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From rigid to robust: How AI in business process automation is changing the game?

AI in business automation
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Automation has undeniably become a cornerstone of success for modern enterprises, propelling efficiency and productivity to new heights while enhancing the overall quality of results. In the past, automation efforts were limited to tools like Windows-based, Web-based, or Citrix-based automation, each with its own capabilities and constraints. Two significant challenges lingered: the lack of capability to orchestrate a seamless end-to-end process across diverse systems, applications, and technologies and the missing element of “intelligence” within the automation.

Enter robotic and Intelligent Process Automation (RPA and IPA), which brings new dimensions to automation by filling these gaps. An exciting synergy emerges with Artificial Intelligence (AI), which mimics human cognitive functions, and Business Process Automation (BPA), specializing in automating mundane and repetitive human tasks. These technologies are more than mere buzzwords; they represent a transformative wave in the business landscape. Together, AI and BPA can redefine the essence of business processes, making them more efficient, precise, and cost-effective.

Utilizing AI-driven innovations like chatbots for constant customer support, machine learning for personalized marketing campaigns, and advanced data analytics for informed decision-making, alongside BPA’s ability to simplify workflows like data entry or document processing, is a testimony to a new era. An era where businesses harness the dual power of AI and BPA to reach their goals, amplify growth, and navigate a rapidly evolving marketplace with agility and intelligence.

The article provides an in-depth analysis of the underlying technologies, real-world applications, benefits, and challenges, offering a comprehensive view of how AI is reshaping the landscape of business process automation, driving innovation, and providing a competitive edge.

What is business process automation?


Business Process Automation (BPA) stands as an essential paradigm shift in modern business operations. By melding technological advancements with strategic objectives, BPA offers a pathway to a streamlined, efficient, and strategically aligned business model. Its multifaceted applications, ranging from HR to marketing, exemplify the transformative potential of automation, setting a benchmark for the future of business innovation.

BPA employs advanced technology to execute business tasks, from simple repetitive actions to complex workflows. It functions to automate processes traditionally performed manually, leading to efficiency and consistency and freeing up human resources for more intricate tasks.

Examples of BPA tools

  • HubSpot’s Marketing Hub: This powerful automation tool facilitates digital marketing by taking care of processes such as email marketing campaigns, social media scheduling, and lead nurturing, enabling marketing teams to focus on strategic planning and creativity.
  • Ontraport: Offering a blend of CRM and BPA features, Ontraport helps businesses grow by automating vital functions in sales, marketing, and online business management, creating a unified platform for all business needs.

The benefits of BPA

  • Cost efficiency: BPA cuts down operational costs by substituting manual labor with automated systems. This shift allows employees to devote time to more value-added activities, increasing productivity and quality.
  • Accuracy and efficiency: The precise nature of automation eliminates human error, and the speed of computerized processes surpasses manual work, leading to swift and reliable outcomes.
  • Enhanced ROI and competitiveness: By optimizing business processes, BPA can drive higher returns on investment, contribute to better customer experiences, and help companies maintain an edge in their industry.

The technology behind BPA

  • Advanced tools: BPA employs sophisticated tools, including intelligent algorithms, to facilitate diverse business functions. It leverages cloud technology for seamless integration and scalability across organizational units.
  • Integration with enterprise applications: BPA features often blend with existing enterprise solutions like ERP software, providing a cohesive system that aligns with best industry practices.

Examples of Business Processes suited for automation

  • Employee onboarding: From welcoming new hires to setting up necessary credentials and training sessions, BPA ensures a smooth transition for new employees. It standardizes processes, ensures compliance, and enhances the overall experience.
  • Customer onboarding in finance: Banks and financial institutions can utilize BPA to efficiently conduct mandatory background checks and adhere to regulatory requirements, expediting customer onboarding and satisfaction.
  • IT service desk support: With the influx of IT tickets, BPA tools equipped with AI can analyze, categorize, and route requests to the right personnel, enhancing response times and overall service quality.
  • Marketing automation: BPA technologies enable businesses to deploy targeted marketing messages across multiple channels. This method enhances lead generation and helps marketing teams customize campaigns, analyze performance, and optimize strategies.

