Pursuing the “Golden Path” in DevOps represents an aspirational journey towards achieving operational excellence. This metaphorical “Paved Road” epitomises a state of DevOps where development and operational processes coexist and thrive in a seamless, efficient, and highly automated ecosystem. The tangible outcomes of this ideal state are manifested through the rapid, reliable, and repeatable delivery of software, positioning organisations to pivot with agility in response to fluctuating market dynamics and the evolving needs of customers.

 

Embarking on this path is full of challenges, and it demands a strategic blend of cultural evolution, process refinement, technological enhancement, security fortification, and continuous measurement and improvement. Here’s a comprehensive exploration of each step necessary to navigate towards the “Golden Path.”

 

  1. Cultural Transformation

 

The foundation of DevOps lies within its cultural paradigm – a philosophy predicated on collaboration, transparency, and shared responsibility. The shift towards this culture is often the most formidable hurdle, as it involves dismantling longstanding silos and fostering a milieu of open communication and continuous feedback among all stakeholders, including Development, Operations, Security, and Quality Assurance teams.

 

Parallel to this is the ethos of embracing a learning mindset. An environment that encourages continuous learning, experimentation, and adaptability is critical. Here, failure is not a setback but a stepping stone to knowledge, and adaptability is the currency of innovation.

 

  1. Process Optimisation

 

The second cornerstone is streamlining processes through automation. By automating repetitive and manual tasks such as testing, deployments, and monitoring, organisations can drastically reduce human error and augment efficiency. Implementing Continuous Integration and Continuous Deployment (CI/CD) pipelines is central to this strategy. CI/CD facilitates frequent and smaller releases, which are inherently more manageable and can be more readily reverted if necessary.

 

Feedback loops are also integral to process optimisation. These mechanisms ensure that any issues are promptly identified and rectified, mitigating costs and preventing delays.

 

  1. Technology Stack and Tools

 

A robust technology stack underpins the DevOps framework. It starts with version control systems, which are the bedrock for effective code change management and collaboration. Configuration management tools follow, ensuring consistency and reliability across different environments by managing server and application configurations.

 

Comprehensive monitoring and logging are non-negotiable in a mature DevOps environment. They provide visibility into application performance and system health, enabling proactive issue resolution and informed decision-making.

 

  1. Security and Compliance

 

The incorporation of security practices throughout the DevOps pipeline, often referred to as DevSecOps is indispensable. “Shifting left” on security means embedding security considerations at the start of the software development lifecycle rather than as an afterthought. Compliance automation, or “Compliance as Code,” fortifies this approach by ensuring regulatory and industry standards are met within the CI/CD pipeline.

 

  1. Measurement and Improvement

 

Metrics and Key Performance Indicators (KPIs) are crucial for tracking progress and identifying areas for enhancement. They provide insights into delivery speed, stability, and quality. The ethos of continuous improvement necessitates regular review and adaptation of processes, tools, and outcomes based on feedback and these metrics.

 

Challenges

 

The journey to DevOps maturity is resource-intensive. It demands considerable investment in time and tools to cultivate and sustain the practices necessary for achieving the “Golden Path.” Moreover, developing or acquiring requisite skills within the team can be a formidable challenge, often necessitating extensive training and recruitment. Resistance to change is another significant barrier, with organisational inertia potentially stalling the adoption of DevOps practices and the cultural transformation that underpins it.

 

The quest for the “Golden Path” in DevOps is intricate and iterative. It requires unwavering commitment, strategic resource allocation, and the agility to adapt continuously. While the path is strewn with challenges, the pursuit is worthwhile. The rewards of enhanced agility, superior quality, and increased efficiency not only justify the efforts but also secure a competitive advantage, ensuring long-term success in a rapidly transforming technological landscape.

Integrating automation and artificial intelligence (AI) in software development reshapes how organisations approach software testing and delivery. This change is particularly significant for deploying complex software, such as Enterprise Resource Planning (ERP) or core banking systems. It affects various global software delivery methodologies that impact organisations, including Agile, DevOps, and the Waterfall model.

AI and Automation within DevOps Practices

Adopting AI and automation in DevOps aims to refine the entire software development lifecycle, from development to production. This approach improves efficiency, enhances software quality, and ensures smooth transitions between development phases. Key focus areas include strategic planning, process improvement, and change management, ensuring that technological innovations align with overall business objectives and operational requirements.

Enhancing Testing and Operations with AI

AI’s growing role in software testing and operations introduces new tools and capabilities for automating complex tasks and providing deep insights. Notable applications of AI in this area include:

  • Automated Testing: Leveraging AI to automate various testing processes, such as regression and user acceptance testing (UAT), significantly reduces manual effort and shortens the testing cycle.
  • Intelligent Data Analysis: Using AI algorithms to analyse development and operational data enables predictive analytics, allowing teams to address potential issues proactively.
  • Dynamic Environment Management: AI’s role in automating the setup, maintenance, and management of development, testing, and production environments helps minimise the resources needed for these tasks.
  • Streamlined Release Management: AI assists in optimising release management by automating deployment pipelines and predicting the impacts of new releases, leading to more efficient release cycles.

Implications for Software Delivery and Large-Scale Implementations

Automation and AI’s implications extend to the delivery of large-scale software projects. Organisations can more effectively navigate the complexities of large-scale deployments by automating critical aspects of testing, release management, and environment setup. This approach reduces risks and enhances system reliability and performance, ensuring software implementations meet high-quality standards.

Adapting Software Delivery Teams to the New Era

The transition towards more automated and AI-driven processes requires software delivery teams to adapt. This adaptation involves shifting their focus towards more strategic and innovative activities, no matter the specific methodology employed (DevOps, Waterfall, etc.). Embracing AI and automation tools enhances operational efficiency and promotes a culture of continuous improvement and skill development within teams. This cultural shift is crucial for fostering innovation and ensuring teams remain competitive in a rapidly evolving technological landscape.

Strategic Realignment Beyond Technological Integration

The successful integration of automation and AI into software development processes involves more than just adopting new technologies. It requires a strategic realignment of organisational methods and goals to leverage these technologies fully. Achieving a balance between operational efficiency, cost-effectiveness, and high-quality software delivery while also mitigating potential risks through early detection and resolution of issues is essential. This strategic approach ensures that technological advancements contribute to the organisation’s broader objectives and enhance its software delivery capabilities.

