What is Intelligent Automation: An In-Depth Guide to the Evolution of RPA and Its Future Outlook

ByPaul Christiano Last Update on

Hello there! Are you looking to understand what intelligent automation is, how it is transforming businesses, and what the future holds? Well, you‘ve come to the right place!

As automation spreads across organizations, we‘ve seen robotic process automation (RPA) rapidly evolve from simple bots automating repetitive tasks to smarter tools powered by game-changing technologies like artificial intelligence (AI) and machine learning (ML). This next-generation wave promises to reshape not just business processes, but even entire operating models.

So let‘s start from the basics – what exactly is intelligent automation and how does it build on RPA?

What is Intelligent Automation?

Intelligent automation refers to RPA bots and tools augmented with cognitive capabilities that allow them to handle unstructured data, ambiguity, and make ‘human-like‘ judgements by learning from experiences and patterns.

This is achieved by harnessing technologies like natural language processing (NLP), machine learning, computer vision and optical character recognition (OCR). These powerful AI capabilities allow intelligent automation to excel at more complex use cases compared to traditional RPA.

For example, NLP enables bots to analyze text-heavy documents like legal contracts, support tickets or product reviews to derive meaning and sentiment. Computer vision allows them to identify objects in scanned images and videos to extract information. Machine learning helps mimic human decision-making for nuanced tasks.

According to Deloitte, over 70-80% of an enterprise‘s knowledge work tasks can be enhanced or automated through intelligent automation. The promise is immense, and this is reflected in soaring projections for adoption.

Gartner predicts that by 2024, over 65% of large organizations will have adopted intelligent automation in some form. Let‘s look at how we reached this inflection point and the key drivers fueling rapid growth.

The Evolution of RPA into Intelligent Automation

We can trace the evolution of RPA across three broad generations:

First Generation: Basic Process Automation

The first RPA tools that emerged in the early 2000s focused solely on automating simple, repetitive digital tasks involving structured data. These early solutions provided rule-based scripting without any intelligence.

Second Generation: Built-In Cognitive Capabilities

By 2015, RPA vendors started incorporating basic cognitive features like NLP, ML and OCR natively into their tools. This enabled automation of more complex processes like extracting information from images, texts and analyzing sentiment.

Third Generation: Integrating with AI Ecosystems

The latest generation of tools don‘t attempt to build all cognitive capabilities in-house. Instead, they integrate with cloud-based AI services, marketplaces and platforms to leverage cutting-edge solutions tailored for specific needs.

For instance, UiPath offers Document Understanding skills to extract info from contracts, but also integrates with Google‘s Document AI, AWS Textract and Microsoft Azure Cognitive Services to augment capabilities. This flexible, ecosystem approach is the future.

The Key Pillars of Intelligent Automation

While individual capabilities vary across tools, most leading intelligent automation platforms leverage a common set of key technologies:

Natural Language Processing – Derives meaning from text by analyzing grammar, sentence structure, context etc. Enables conversational bots, contract analysis, sentiment analysis and more.

Machine Learning – Mimics human-like judgement and decision making by learning from data patterns. Allows handling ambiguous information.

Computer Vision – Analyzes and extracts information from images, videos and other visual inputs using AI. Enables document digitization, facial recognition, object detection etc.

Optical Character Recognition – Identifies text in scanned documents and handwritten input by analyzing fonts, styles etc. Unlocks information trapped in papers.

These pillars augment RPA with the ability to handle unstructured data, learn continuously, and make smart decisions. Now let‘s look at some leading products in this space.

Intelligent Automation Capabilities of Top RPA Vendors

While the market is competitive, some major RPA platforms lead the pack when it comes to intelligent automation:

VendorKey Intelligent Automation Capabilities
Automation AnywhereIQ Bot combines RPA with NLP, ML and analytics.
Blue PrismLeverages document comprehension, sentiment analysis, computer vision.
UiPathSkills include document understanding, sentiment analysis, forecasting.
MicrosoftTightly integrates Azure Automation with Azure AI services.
WorkFusionKey skills are ML, NLP, OCR, predictive analytics.

However, RPA vendors cannot build tailored AI solutions for every customer need. This is where the broader enterprise automation ecosystem comes in.

Looking Beyond RPA Vendors

While RPA platforms are enhancing their cognitive capabilities, they are also partnering with other technology providers to augment solutions:

  • Cloud providers – AWS, GCP, Microsoft Azure provide on-demand AI services like vision, language, voice APIs.
  • Global IT cos – Firms like IBM offer Watson AI and deep consulting expertise around automation.
  • Niche AI startups – Numerous startups provide specialized capabilities around data validation, document processing, analytics.
  • Digital transformation firms – IT consulting companies also offer custom automation solutions leveraging AI.

This ability to tap into the broader ecosystem allows enterprises to get the best-of-breed capabilities for their specific process automation needs in a flexible and scalable manner.

Now that we‘ve looked at the key vendors and players, let‘s explore some common use cases.

Intelligent Automation Use Cases Across Domains

Intelligent automation is enabling breakthroughs in operational efficiency, quality, and insights across functions:

Banking

  • Automating KYC processes using ML and NLP for identity verification.
  • Processing trade finance transactions by digitizing paperwork and ensuring compliance.
  • Accelerating underwriting by extracting data like income, credit scores from documents.

Insurance

  • Managing policies by extracting data from forms to assess risks and recommend changes.
  • Claims processing by analyzing documents and approving payouts automatically.
  • Detecting fraud patterns across claims data using analytics.

Healthcare

  • Automating patient onboarding by filling forms and updating records across systems.
  • Speeding up billing and payments by extracting data from unstructured reports.
  • Deriving insights from medical research papers to aid clinical decisions.

The use cases are extremely diverse, but ultimately help drive cost savings, improve compliance, and unlock value from unstructured enterprise data.

Now that we‘ve seen examples of the technology in action, what does the future look like?

The Road Ahead: Intelligent Automation in 2024 and Beyond

Looking ahead, here are the key trends that will shape intelligent automation:

  • Wider adoption by medium enterprises as solutions get more affordable and accessible.
  • Tighter integration between automation tools and broader enterprise software ecosystems.
  • Rise of industry-specific solutions rather than one-size-fits-all tools.
  • Improved NLP capabilities to understand natural language and conduct human-like conversations.
  • More focus on augmenting human capabilities rather than pure labor displacement.
  • Evolution of organizational structures and policies to effectively govern automation programs.

The future offers exciting possibilities, but success ultimately lies in striking the right balance between bots and humans. While routine tasks get automated, human creativity, empathy and strategic thinking will remain vital.

Key Takeaways

  • Intelligent automation combines RPA with AI capabilities like NLP, ML and computer vision to handle unstructured data and make decisions.
  • It builds on RPA to automate more complex, cognitive knowledge processes across domains.
  • Leading RPA tools now offer inbuilt intelligence, and also integrate with broader AI ecosystems for augmentation.
  • Adoption is surging with over 65% of large companies expected to adopt intelligent automation by 2024.
  • Look for tighter integration with enterprise stacks, rise of industry-specific solutions, and human-bot collaboration.

So in summary, intelligent automation is truly taking RPA to the next level. While challenges remain around change management, the future looks promising as cognitive bots tackle increasingly complex tasks and free up human talent for higher-value work.

I hope this guide offered you a comprehensive introduction to intelligent automation. Let me know if you have any other questions!

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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