BPA applications across various business areas

Management, Operations, Supply Chain, HR, Marketing: BPA is instrumental in automating repetitive, time-sensitive, and multi-departmental tasks. Its application ranges from supply chain optimization to human resources management, aligning with compliance requirements and creating audit trails for accountability.

How artificial intelligence and business process automation complement each other?

Though Artificial Intelligence and Business Process Automation are distinct in their applications and capabilities, they converge on several key attributes. These technologies intersect at various junctions, from shared goals in business enhancement and automation to data dependency, integration, scalability, continuous evolution, and decision-making support. Nevertheless, it’s essential to recognize their unique characteristics; BPA’s rule-based automation contrasts with AI’s more advanced capabilities, like machine learning and computer vision. However, together, they can form a synergistic combination, maximizing business potential and innovation.

Focus on achieving business objectives

  • BPA: Works towards automating repetitive and rule-driven tasks, aiding operational efficiency and cost reduction.
  • AI: Goes beyond simple automation, handling complex processes requiring cognition, learning, and decision-making.
  • Similarity: Both technologies aim to enhance business performance, streamline operations, and contribute to organizational competitiveness.

Automation as a core principle

  • BPA: Automates mundane and structured tasks, following predefined rules.
  • AI: Expands automation to include complex activities, demanding learning and adaptive reasoning.
  • Similarity: They both primarily focus on automation, although at different complexity levels, fostering overall efficiency.

Dependency on data

  • BPA: Utilizes data to execute preset functions and processes.
  • AI: Leverages data to learn, adjust, and make autonomous decisions.
  • Similarity: Both rely on data for their operation, with BPA employing it for process execution while AI utilizes it for continuous learning.

Integration with existing technologies

  • BPA: Can be amalgamated with current systems, augmenting functionality without a radical transformation.
  • AI: Offers similar integration possibilities, adapting to existing technological landscapes.
  • Similarity: Both can seamlessly integrate with current systems, enhancing efficiency and functionality.

Scalability for business growth

  • BPA: Allows easy scaling of operations by handling increased workloads through automated processes.
  • AI: Equally scalable, AI can manage growing demands without substantially reducing human resources.
  • Similarity: Both technologies support organizational growth by facilitating scalability in operations.

Commitment to continuous improvement

  • BPA: Capable of constant refinement and optimization based on feedback and performance metrics.
  • AI: Inherently designed to evolve by learning from data and past experiences, becoming progressively more intelligent.
  • Similarity: AI and BPA are committed to evolving and improving over time, even through different mechanisms.

Contribution to informed decision-making

  • BPA: Offers real-time insights into business operations, aiding in data-driven decisions.
  • AI: Analyzes extensive data sets to discern patterns and trends, providing valuable insights for strategic planning.
  • Similarity: Both technologies contribute to better decision-making through data analysis and insight generation.

How AI transforms traditional business automation beyond predefined models?

AI transforms traditional business automation

The limitations of traditional process automation

Traditional BPA has been a game-changer for many organizations, automating routine tasks and improving efficiency. However, it has been confined to predefined pathways, lacking flexibility. Any alteration within the automated process can lead to errors or inefficiencies, as traditional systems are not designed to handle dynamic changes. This rigidity has been a significant limitation in an ever-evolving business landscape. That is where AI in business automation takes the lead, integrating adaptive learning and real-time analysis into the automation process. Unlike traditional BPA systems, AI-powered solutions can adjust to changes in the process, identify patterns, and make intelligent decisions, providing a more resilient and responsive system. By leveraging AI’s inherent ability to learn and adapt, businesses can overcome the limitations of traditional BPA, enabling a more agile and efficient operation that is in tune with the dynamic nature of today’s business environment.