The Future of Software Delivery

As companies in various industries continue to face the challenges of digital transformation, incorporating automation and AI into software development and delivery can be a promising solution. This transition can provide significant benefits, such as improved efficiency, reduced costs, and higher-quality software products. However, achieving these benefits requires a comprehensive strategy that covers technological adoption, strategic planning, and cultural adaptation.

Organisations must utilise automation and AI to support their business objectives and enhance their software delivery capabilities. This requires adopting new technologies and fostering an environment encouraging continuous learning, innovation, and flexibility among software delivery teams. By doing so, companies can ensure they are well-prepared to tackle the challenges of an increasingly complex and fast-paced technological environment. Integrating automation and AI into software development and delivery can be a promising solution as businesses in various industries continue to undergo digital transformation. This transition can provide significant benefits, such as improved efficiency, reduced costs, and higher-quality software products. However, achieving these benefits requires a comprehensive strategy encompassing technological adoption, strategic planning, and cultural adaptation.

Introduction

Businesses are continuously striving to streamline their operations and enhance productivity. One crucial aspect of this endeavour is the management of documents and data. This is where Intelligent Document Processing (IDP) comes into play. In this blog, we’ll explore the world of IDP, its solutions, and the numerous benefits it offers to businesses looking to optimise their document-driven processes.

What is Intelligent Document Processing?

Intelligent document processing (IDP) is the process of automatically extracting, validating and transforming data from unstructured or semi-structured documents, such as invoices, contracts, forms, receipts, etc. IDP solutions use a combination of technologies, such as optical character recognition (OCR), natural language processing (NLP), machine learning (ML) and artificial intelligence (AI), to automate the document processing tasks that are usually done manually by humans.

In the ever-evolving landscape of financial services, efficiency and speed are of paramount importance, especially for small businesses seeking various forms of financing. The traditional loan processing approach has long been plagued by cumbersome paperwork, delays, and inefficiencies, hindering small businesses’ access to capital that is vital for survival. Enter AI document processing, a sophisticated technology that is facilitating change across all facets of the lending industry, encompassing loans, credit cards, home loans, asset finance, car loans, and personal loans.

The Challenge of Conventional Loan Processing

Before we delve into the transformative potential of AI document processing, let’s first grasp the challenges inherent in the conventional loan application and processing procedures across diverse financial products:

Documentation Complexity

Businesses seeking loans, particularly those held in trusts, are often required to submit a myriad of complex documents, such as trust agreements, financial statements, and legal contracts.

Paperwork Pile-Up

Small business owners are often inundated with a multitude of documents, ranging from financial statements to legal contracts, depending on the type of financing they seek.

Manual Data Entry

Loan officers typically dedicate a significant amount of time manually inputting data from these documents into their systems, a process that is error-prone and time-consuming.

Verification Hurdles

After data entry, the verification phase may entail further delays, as loan officers scrutinise the authenticity and accuracy of the provided information.

Decision-Making Bottlenecks

Complex underwriting processes, particularly for home loans and asset finance, can result in protracted approval times, frustrating both borrowers and lenders alike.

Missed Opportunities

These inefficiencies can cause small businesses to miss out on time-sensitive opportunities or face financial hardships without timely access to capital.

Fraud Vulnerabilities

Small businesses held in trusts can be vulnerable to identity fraud and misrepresentation, which may go unnoticed during manual document processing.

AI Document Processing: Transforming the Lending Landscape

AI document processing offers a revolutionary solution to the challenges of traditional loan processing across the spectrum of financial products:

Streamlined Data Extraction

AI-powered algorithms excel at automatically extracting and categorising data from diverse documents, eliminating the need for manual data entry and reducing error risks.

Data Verification Enhancement

AI systems can cross-reference data across multiple documents, ensuring data consistency and accuracy, a crucial factor in loan approval processes.

Swift Decision-Making

With AI document processing, lenders can expedite underwriting by leveraging machine learning algorithms to assess creditworthiness and risk factors more efficiently, across all types of loans.

Enhanced Customer Experience

Small business owners benefit from a streamlined application process. They can digitally submit documents and conveniently track the progress of their applications in real-time.

Operational Efficiency

Lenders can significantly reduce operational costs linked to manual document processing, data entry, and verification. This often translates into lower interest rates and fees for borrowers.

Enhanced Fraud Detection

AI algorithms can identify anomalies and inconsistencies within documents, making it harder for fraudulent submissions to go unnoticed, particularly when businesses are held in trusts.

Real-World IDP Success Stories

Leading financial institutions and innovative fintech companies have already integrated AI document processing into their operations across various loan products:

Mortgage Lenders

Mortgage lenders like Quicken Loans have harnessed AI document processing to expedite the approval process for home loans, reducing the time from application to closing.

Credit Card Issuers

Credit card companies like American Express employ AI to enhance the approval process, allowing customers to receive instant decisions on their applications.

Auto Finance Providers

Companies like Ally Financial utilise AI to streamline auto loan processing, providing borrowers with quick access to vehicle financing.

Personal Loan Providers

Online lenders such as SoFi use AI to assess borrower profiles rapidly and make prompt lending decisions for personal loans.

 

Conclusion

AI document processing represents a monumental step forward in small business financing, offering efficiency and security across various forms of loans, including credit cards, home loans, asset finance, car loans, and personal loans. Its ability to automate data extraction, verification, and decision-making not only reduces processing times but also fortifies defenses against fraud, particularly when businesses are held in trusts.

In an era where financial security is paramount, embracing AI document processing is not merely a choice; it’s an imperative. It not only empowers small businesses to access capital swiftly but also ensures that the financing process remains secure and free from fraudulent activities. As the financial industry continues to evolve, AI document processing will continue to be a cornerstone in the journey to foster growth, protect assets, and provide financial opportunities for small businesses, irrespective of their legal structure or type of financing sought.

