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Intelligent Automation: Understanding the Tools that Transform Your Business – ReadWrite



Bruce Orcutt

Digital transformation has become a buzzword that can be an overwhelming concept. Most companies know they need to do it, but they are not sure where to start. With so many different acronyms and terms in the world of intelligent automation, it can be challenging to know what it all means.

Understanding the Tools that Transform Your Business — Digital Transformation

According to Gartner, 91% of organizations are engaged in digital initiatives, and 87% of senior business leaders say digitalization is a priority.

Whether it is a digital front-end platform that allows insurance companies to deliver a better customer experience or major healthcare providers trying to reduce underpayments, initiatives like this are all under the digital transformation umbrella.

Highly automated

The future of work will be highly automated, and for executives responsible for driving digital transformation, success will depend on getting the right combination of tools. They must also deliver innovation that enables them to disrupt their industry without disrupting its day-to-day operations.

So, how do you navigate this ever-evolving intelligent automation journey?

Understanding intelligent automation

Imagine that every morning you grab your phone right before you leave for work. It’s so routine that it’s become automatic, and you almost do it without thinking. But one day, you walk out the door, and it feels like something is missing – you realize you’ve misplaced your phone, so you go back and find it.

You just imitated intelligent automation.

Intelligent Automation

Intelligent automation involves taking a machine taught to do simple, repetitive tasks (aka automation). They teach the machine to adapt or correct its performance based on changing variables at unbelievable speed and accuracy.

Some of the top benefits of intelligent automation include greater accuracy, cost reduction, and improved customer experience.

In finance

In finance and accounting, intelligent automation solutions can interface with existing ERP systems and data from invoices and purchase orders just like humans but complete them in minutes instead of days.

In healthcare

In healthcare, for example, finance leaders can use intelligent automation within accounts payables to consolidate insurance and patient payment data. The data comes from various sources and conducts a comprehensive risk analysis of patients and payers to reduce debt and total days outstanding.

Similar risk analysis is common in F&A departments but requires access to data stuck within unstructured documents. With intelligent automation, that data is accessible, enabling data-driven decision-making at scale and on-demand.

AI usage in automation

Intelligent automation uses several AI-enabled technologies to move beyond the basic digitization of processes and truly digitally transform the way work is completed. The objective is to automate more end-to-end processes and decisions while keeping humans in the loop.

Solutions used to enable intelligent automation are not mutually exclusive and are more often than not complementary.

But what do all of these tools mean and what are their real-world benefits?

Common Terms:

Robotic Process Automation (RPA)

RPA uses software robots, also referred to as digital workers, to automate manual processes that are repetitive, prone to error, and rules-based.

RPA has been a popular, tactical tool to automate mundane tasks to initiate business processes, such as data entry between software applications. Still, many users have been dissatisfied with how RPA vendors overpromise and under-deliver on being able to truly digitally transform operations.

According to a Deloitte survey, 58% of executives reported that they had started their intelligent automation journey, showing that organizations are using RPA but are moving beyond it to ramp up deployment of more intelligent automation.

Artificial Intelligence

AI has become an umbrella term that describes several types of technologies, such as machine learning (ML), natural language processing (NLP), and optical character recognition (OCR).

They perform tasks that previously required human intelligence, such as extracting meaning from images, text, or speech, detecting patterns and anomalies, and making recommendations, predictions, or decisions. Combined, they form the foundation for the most widely used application of AI within the enterprise – content intelligence.

Content intelligence helps software bots understand and create meaning from enterprise content. It delivers cognitive skills that the digital workforce can harness to turn unstructured content into structured, actionable information to make processes run more efficiently.


NLP is a way for computers to understand human languages. It does this by processing the language data and breaking it down by context and syntax to identify what words are being used and how they’re being used.


OCR is the process of mechanically or electronically taking scanned images of handwritten or printed text and converting them digitally into machine-encoded text.

OCR works by using character recognition to identify text and numbers to extract or analyze information from documents and forms. One example is using it within the banking industry to verify checks delivered over an app when a client submits a photo of it.


ML is defined as a tool where a computer can learn from data by looking at similar patterns. It can help the automation process by being able to predict a decision a human would make from repeated patterns.

For example, in the manufacturing industry, with enough data, ML could be used to identify errors and irregularities within the manufacturing processes to ensure product quality.

Intelligent Document Processing (IDP)

IDP leverages OCR, machine learning, and natural language processing technologies to digitize and understand the most inconvenient forms. Then it and adds AI skills to RPA bots so they can learn, reason, and understand the content within various documents, and categorize these. Lastly, it extracts relevant data for further processing.

According to Everest Group, the IDP market grew 25-50% in 2020, with finance and accounting processes and banking industry-specific use cases having the most penetration. IDP solutions help enterprises achieve cost savings while improving their workforce productivity and employee and customer experience.

They are typically integrated with internal applications, systems, and other automation platforms.

Intelligent automation requires monitoring

With so many tools available for your organization to automate, initiate, and drive processes forward, it’s imperative to monitor their performance. This should include the ability to identify and rectify bottlenecks and have insights into how digitally transformed processes are impacting overall operations and the customer experience.

Process intelligence, business intelligence, and data science and analytics tools can be used alone or together to help managers and C-suite leaders know how their departments are working.

Process and task mining

The most common and expensive mistakes businesses make when implementing intelligent automation initiatives fail to properly understand how their processes are performing and then choose the wrong processes to automate.

Leveraging actual business process data is critical to the long-term success of any automation project. Powered by AI and ML technologies, process intelligence enables organizations to discover, assess, visualize, analyze, and monitor process flows.

Related to process mining, task mining can also monitor how individual employees interact with systems to determine if you need to add more training, re-evaluate any steps, or set a new best practice procedure.


Augmenting intelligent automation with analytics solves most organizations’ problems with being data-rich and information and insight poor. Analytics software can leverage data from processes to find time savings, added productivity, and opportunities for innovation.

More advanced analytics automation platforms blend analytics, data science, and business process automation into a single end-to-end platform. This helps obtain efficiency gains, topline growth, bottom-line return, risk reduction, and upskilling for your workforce.

Digital Intelligence

Digital intelligence is being able to fully see, analyze, and understand the processes and content that keep your organization moving. It enables leaders to identify shortcomings, bottlenecks, and cost drivers to pinpoint the most impactful way to automate processes.

It also allows you to resolve issues causing stagnation and elevate your automation initiatives to the next level.

While the term “sweet spot” is often used in sports, it has its place in automation, too. In the world of automation, it can be the right combination of tools that deliver a good balance of cost and benefits and automation and intelligence.

Intelligent automation is necessary to transform your workplace to empower employees, enhance customer experiences, increase ROI, and gain a competitive edge. It must be part of your organization’s overall strategic digital transformation initiative.

Image Credit; fanki chamaki; unsplash; thank you!

Bruce Orcutt

Bruce Orcutt is VP of Product Marketing at ABBYY, a Digital Intelligence company. He helps organizations to gain complete understanding of their business and raise their Digital IQ.


Fintech Kennek raises $12.5M seed round to digitize lending



Google eyed for $2 billion Anthropic deal after major Amazon play

London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Fortune 500’s race for generative AI breakthroughs



Deanna Ritchie

As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations



Deanna Ritchie

As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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