AI into BPA – benefits

  • A dynamic solution: AI in business automation leads to a transformation in how processes are handled. Unlike traditional automation, AI doesn’t rely solely on fixed rules or programming. Instead, it learns, adapts, and evolves. This adaptive nature makes it more resilient to changes within the process, allowing it to respond dynamically to different scenarios.
  • Learning and improvement over time: With AI-powered automation, systems become akin to human learners, continually improving and refining their skills. Machines, however, have the advantage of learning at a more accelerated rate without human limitations like fatigue or subjectivity. The outcome is a level of proficiency and efficiency challenging to attain by human workers alone.
  • Expansion in applicability through machine learning: In the early stages, AI’s application in automation was restricted due to the complexity of machine learning algorithms. But as technology has advanced, so has the range of processes that can benefit from AI. Machine learning is now capable of understanding and managing even multifaceted processes. This expansion is epitomized by AI’s achievements in areas like E-Sports, where it has successfully outperformed human players in highly intricate games.
  • An attractive investment for businesses: The evolving nature of machine learning systems presents an appealing ROI for businesses. Investing in AI in business automation is analogous to hiring an ever-improving employee who continually enhances the company’s efficiency. Like Facebook, major corporations recognize this potential and invest heavily in AI research and applications.

AI in business automation marks a significant shift from static, rule-based automation to a dynamic, adaptive solution capable of handling contemporary business challenges. By combining automation’s consistency with AI’s adaptability, organizations can create systems that not only perform tasks but also learn, adapt, and excel in complex environments. This fusion presents new possibilities for efficiency, adaptability, and innovation, ushering in a new era of intelligent automation that far surpasses traditional methods.

AI technologies used for business process automation

Recent progressions in AI technology, including techniques such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP), have remarkably enhanced the capacity to automate various business procedures. These innovations provide a higher level of accuracy, efficiency, scalability, and understandability than what was attainable just a few years ago, paving the way for fresh opportunities for automation across diverse sectors.

AI technologies used for business process automation

Natural Language Processing (NLP) and Machine Learning (ML) have become vital tools in automating business processes. Here’s how they are utilized:

Natural Language Processing (NLP) and Machine learning (ML)

NLP gives machines the capability to interpret and comprehend human language, opening up avenues for various applications:

  • Automated text and sentiment analysis: NLP can be deployed for automatic text examination, mood assessment, language translation, and interactions with chatbots.
  • Customer support automation: Businesses can automate areas like client support, document scrutiny, and data retrieval with NLP algorithms that pull out insights and meanings from the text.
  • Speech recognition: Along with models like Long Short-Term Memory (LSTM) and Transformer architecture, NLP can be applied for speech recognition, translating spoken language into written form. This supports automated transcription and voice-activated commands.

ASR systems and NLP applications assist in automating tasks like automated translation and information retrieval.

ML allows machines to learn from data, recognize patterns, and improve performance without being explicitly programmed. Here’s how ML is applied:

  • Decision trees and random forests in business automation: ML algorithms like decision trees and random forests have been used to make decisions based on specific attributes for years. For example, they can segment customers for targeted marketing or detect fraud by identifying patterns in transaction data.
  • Deep learning (DL) advancements: More recently, DL models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have brought further improvements. CNNs are adept at processing grid-like data, such as images, while RNNs excel in language modeling and machine translation tasks. CNNs are valuable in automating tasks like document categorization and data extraction. For instance, DocExtract uses AI and ML to digitalize and sort both physical and digital documents. RNNs are widely employed to automate sentiment analysis, helping businesses understand customer sentiment and make informed decisions. RNN models, especially LSTM variants, have revolutionized chatbot development. They enable more natural interactions, automating customer support and providing personalized assistance. RNNs are effective in forecasting tasks, enabling automation in demand prediction, inventory control, and sales projection.

These examples highlight the vast potential of NLP and ML in business process automation. Understanding complex data types like images, videos, and text serve as indispensable tools in automating various tasks and enhancing efficiency within business processes.