Abstract

As technology continues to advance, the integration of artificial intelligence (AI) into various industries is becoming increasingly prevalent. In the realm of customer experience, AI has the potential to revolutionise the way businesses interact with their customers. This article explores the impact of AI on customer experience, examining its benefits, challenges, and ethical considerations. By understanding the capabilities and limitations of AI, businesses can effectively leverage this technology to enhance customer satisfaction and drive success in the age of AI.

Introduction

In today’s digital era, businesses are increasingly focused on delivering exceptional customer experiences to gain a competitive edge. With the rapid advancement of Artificial Intelligence (AI), organisations are now leveraging the power of AI to enhance customer experiences like never before. Customer Experience Automation, driven by AI technologies, has revolutionised the way businesses interact with their customers. In this blog, we will explore the key aspects of AI in customer experience, highlighting the significance of customer experience automation and the transformative impact of AI on the modern business landscape.

The Role of AI in Customer Experience Automation

a) Customer Experience Automation (CEA or CXA) refers to the utilisation of AI technologies to automate and enhance various aspects of the customer journey. By leveraging AI’s capabilities, businesses can streamline processes, personalise interactions, and deliver seamless customer experiences. Here are some key areas where AI plays a pivotal role in customer experience automation:

AI-powered chatbots and virtual assistants have revolutionised customer support by providing instant and round-the-clock assistance. These intelligent interfaces can handle routine customer queries, offer personalised recommendations, and even resolve issues efficiently. With natural language processing (NLP) and machine learning (ML) algorithms, chatbots continue to improve their conversational abilities, creating a more interactive and engaging customer experience.

Research indicates that customers are increasingly accepting AI-powered customer support. A study by Pegasystems found that 68% of consumers are comfortable receiving assistance from AI-powered chatbots. Moreover, the same study revealed that 72% of customers value AI for its ability to provide instant responses. These findings highlight the effectiveness of AI in delivering efficient and timely customer support.

b) Personalisation at Scale: AI enables businesses to analyse vast amounts of customer data, allowing for highly targeted and personalised experiences. By leveraging machine learning algorithms, businesses can gain valuable insights into customer preferences, behaviours, and purchase history. This information empowers organisations to deliver tailored recommendations, customised promotions, and personalised content, fostering stronger customer connections.

Research findings indicate that personalisation powered by AI significantly impacts customer satisfaction and loyalty. A study by Accenture revealed that 91% of consumers are more likely to shop with brands that provide personalised offers and recommendations. Furthermore, a report by Evergage found that 88% of marketers witnessed a measurable improvement in business results due to personalization efforts. These findings underscore the importance of AI in delivering personalized experiences that resonate with customers.

c) Predictive Analytics and Anticipatory Service: AI algorithms can analyse vast amounts of customer data to predict future behaviour patterns and preferences. AI-driven predictive analytics enables businesses to anticipate customer needs, behaviours, and preferences. By analysing historical data, AI algorithms can generate actionable insights, enabling organisations to proactively address customer pain points and offer personalised solutions. This proactive approach enhances customer satisfaction, reduces churn, and drives revenue growth. AI can also provide proactive recommendations, and address potential issues before they occur. For example, AI algorithms can predict when a customer is likely to require a product refill and automatically initiate the reordering process.

Research findings demonstrate the value of anticipatory service powered by AI. A study by Gartner indicated that by 2025, 75% of customer service organisations will use AI-enabled virtual customer assistants or chatbots, which can offer proactive customer service. Additionally, a report by Salesforce found that 73% of customers expect businesses to anticipate their needs and make relevant suggestions. These findings emphasise the significance of AI in providing anticipatory service, enhancing customer satisfaction, and driving loyalty.

d) Omnichannel Integration: AI facilitates seamless omnichannel experiences, ensuring customers receive consistent service across multiple touchpoints. Through AI-powered automation, businesses can synchronise customer data, interactions, and preferences, enabling a cohesive and personalised journey. This integration enhances convenience, improves customer engagement, and strengthens brand loyalty.

Transformative Impact of AI on Customer Experience

a) Automation and Efficiency: AI-powered automation can streamline and optimise various customer-facing processes, such as order management, inventory tracking, and personalised marketing campaigns. By reducing manual effort and human error, businesses can improve efficiency, deliver faster service, and enhance overall customer experience.

Research highlights the benefits of AI-driven automation in improving customer experience. A study by McKinsey & Company revealed that automation technologies, including AI, can reduce customer service response times by up to 80%. Furthermore, a report by Capgemini found that 71% of organisations have seen a significant improvement in customer satisfaction after implementing AI-powered automation. These findings emphasize the importance of leveraging AI technologies to enhance efficiency and improve the customer journey.

b) Enhanced Customer Insights: AI-driven analytics provides businesses with deeper customer insights, enabling them to understand their target audience better. By analysing vast amounts of data, AI algorithms uncover patterns, trends, and correlations that human analysis may overlook. These insights empower organisations to make data-driven decisions, refine their customer strategies, and offer more relevant and valuable experiences. Customer journey analytics can help brands gain insights into customer behaviour, preferences, needs, motivations, and expectations. It can also help brands measure and optimise the impact of their CX strategies on key metrics such as conversion rates, retention rates, churn rates, lifetime value, etc. For example, Zendesk uses AI to provide customer journey insights that help brands improve their CX performance.

c) Customer sentiment analysis: This is the process of using AI to analyse customer feedback and emotions from various sources, such as surveys, reviews, social media posts, emails, chats, calls, etc. Customer sentiment analysis can help brands understand how customers feel about their products, services, or brand image, and identify pain points, opportunities, or trends. For example, researchers used AI to track how customers feel in real time by analysing their open-ended comments in surveys. They found that these comments offer a much more reliable predictor of customer behaviour than quantitative scores.

d) Proactive Issue Resolution: AI-powered customer experience automation enables proactive issue resolution. By continuously monitoring customer interactions and feedback, businesses can identify potential problems in real-time and address them before they escalate. This proactive approach not only resolves customer issues promptly but also demonstrates a commitment to excellent service, enhancing customer satisfaction and loyalty.

e) Continuous Improvement: AI allows businesses to gather customer feedback at scale and extract actionable insights. By analysing customer sentiments and preferences, organisations can identify areas for improvement and refine their products, services, and processes accordingly. This iterative approach to customer experience drives continuous improvement, ensuring that businesses stay attuned to evolving customer expectations.

