies cut down on costs by 30–40%
• Productivity improves by 20–50%
• Intelligent workflows provide much quicker return on investment (ROI) than manual and time-consuming processes

Hence, this means that RPA services for workflow automation +AI/ML is not optional, but a requirement for operational efficiency.

Why Emails, Scanned Docs & Images Depend on AI/ML

So to be extremely clear, let us break it down: 

Emails Require:

• NLP to understand language
• ML to classify requests
• Extraction models to identify attachments
• Sentiment analysis for priority

Scanned Documents Required:

• OCR + computer vision
• ML for layout detection
• Field-level extraction
• Validation logic
• Confidence scoring

Images Require:

• Computer vision
• Handwriting recognition
• Noise removal
• Cropping & enhancement
• Entity extraction

RPA by itself can do none of these.

And that is the reason intelligent automation (RPA + AI/ML) is the only real-world model that works on unstructured data.

Top Business Use Cases (Simple, Real & Practical)

1. Invoice & Accounts Payable Automation

Vendors basically have their own invoice formats. Intelligent automation extracts:

• Vendor name
• Amount
• Due date
• Invoice number
• Line items

RPA then posts it into ERP.

2. Customer Email Automation

It is in response to emails with such vague instructions as:

Please address my ID and update my address —

NLP comprehends the intention, extracts, checks & retrieves attachments along with identity, and RPA pushes the same into CRM automatically.

3. KYC & Identity Verification

Customers upload documents of IDs as images or PDFs. AI extracts:

• Name
• Address
• DOB
• ID number
• Signature

RPA verifies, contrasts, and makes changes to onboarding systems.

4. Claims Processing (Insurance/Healthcare)

Claims often include:

• Photos
• Receipts
• Printed forms
• Doctor notes

Content is identified by the AI; an automated RPA handles the rest of the claim validation activity.

5. Digital Mailroom Automation

Almost every day, I get scanned letters and emails. AI classifies content into:

• Supplier
• Customer
• Operations
• Compliance
• Support

RPA channels them to appropriate queues. And this is how leading enterprises drive operational modernisation.

Why RPA + AI/ML Outperforms Humans in Document Workflows

1. Faster Processing

With AI acceleration, IDP systems can process documents 10x–20x faster.

2. Higher Accuracy

AI-powered IDP leaves no room for error, with studies reporting up to 99% accuracy.

3. Lower Operational Costs

Millions saved in manual handling by the organisations.

4. Consistency & Compliance

Bots when they follow the rules to the dot, create logs and lower the compliance risk.

5. Scalability

No matter how many documents, automation scales immediately to handle 1,000 versus 1M.

How Organisations Should Implement: A Practical Roadmap

1. Identify High-Value Processes

Begin with One-time Processes that Include Repetitive Unstructured Inputs:

• Invoices
• Email support
• Claims
• KYC
• Approvals

2. Choose the Right Platform

Choose tools that offer:

• Built-in OCR
• NLP
• Machine learning
• RPA orchestration
• Low-code no-code development solution
• Integration options

This reduces technical complexity.

3. Build Intelligent Workflows

Combine:

• RPA for steps
• AI/ML for interpretation
• Human-in-the-loop for difficult cases

4. Monitor, Improve & Scale

Use feedback to improve:

• Accuracy
• Speed
• Coverage
• Confidence scoring

Scale to other departments gradually.

Why Low-Code/No-Code Platforms Matter

Modern adoption depends on accessibility.

A low-code no-code development solution aids in:

• Non-developers build and modify workflows
• Business teams handle configurations
• Reduce IT dependency
• Speed up automation delivery

All of these are the reasons which made a lot of top RPA service providers have LCNC capabilities embedded in their platform as of today.

Challenges to Be Aware Of

Intelligent Automation is intelligence in physical form, but there is still more to come and be prepared for in businesses where:

• Variations in document types
• Need for high-quality training data
• Initial model training effort
• Integration challenges
• Governance & data security needs

These are challenges that can be overcome with the right planning and tools.

Conclusion: RPA +AI/ML Are No Longer Options — They Are Necessities

Enterprises are handling tremendous amounts of unstructured data daily now. Well, Emails, scanned PDFs, mobile images, and handwritten forms — all need intelligence to be automatable.

That intelligence comes from AI/ML. RPA provides the execution and workflows. Collectively, they provide end-to-end AI ML services for companies to deploy these advanced models process automation that is:

• Fast
• Accurate
• Scalable
• Cost-efficient
• Future-proof

Intelligent automation gives organisations the competitive edge, while manual work leaves others at a loss in productivity, compliance and customer experience.

If you want to simplify operations in your organisation, while avoiding higher manual dependence and automation of unstructured data, then RPA services for workflow automation + AI/ML powered by best-in-class platforms and top RPA services providers is the only true viable solution.