Computer vision

Computer Vision, a subset of artificial intelligence and computer science, plays a pivotal role in Business Process Automation (BPA) by enabling computers to understand, interpret, and analyze visual information from images, videos, and other visual inputs. The essence of computer vision lies in imitating human visual perception, extracting meaningful information from visual data similarly to how humans do. This includes recognizing objects, tracking movement, assessing depth or 3D structure, and more. It does so through:

  • Developing algorithms and models: These are created to analyze and process images or videos, extracting information about objects, scenes, patterns, motion, spatial relationships, and other visual attributes.
  • Using machine learning and deep learning techniques: Models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used to learn patterns and features automatically. These models can recognize, classify, and detect objects within images and videos.

Applications in Business Process Automation

  • Image and video analytics: CNNs are instrumental in automating image and video analysis. For example:
  • Quality control in manufacturing: Computer vision can automate visual quality inspection, detecting defects or inconsistencies in products on the manufacturing line.
  • Video surveillance: In security and surveillance, computer vision can automatically detect and track objects, recognize specific activities, and analyze video content.
  • Facial recognition: In sectors like security or customer service, facial recognition can authenticate or personalize user experiences.
  • Medical imaging: In healthcare, computer vision can assist in analyzing medical images and identifying patterns related to diseases or conditions, thus automating the diagnostic process.
  • Robotics: Computer vision supports the automation of robotic tasks by enabling robots to interpret their environment, navigate autonomously, and interact with objects.
  • Autonomous vehicles: It plays a vital role in automating driving tasks by enabling vehicles to interpret their surroundings, detect obstacles, recognize traffic signals, and navigate accordingly.
  • Augmented Reality (AR): In marketing or education, AR applications use computer vision to blend virtual elements with the real world, providing interactive and engaging experiences.

Robotic Process Automation

RPA is an essential component of BPA, providing the means to automate a wide range of routine and rule-based tasks. Its integration with AI further broadens its capabilities, allowing for more intelligent and adaptive automation. By enhancing efficiency, accuracy, and flexibility, RPA streamlines processes and frees human workers to focus on more complex and creative tasks. The symbiosis between RPA and BPA represents a transformative shift in modern business operations, facilitating a new level of agility and innovation.

Automating repetitive tasks

  • Data entry and validation: RPA can automate the manual entry of data into systems, validate the accuracy of the data, and even make decisions based on predefined rules.
  • Report generation: By gathering and processing information, RPA can create regular reports automatically, ensuring timely delivery and consistency.
  • Invoice processing includes automating tasks like scanning invoices, extracting relevant details, and processing payments without human intervention.

Integration with AI for enhanced capabilities

While traditional RPA deals with structured tasks, integrating it with AI techniques like Machine Learning and Natural Language Processing (NLP) can enable more intelligent decision-making and data analysis. This combination allows:

  • Handling unstructured data: By integrating AI, RPA can process unstructured data such as emails, documents, or social media posts, extending its applicability.
  • Intelligent decision-making: AI-powered RPA can analyze data, recognize patterns, and make decisions based on learned insights, offering more nuanced automation.

Efficiency and productivity gains

  • Faster execution: RPA bots can execute tasks much faster than human workers, drastically reducing processing time.
  • 24/7 operation: Bots can work continuously without breaks, allowing non-stop operation and enhanced productivity.
  • Error reduction: Automated processes are less prone to human error, leading to higher accuracy and reliability.

Flexibility and compatibility

  • Integration with existing systems: RPA can work with various software applications, databases, and systems without major IT changes or infrastructure overhauls. This flexibility makes it suitable for diverse business environments.
  • Scalability: RPA can easily be scaled up or down according to business needs, accommodating changing demands and workloads.

Use cases of AI in business automation to reach your business objectives

The synergy between AI and Business Process Automation transcends conventional business operations, adding value across different domains. These intertwined technologies are shaping modern business landscapes, from revenue enhancement and cost control to customer contentment and brand proliferation. By strategically implementing AI in business automation, businesses attain their core objectives and position themselves for sustainable growth and innovation in an ever-competitive market.

Several key use cases in modern business can be accomplished by harnessing the power of Artificial Intelligence (AI) and Business Process Automation (BPA). These use cases include:

Enhancing revenue streams

The fundamental goal for businesses is to maximize revenue and continue to increase it. Typical strategies include attracting more customers, boosting sales, introducing new offerings, or adjusting prices. Employing AI and BPA to mechanize sales and marketing can boost revenue.