Challenges and Considerations in Implementing AI in Customer Experience

While AI offers immense potential in enhancing customer experience, there are some challenges and considerations that businesses need to address:

a) Data Privacy and Ethics: The use of AI in customer experience requires careful attention to data privacy and ethics. Organisations must handle customer data responsibly, ensuring compliance with relevant regulations and maintaining customer trust.

b) Transparency: AI algorithms can be complex, making it essential for businesses to ensure transparency. Customers need to understand how AI is being used and how it impacts their experiences. Clear communication and transparency build trust and mitigate concerns about AI-powered automation.

c) Balancing Automation and Human Touch: While AI automation streamlines processes, organisations must strike a balance between automation and human interaction. Some customers may prefer human assistance for complex queries or emotional support. Offering a hybrid approach that combines AI automation with human touch can lead to the best customer experience outcomes.

d) Continuous Learning and Adaptability: AI algorithms need to continuously learn and adapt to evolving customer needs and preferences. Organisations must invest in ongoing training and development of AI models to ensure they remain effective and relevant over time.

Conclusion

As businesses strive to deliver exceptional customer experiences, AI and customer experience automation have emerged as crucial enablers. Leveraging AI technologies, organisations can automate processes, personalise interactions, and gain deeper insights into customer preferences. The transformative impact of AI on customer experience is evident in improved efficiency, proactive issue resolution, and continuous improvement. However, businesses must address challenges around data privacy, transparency, and striking the right balance between automation and human touch. By embracing AI and customer experience automation, businesses can unlock new opportunities, build lasting customer relationships, and thrive in the age of digital transformation.

Looking to the Future

Looking ahead, AI will continue to shape the customer experience in exciting and unprecedented ways. Emerging AI technologies, like emotional AI, could provide even more personalised experiences by understanding and responding to customers’ emotional states. In addition, the growing adoption of AI in voice and visual search technologies promises to simplify and expedite the shopping experience, while the rise of AI in virtual and augmented reality could transform the way customers engage with brands and products.

However, to fully unlock the potential of these advanced technologies, businesses will need to invest in AI literacy across all levels of their organisation. This includes educating employees about AI’s capabilities and limitations, fostering a culture of experimentation and learning, and ensuring ethical AI practices.

In the age of AI, the customer experience is more than just a buzzword; it’s a strategic priority that can make or break a business. With AI at the helm, businesses have an unprecedented opportunity to innovate, personalize, and enhance the customer journey like never before. But they must tread carefully, ensuring they harness the power of AI responsibly and ethically, always with the customer at heart.

No matter what the future holds, one thing is clear: AI is here to stay, and its impact on customer experience is just beginning. So, whether you’re just starting your AI journey or are already well on your way, now is the time to embrace AI, explore its possibilities, and prepare for a future where AI and customer experience are intrinsically linked.

 

Cognitive automation refers to the use of advanced technologies such as artificial intelligence, machine learning, natural language processing, and robotics to automate routine tasks, analyse data, and provide real-time insights.

Combining the best of AI and automation to mimic human intelligence with the processing power of machines it’s helping organizations streamline and scale operations like never before. Research has shown that cognitive automation leaders operate up to 5-15% higher margins than their peers.

If you’re still wondering what is cognitive automation? Don’t worry. Below, we’ll fill you in on everything you need to know regarding this nascent but revolutionising technology.

Components of Cognitive Automation

Modern-day cognitive automation systems rely on various components that work in unison to unlock their full potential:

Artificial Intelligence (AI) and Machine Learning (ML)

AI refers to machines that can perform tasks normally requiring human intelligence, such as recognising objects and making decisions.

Machine learning is a subset of AI that focuses on developing algorithms that can learn from data and improve performance over time.

Combined, AI and ML are critical in enabling machines to learn from experience to improve decision-making.

Natural Language Processing (NLP)

NLP is a branch of AI that focuses on enabling machines to understand, interpret, and generate human language.

In cognitive automation, NLP enhances machines’ ability to understand and respond to natural language inputs. For example, chatbots use NLP to understand customer queries and respond with relevant information.

NLP can also be used to extract insights from unstructured data, such as customer feedback or social media posts. This enables businesses to gain a deeper understanding of customer sentiment.

Robotic Process Automation (RPA)

RPA is a technology that automates repetitive tasks, such as data entry or generating reports. RPA software can mimic human interactions with digital systems, enabling them to perform tasks quickly and accurately.

In cognitive process automation, RPA can be used to automate tasks that normally require human action, such as data analysis or decision-making. For example, RPA bots can automate the processing of loan applications, where they can analyse financial data and make decisions based on predefined rules.

Applications of Cognitive Automation

Due to rapid advancements and its adaptability, cognitive automation is becoming ubiquitous across all business sectors. Here are some cognitive automation examples of how you can leverage it for your business:

Customer service and support

Cognitive automation can improve customer service and support by enhancing response times and personalisation. For example, chatbots with natural language processing capabilities can engage with customers in real time, answering queries and providing personalised recommendations.

Cognitive automation can also analyse customer data, enabling organisations to gain insights into customer behaviour and preferences so businesses can deliver personalised experiences at scale.

Data analysis and decision-making:

Cognitive automation leverages machine learning and other AI techniques to extract insights from large data sets to enhance or perform data-driven decision-making.

For example, cognitive automation can analyse sales data and identify patterns and trends, enabling organisations to optimise pricing and product offerings.

Businesses can also use it for predictive modelling to forecast future trends and make proactive decisions.

Compliance and risk management:

Cognitive automation can streamline compliance and risk management processes by automating routine tasks and ensuring regulatory adherence. For example, businesses can use it to monitor financial transactions and identify potential fraud, enabling companies to take proactive measures to mitigate risk. Cognitive automation can also be used to automate compliance reporting, ensuring that businesses comply with regulatory requirements.

Supply chain management

Cognitive automation can improve efficiency and accuracy in supply chain operations by automating routine tasks and providing real-time insights into supply chain performance. For example, cognitive automation can optimise inventory levels and manage logistics, enabling businesses to reduce costs and improve delivery times. It can also be deployed for real-time, 24/7 supply chain monitoring, enabling any business to identify and address issues quickly.

