• Zerqam Salman and Tayabur Rahman
  • Published: 31 August 2021

By 2022, 65% of organizations that deployed Robotic Process Automation (RPA) will introduce Artificial Intelligence (AI), including Machine Learning and Natural Language Processing
algorithms. Intelligent Automation (IA) is an automation solution that combines complementary automation technologies with AI-enabled intelligence to digitize business processes. IA employs AI to augment task-level automation using RPA within business processes that have been streamlined using Business Process Management (BPM). BPM is used to orchestrate users, data, tasks, systems, and RPA bots while AI is used to automate the decision-making process.  


Organizations tend to implement various automation tools, such as RPA, from multiple vendors to accelerate their digital transformation journeys. However, these solutions are often poorly integrated and delivered in silos to provide fragmented solutions. Without the oversight of an enterprise-wide strategy, these automation silos create technical debt and hinder an organization’s ability to achieve long-term agility.

“The deeper organizations get into their transformation initiatives, the more challenging it is to readjust their strategies. As a result, in hindsight, firms realize that they invested in a set of technologies that are challenging to maintain — leading to high technical debt”.

As an increasingly popular alternative to this ad-hoc automation, a single-vendor Intelligent Automation approach provides a holistic, scalable, and intelligently integrated solution for end-to-end automation. IA combines the individual benefits of RPA, BPM and AI tools with the over-arching objective of improving customer experience, organizational agility, and employee productivity.

IA helps organizations move beyond basic use-cases and low-hanging fruit towards an accelerated digital transformation. Yet many organizations fail to realize the full benefits of IA due to the challenges associated with skilled resources, security risks, legacy systems, adoption, and cultural shifts. Legacy systems can be difficult to integrate and too many legacy systems can minimize the increase in efficiency from IA. Perhaps the bigger challenges are the security threats that can arise from data leaks, unauthorized access, privilege abuse and system vulnerabilities. Improper planning and implementation will only add to the complexities through tool sprawl and weak quality assurance.

The rising trend of IA implementation within organizations can be attributed to its ability to achieve a quick ROI by transforming simple, manual, and repetitive front or back-office business processes. However, to achieve sustainable competitive advantage, organizations need to scale beyond the simple repetitive task automation and utilize the intelligent toolkits on an enterprise level. Successful organizations will follow a well-defined and structured approach to leverage IA capabilities and align it with their enterprise digital strategy. 


Stages of the Intelligent Automation approach

  Identify Opportunities   Build Business Case  Create IA Toolkits Deliver Automation 
Key Components 
  • Identify the business problems
  • Define the desired business outcomes
  • Evaluate the complexity of the current state processes
  • Simplify the scope by removing unnecessary steps
  • Define the use cases
  •  Measure the desired business outcomes
  • Estimate capital and operating expenses
  • Set up financial milestones and KPIs
  • Assemble IA capabilities based on the complexity and scalability of the solution
  • Ensure alignment of the proposed solution with the enterprise digital strategy
  • Create a 'proof of concept' to test and fine-tune
  • Identify the right resources and environments
  • Follow iterative build and test process
  • Deploy a flexible target operating model
  • Conduct change management and workforce training

Selecting the right type of IA tool to streamline and transform business process is crucial for successful implementation and quick benefits realization. Organizations need to fully understand the complexity of business requirements and decide whether they need to bring in tactical changes by automating specific repetitive back-end operations or transform the entire process with an enterprise digital strategy.
Spectrum of Intelligent Automation Tools

IA provides an integrated solution to streamline complex, end-to-end processes within Financial Services in areas such as customer onboarding, document verification, fraud detection, claims management and many more. Let us take a closer look at fraud detection within a claims process, where RPA is already being utilized to flag suspicious claims.


At Capco, we combine our extensive Financial Service and Digital expertise to help our clients implement effective automation solutions to drive digital transformation with scalability and continuous improvement embedded at its core. To learn more about Capco’s Intelligent Automation solutions, contact


1. Gartner Research, “Move Beyond RPA to Deliver Hyperautomation”

2. The Kofax 2020 Intelligent Automation Benchmark Study