For instance, CRM platforms can streamline leads, while AI-driven chatbots offer tailored recommendations, boosting sales. Invoicing tools can expedite the billing process, and predictive analytics solutions aid in forecasting sales.

Decreasing operational expenses

Cutting costs and raising profits are pivotal for businesses. This is achieved through optimizing operations, employing automation, or offloading non-essential functions. BPA aids in cost reduction by taking over mundane tasks.

Utilizing workflow automation tools can lead to substantial savings. AI-enabled finance applications can track spending and pinpoint cost-saving opportunities.

Boosting customer satisfaction

Ensuring customer satisfaction is essential for success. Offering quality products, excellent service, and being receptive to feedback is key. AI-driven chatbots can offer constant support, timely issue resolution, and individualized recommendations.

Similarly, automating order fulfillment with CRM tools offers a comprehensive view of the customer, enabling personalized interactions and boosting satisfaction and loyalty.

Increasing brand recognition

A strong brand is synonymous with a successful business. Enhancing brand awareness through marketing, online presence, and cultivating a positive image is vital. AI and BPA can assist by fine-tuning marketing strategies and boosting online visibility.

Tools for overseeing social media and SEO tools can elevate website content. AI sentiment analysis tools can gauge customer responses, enabling more targeted communication.

Expanding market share

Growing market share requires a competitive edge, expansion, or mergers. AI and BPA provide valuable insights into customer behavior and enhance operational efficiency.

AI-driven analytics tools can pinpoint areas for growth, while supply chain software can streamline supplier interactions. Business process automation (BPA) aids in staying competitive and capturing a larger market share.

Fostering innovation

Staying ahead in the ever-changing business world necessitates innovation. Understanding customer needs from various feedback channels can guide the creation of innovative products. AI and BPA facilitate innovation by offering insights and uncovering new opportunities.

Analytical tools can identify new products by analyzing customer feedback. Meanwhile, business process automation (BPA) software can refine processes, eliminate inefficiencies, and enhance quality, fostering innovation and efficiency.

AI and Business Process Automation provide versatile solutions across diverse aspects of business, from revenue growth and cost reduction to customer satisfaction, brand development, market expansion, and innovation. By integrating these technologies, businesses can forge a path toward greater success and sustainability.


In the rapidly evolving landscape of modern business, AI in business automation is a transformative milestone. As we’ve traversed the myriad ways AI enriches BPA—from automating repetitive tasks to unlocking intelligent decision-making and enhancing customer experiences—we’ve glimpsed a future where machines mimic human abilities and amplify them.

The harmonious marriage between AI and BPA is not a mere enhancement but a considerable shift reshaping how businesses operate. It’s a change that propels organizations beyond conventional boundaries, opening doors to unprecedented efficiency, agility, and innovation.

The benefits we’ve explored in this article only scratch the surface of what’s possible. With each passing day, advancements in AI and machine learning are unveiling new horizons for automation, creating opportunities that were once the realm of science fiction.

However, not just the technology itself but how we harness it will define our success in this brave new world of automated business processes. Ethical considerations, a clear understanding of business goals, and a focus on enhancing human collaboration rather than replacing it are vital to realizing the full potential of AI in BPA.

As we stand on the brink of this exciting frontier, one thing is clear: AI is not merely an optional addition to the business toolkit; it is becoming a critical engine driving the future of business. Embracing this dynamic fusion of AI and BPA is not just a pathway to stay competitive; it’s an invitation to lead in an era of intelligent automation, where creativity and strategy meet technology, forging a future that’s as inspiring as it is inevitable.

Adapt, innovate, and thrive in today’s fast-paced business landscape by unlocking efficiency with AI-powered Business Process Automation. Start your automation journey with LeewayHertz’s AI experts and transform your processes now with intelligent automation!

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Author’s Bio

Akash Takyar

Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of building over 100+ platforms for startups and enterprises allows Akash to rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

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