A Real-World Example: Cognitive Automation in Banking

To illustrate the practical applications of cognitive automation, let’s explore how the banking sector could implement this technology. The aim would be to streamline processes, enhance decision-making, and improve customer experience. The three most significant aspects to consider are the following:

Transformative Outcomes:

By integrating cognitive automation into industry-wide operations, banking can achieve the following outcomes, which will enhance various aspects of the industry:

  • Automating loan application processing using RPA bots
  • Analysing customer feedback using NLP
  • Predicting customer credit risk using machine learning
  • Enhancing customer support with AI chatbots

The Significant Benefits

Implementing cognitive automation in the banking sector brings numerous advantages, positively impacting efficiency, cost savings, decision-making, and customer satisfaction:

  • Increased efficiency and productivity: Employees can focus on high-value tasks like relationship building, while cognitive automation handles repetitive tasks like loan processing.
  • Cost savings and resource optimisation: Banks can save on labour costs, as they require fewer employees to perform routine daily tasks.
  • Improved decision-making and reduced human errors: Analysing customer data and predicting credit risks with cognitive automation minimises human errors and biases.
  • Enhanced customer experience: AI chatbots can respond to customer queries in real-time, improving customer satisfaction.

The Potential Challenges and Concerns

Despite the numerous benefits, banks should be aware of potential challenges and concerns that may arise during the implementation of cognitive automation. These issues must be carefully considered and addressed to ensure successful integration:

  • Implementation cost: Integrating cognitive automation into existing systems may be initially expensive.
  • Data privacy and security concerns: Handling sensitive customer data may raise privacy and security concerns that must be addressed.
  • Technological unemployment: Automation may lead to job losses for some employees, particularly for those who perform routine tasks.
  • Adoption challenges: Employees will require training and time to adapt to the implementation of new technologies.

By considering both the benefits and issues of implementing cognitive automation, banks can make informed decisions on how to leverage this technology best to optimise their operations and gain a competitive edge.

Overall Benefits of Cognitive Automation

  • Increased efficiency and productivity: Cognitive automation can automate routine tasks, freeing up employees to focus on higher-value tasks. This can lead to increased efficiency and productivity, as well as faster turnaround times for projects and tasks.
  • Cost savings and resource optimisation: Similarly, cognitive automation reduces the need for manual labour and resources, resulting in cost savings and resource optimisation. Redeploying employees to higher-value tasks can also lead to further cost savings and improved resource utilisation.
  • Improved decision-making and reduced human errors: Cognitive automation can analyse large volumes of data and provide real-time insights. This leads to better data-driven decision-making and reduced risk of human errors caused by bias or fatigue.
  • Enhanced customer experience: Cognitive automation can enable personalised interactions and faster response times, leading to a better customer experience. Chatbots equipped with natural language processing capabilities can engage with customers in real-time, so they won’t have to wait for busy support agents.

Conclusion

Cognitive automation is increasingly important in today’s business landscape as companies seek to optimise their operations and gain a competitive edge. By automating routine tasks, analysing data, and providing real-time insights, cognitive AI can increase efficiency, reduce errors, and enhance the customer experience. Embracing cognitive automation early can help any business stay ahead of the curve.

If you want to learn more about how Innovo can help you to leverage Cognitive Automation to improve efficiency and reduce costs in your organisation, contact us today.

The healthcare industry is constantly evolving, and effective process improvement strategies are crucial for healthcare providers to ensure optimal patient care. Below, we’ll discuss various approaches leveraging AI, IA, and machine learning that healthcare providers can adopt to transform the delivery of care in the emergency department to improve customer satisfaction and outcomes and profits and business outcomes.

By implementing these healthcare process examples, providers can ensure that their digital transformation puts them on the right track.

1.     Transform the Delivery of Care in the Emergency Department:

A multi-faceted approach is needed to improve emergency department care delivery and address overcrowding while enhancing the patient experience.

Data-driven decision-making is crucial to effectively allocate resources and optimise workflows, staffing, and leadership expectations. AI algorithms can help by predicting patient acuity as well as trends and patterns in patient care. It can also help formulate strategies to allocate and use resources efficiently, such as staffing and bed availability.

AI-powered chatbots can also assist in triaging patients, directing them to appropriate care based on their symptoms and urgency. This can reduce wait times and help ensure patients receive timely care.

Combined, AI and machine learning will help providers make data-driven decisions to optimise workflows, staffing patterns, and leadership expectations to improve patient experience and outcomes.

2.     Adopt Lean Methodologies for Healthcare Improvement

Adopting lean methodologies is an effective way to drive healthcare improvement by reducing waste and improving care quality. Lean principles can be applied to healthcare delivery to optimise processes and eliminate inefficiencies.

AI, IA, and machine learning can help healthcare providers implement lean principles by automating processes, predicting patient demand, and identifying areas of inefficiency. This will help reduce unnecessary testing or redundant paperwork and wastefully deploying resources.

Similarly, AI-powered chatbots and virtual assistants can assist with administrative tasks, freeing staff to focus on patient care.

Implementing machine learning will help create a continuous learning loop, which is critical to implementing lean methodologies in healthcare. This involves using clinical data to evaluate processes and identify best practices to incorporate them into clinical workflows.

3.     Improve Hospital Patient Flow with Machine Learning

Hospitals can improve patient flow and optimise operations by leveraging machine learning and AI-powered predictive models. AI algorithms can predict patient demand and optimise patient flow across different departments by analysing patient arrival times, diagnoses, and treatments.

Combined with machine learning, care providers will be better able to streamline their operations and efficiently deploy resources in line with patient trends. This helps to reduce patient wait times, minimise staff overtime, and improve patient outcomes and satisfaction.

AI & Machine learning can also be used to identify bottlenecks in the patient flow process and suggest changes to relieve them. This results in smoother care delivery and improved patient experiences.

4.     Prevent Medication Errors

Preventing medication errors is critical to avoid significant financial and patient safety implications of harmful medication errors. A data-driven approach is vital to effectively monitor, prevent, and mitigate adverse drug events (ADEs).

AI, for example, can quickly and accurately cross-check medication to detect and prevent potential negative drug interactions or dosing errors. They can also combine this data with a complete analysis of a patient’s medical history for personalised recommendations or warnings. Prevention is the best cure, allowing providers to take a more proactive approach to avoid these errors.

NLP (natural language processing) can also be combined with IA to capture medication data accurately and automatically without the risk of human data entry errors. Or to provide intelligent, automated medication advice.

All-in-all, these technologies can help providers uphold medication safety protocols, educate patients on medication use, and engage in ongoing training for staff to maintain best practices.

5.     Reduce Unwanted Variation in Healthcare

Healthcare providers can develop strategies to reduce unwanted variation by identifying areas where inconsistency impacts patient outcomes. In turn, this will increase efficiency, minimise waste, and improve patient outcomes.

Machine learning algorithms can analyse clinical data and identify patterns of variation in care delivery, enabling healthcare providers to develop targeted interventions to reduce unwanted variation. Using rule-based AI/ML models trained on best practices, organisations can more easily enforce quality management systems and standardise processes.

Using these same insights, AI-powered chatbots and virtual assistants can assist with administrative tasks and deliver patient advice in a consistent and standardised manner.

This enables providers to measure outcomes, continuously improve processes, and maintain long-term consistency.

6.     Prioritise Process Measures Over Outcome Measures

As a rule of thumb, providers must prioritise tracking process measures instead of solely focusing on outcome measures. This approach lets providers identify the root causes of healthcare system failures and develop strategies to improve service quality and patient outcomes.

Process measures provide valuable insights into how healthcare organisations operate and help ensure that they consistently deliver high-quality care. Prioritising them allows healthcare providers to detect areas for improvement, standardise processes, and reduce unwanted variation.

This can increase efficiency, decrease errors, and improve patient satisfaction. Furthermore, healthcare providers can assess the effectiveness of process improvement initiatives, identify opportunities for further improvement, and continuously refine processes to deliver optimal patient care.

Conclusion

Health care process improvement is critical to delivering high-quality care and improving patient outcomes today. However, it can be difficult to scale, manage, and implement Improvements unilaterally. By leveraging AI, IA, and machine learning technologies, healthcare providers can automate inefficient manual processes and low-level customer interactions, predict or forecast patient trends, personalise care, and gain greater visibility & insight into their processes. Not to mention augment the abilities and knowledge of carers with the power of AI and data analytics.

This will help lead to better outcomes for both the business and patients.

According to estimates from McKinsey, AI investments will drive up to $1.1 trillion in value for the insurance industry annually. The bulk of this value ($888.1 bn) will come from marketing and sales, but it will span all facets, from risk to operations to finance & IT to HR. But what exactly will AI look like for the average insurer, and how can they leverage it to achieve these business outcomes? That’s what we will explore in this blog.

The modern insurance landscape – Why is there a need for AI?

Insurance is one of the largest industries in the world, with a total market cap of $692.33 billion in 2020, projected to reach $1613.34 billion by 2030.

So, any major shifts in the global landscape will have an outsized impact on this industry. And there’s been no shortage of changes since the COVID-19 pandemic.

The number one challenge facing insurance is how highly competitive this industry is. New entrants, digital disruptors, and insurtech startups are making it increasingly important to find ways to get ahead of the competition and gain a competitive advantage.

Furthermore, customer expectations have radically evolved. Customers demand more personalised products and services, faster response times, and more insurer transparency.

At the same time, insurers are processing more data than ever before. Insurers must have the right tools and expertise to extract insights from their data and use it to make informed decisions.

Insurers are also facing growing pressure regarding an increasingly complex global web of regulatory compliance that comes with the risk of fines, reputational damage, and legal issues.

Lastly, cybersecurity threats and risks have proliferated since the pandemic.

All of this leaves insurers thinly stretched and looking for ways to augment their human resources abilities. This is the type of problems AI is particularly adept at helping to solve in the following ways:

  1. Automating processes like claims processing and underwriting can save time and reduce costs.
  2. Data analysis helps insurers identify patterns and trends to make more informed decisions in less time and with greater accuracy.
  3. Risk assessment, by analysing data on past claims and identifying potential risks, insurers can make more accurate predictions and set premiums that reflect the truth.
  4. Fraud detection by analysing patterns and anomalies in claims data.
  5. Customer service by providing personalised recommendations and answering customer queries in real time.

Overall, AI can help insurers improve efficiency, reduce costs, and make more informed decisions, which is why it has become an essential tool in the modern insurance business.

AI trends shaping the future of insurance

As AI technologies continue to improve and mature, AI’s benefits are bound to become increasingly pronounced. We are already seeing major developments in the following areas that will make AI even more valuable to insurers in the future:

  1. Predictive analytics: Machine learning algorithms are increasingly adept at analysing historical data and predicting future events. These models will continue to become more sophisticated, efficient, and accurate as they’re exposed to more data and trained to produce desired results.
  2. Chatbots and virtual assistants: Advancements in NLP (natural language processing), NLG (natural language generation), NLU (natural language understanding), and LLM (large language models) are making these systems increasingly life-like and able to respond intelligently to complex queries.
  3. Telematics: Telematics is the use of technology to monitor driving behaviour, such as speed, acceleration, and braking. In the insurance industry, telematics can offer usage-based insurance (UBI) policies, which allow insurers to price premiums based on the actual driving behaviour of policyholders.
  4. Image recognition: Image recognition technology is used in the insurance industry to automate claims processing. Insurers can use AI algorithms to analyse photos and videos of damage and assess the extent of the damage, which can speed up the claims process and improve accuracy.

While we are already experiencing some of AI’s immense benefits, new developments are always around the corner. There’s no way to predict what new technology may emerge tomorrow that will completely up-end our existing notions such as ChatGPT did in late 2022/early 2023. So, it’s best to get on the train as soon as possible and start developing internal AI maturity in your organisation.

AI in insurance use cases

We’ve already touched on some use cases for AI in insurance. However, here are a few more to provide additional insight or inspiration into how insurers can leverage this technology:

  1. Claims management: Using AI, RPA (robotic process automation), and IoT, insurers can almost completely automate repetitive, standardised, and attention-demanding claims processing. This will also reduce dependence on paper-based systems, which eat 50-80% of premium revenues.
  2. Document digitization: OCR (optical character recognition) and image recognition have drastically improved recently. The ability to rapidly scan a document and digitize its contents will drastically decrease paper-based systems and improve operational efficiency.
  3. Underwriting: Conventional rule-based evaluation and risk engines can’t keep up with today’s scale, complexity, and speed requirements. IoT sensors and OCR tools can help insurers assess assets’ value faster and more accurately. Artificial Intelligence insurance underwriting can also help adjust values over time based on increasingly complex models.
  4. Winning customers with premiums: One example is how new vehicles collect and transmit vast amounts of data. Insurers can leverage this information to offer premiums that are highly personalized and more attractive to individual drivers to beat out the competition.

Conclusion

The impact of AI on the future of insurance is already significant and far-reaching, and there’s no telling to what heights it will skyrocket as these nascent technologies evolve. AI has the potential to transform the way insurers operate by improving efficiency, reducing costs, and providing more personalised products and services to customers.

If you want to learn more about how Innovo can help you to leverage AI to improve efficiency and reduce costs in your organisation, contact us today.

According to the World Health Organisation, the world’s 60 years and older population will roughly double. That will put significant pressure on the aged care industry to scale operations to care for an aging population while upholding care standards and running efficient and profitable organisations. One of the ways service providers can achieve this is by leveraging technologies like automation to work smarter, not harder.

Why Aged Care Providers Need to Look Into Automation

Royal Commission into Aged Care Quality and Safety recently published a report titled Care, Dignity, and Respect. In it, Royal Commissioners Tony Pagone QC and Lynelle Briggs AO call for the fundamental reform of the aged care system. The comprehensive report lays out what this reform might look like with 148 wide-ranging recommendations.

The primary goals of this reform would be:

  • Redesign the client and resident journey
  • Get creative with feedback collection mechanisms
  • Action feedback immediately by building program awareness across the organisation
  • Improve the employee experience

If implemented, the recommendations will force entities involved in aged care to rethink their relationship with technology fundamentally. That is done by leveraging technological advancements and new processes to improve service delivery. In this new era, compliance verification, digital transformation, and automation will be critical for success.

What Problems Are Australia’s Aged Care System Facing

The main underlying challenge facing the aged care system is changing demographics. Specifically, the senior population is growing, both in sheer numbers as well as as a percentage of the overall populace. This is not a unique problem to Australia, but one facing most of the developed world. Rising lifespans also mean that seniors need aged care for a longer timeframe.

The vast majority of aged care funding comes straight from the Australian Government. This includes paying the majority of aged care workers’ salaries. While it is subsidised to an extent, individuals are at least partly responsible for funding their own aged care.

With all this in mind, here are the current problems facing the aged care system as outlined by the report:

  • The difficulty of entering and navigating the system
  • The difficulty of accessing aged care services
  • Access for disadvantaged groups
  • Identifying and addressing sub-standard care and systemic problems

Many of these challenges can be addressed by encouraging aged care digital transformation. Automation, in particular, is one technology implemented in various ways today that can help overcome many of these challenges.

How Automation Can Help Overcome Challenges and Improve Aged Care

Here are some examples of what aged care automation could look like:

Notifications

Automated notifications can help improve service delivery and compliance across the system. For example, automated notifications can facilitate guided onboarding, inductions, and document collection for care recipients and staff. Automated notifications can also prompt staff to complete compliance or care-related tasks.

Self-service Portals

Self-service portals can make accessing aged care faster and more convenient. It gives both patients access to 24/7 portals where they can get information, submit requests, directly engage aged care services, and track their status in the system. It can offer similar benefits to care provider staff to access information or action tasks relevant to the care they provide.

Workflows and Automated Processes

Automation greases the wheels in chained events, speeding up service delivery. For example, imagine a workflow for fulfilling a medical drug request. A patient, or primary care provider, can submit a request via an online portal.

The workflow system will automatically forward the request to the relevant stakeholders that need to approve/deny the request. With a simple click, the approver can allow the workflow to continue, maybe generating or sending a medication request to a supplier. Once the supplier accepts, the system will notify the necessary stakeholders and the patient that the request is approved and the medication is en route.

As you can see, this type of automation removes unnecessary friction at various points, including many manual tasks (like manually typing, sending, and following up on a request approval email), which could delay the process significantly.

Compliance and Governance

Compliance and governance will become increasingly important, particularly as a key part of the Royal Commission’s mandate to root out sub-standard care. Usually, enforcing this is an extremely tedious, frustrating, and time-consuming task. However, automation can also greatly streamline it while improving compliance with standards and governance measures.

Taking the approval workflow example above, automation compliance in aged care can “pre-approve” a request for medication. It can consider factors like the patient’s biodata, status, service plan, medical regiment, etc. It can help the approver by, for example, stating whether a patient is pre-approved based on criteria, whether the case needs further investigation, or whether there are black-and-white grounds for denial.

Similar compliance mechanisms can be built across the system, from how aged care partners interact with each other, their staff, and patients to the actions that can be taken using self-service portals.

What Do These Benefits Mean?

Many of these benefits have wide-ranging positive implications for patients, service providers, staff, and even governing bodies. Automated notifications and self-service portals can significantly unburden HR, for example. It also greatly streamlines many everyday operations which would otherwise require numerous mundane and manual interventions.

One of the positive outcomes of this will be a greater focus on meaningful work, particularly for frontline workers. By saving time usually eaten up by menial back-office tasks, they can focus their time and efforts where it’s needed – on their patients. These improvements will help providers directly achieve some of the report’s recommendations. For example, that care providers should spend at least 200 minutes daily with each resident while improving job satisfaction.

At the same time, by automating repetitive tasks, organisations can scale up and grow without letting their standards slip.

Conclusion

Rarely a technological advancement can help organisations achieve improvements on multiple fronts. If implemented correctly, automation can be a win for service providers, carers, and patients. If you want to learn more about how Intelligent Automation Services can improve your aged care service delivery, contact us today.

As any CIO, CTO, or technology-conscientious business leader is aware; digital transformation has been one of the chief driving forces behind organisational change for the last decade or more. In fact, you’re probably one of the 70% of organisations with a digital transformation strategy or are actively developing one. The drive to remain competitive and stay ahead has reached fever-pitch, with businesses spending $1.5 trillion in digital transformation in 2021.

However, as businesses go deeper and deeper down the rabbit hole, they are beginning to realise that simply investing in the technologies of the day is not enough. To get the maximum benefits from their efforts, businesses need to ensure these technologies are aligned with and explicitly enable their core objectives and future vision. This is where the concept of “business transformation” or “digital business transformation comes in.”

In this article, we’ll explore the different digital and business transformation concepts and how you can prepare your organisation for a total digital business transformation.

What is Digital Transformation?

Digital transformation is the integration of technologies into various areas of the business. Overall, the objective is to improve how you go about your daily operations, whether it’s being more productive, delivering more value to customers, or achieving smarter and more accurate decision-making.

Most business leaders today already realise the importance of digital transformation. It’s the key to continuous improvement, innovation, and competitiveness in an ever-evolving landscape. After all, the importance of digital transformation is hammered home in any business-IT-related seminar, workshop, keynote address, webinar, and podcast. From small, family-owned, brick-and-mortar businesses to border-defying enterprises, digital transformation is imperative for any business today.

When planning a digital transformation, businesses typically start with a “why” statement, such as:

  • Reducing friction within or between business processes
  • Eliminating hidden inefficiencies within specific operations
  • Improving the customer experience
  • Improving productivity or profits

Undoubtedly, technology is a crucial factor underpinning the digital transformation process. Newer technologies are better equipped to take advantage of new infrastructure developments (5G, for example), tackle new challenges, and build on the wisdom of learning from past mistakes. Newer technologies also tend to be more scalable, flexible, intelligent, secure, and capable of meeting the growing expectations of end-users.

Unfortunately, to their detriment, many only see digital transformation through the narrow lens of technology. That means the software and hardware solutions you employ to enhance or replace existing business practices. However, a true change that’s sustainable, long-lasting, and groundbreaking involves more than just installing (and successfully using) technologies. Taking this narrow approach leaves many of the potential benefits of digital transformation on the table.

True digital transformation goes far deeper than just the technologies involved. It requires fundamentally rethinking your relationship with technology, including your company culture, operating models, and how you approach and manage customer relations. It’s coming to terms with the fact that, increasingly, the technologies you use are becoming the business.

Unfortunately, the term “digital transformation” has become so enmeshed with this old way of thinking that perhaps, it’s time to adopt a new term that better encapsulates this rethink of the digital transformation process. That’s where the idea of “business transformation” comes in, which we’ll explore further below.

What is Business Transformation?

Business transformation is, in some ways, an umbrella term of which digital transformation is a crucial component. While digital transformation has conventionally been associated with constantly improving business technologies, business transformation takes a more holistic approach. It’s a “whole-of-business” approach that’s also concerned with ensuring positive outcomes, whether it’s being more competitive, efficient, or delivering more value to customers.

It just so happens that technology is one of the main enablers of these positive outcomes.

Acquiring and implementing technologies is one thing. However, ensuring they are aligned with your business strategy and measurable outcomes is another. Investing money, time, and effort in the most impressive technologies is pointless if it’s done in isolation from your business objectives.

No matter how savvy or well-informed your digital transformation attempts are, the process is typically expensive, and there will be a disruptive transition phase. So, any investment must bear tangible fruit that clearly satisfies your expectations.

This requires an ideological shift from looking at transforming traditional silos individually to leveraging transformation to the benefit of the company as a whole. Consequently, business transformation is not about addressing specific “why’s” or problem statements but realising wholesale business improvements in line with one particular business strategy. For example, becoming a more customer-centric firm, expanding into new or global markets, or switching to agile work methodologies.

In short, while technology is a key (and some would say indispensable) driver of digital business transformation, it should not be its primary motivation. The main motivating factor should always be the positive business outcomes you want to achieve.

Hardware and software, as well as the security, best practices, and compliance-related standards that govern them, are evolving at a relentless pace. To stay ahead, this forces businesses to be more agile, constantly thinking about innovation and positive change instead of maintaining the status quo. This involves a shift in attitude towards change, from viewing it as a scary and disruptive force to one of optimism and constant improvement.

To do this, companies must get comfortable with experimenting and failing, often and quickly. If you can do this without skipping a beat or wallowing in your failures, you’ll be able to quickly iterate through different models and pick the best one to take your digital transformation forward.

This has led many businesses to adopt agile business models or frameworks. While agile methodologies were initially developed in the context of software development, companies across many verticals have realised their usefulness. After all, most businesses today interface with customers through digital products or services.

The “start small and build” approach, combined with rapid and cheap iterations, makes the entire process more manageable. This is particularly true for businesses that are relatively new to digital transformation or managing their own portfolio of digital products and services.

Small-scale, attainable objectives help ensure that efforts align with success criteria and the transformation roadmap. It also makes it possible to clearly showcase results to business stakeholders and more readily acquire additional guidance.

As a final point in its favour, approaching change from the business transformation perspective will help you become more resilient and receptive to change. If adequately embraced, it will instill the necessary people and culture change for a complete business digital transformation.

How to Approach the Digital Business Transformation Process

Understandably, planning and launching a business transformation campaign can seem daunting. To help pave the way to success, you first must ensure that your project has the necessary drive behind it. This means preparing your most important asset, your people, and your mindset for the change process:

  • People & culture change: Ground-level employees, and even customers, need to see the positive impact changes will have on their lives. Business transformation should generate excitement and hope instead of fear and insecurity. At the same time, employees might need time, guidance, training, and resources to adapt to new working models, like agile frameworks.
  • Stakeholder commitment, leadership, and involvement: Stakeholders at all levels need to feel included and invested in the transformation process. There should also be top-down support and leadership to ensure the necessary guidance and resources is always on hand. Everyone needs to understand the “whys,” “how’s,” “when,” and “what’s” so they feel included in the journey and not left behind by it.
  • Determining actual baselining: You can only validate achieving measurable outcomes if you have a benchmark to measure the success of your efforts. This is another area where technology has an important role to play. By using mechanisms like process & task mining, you can quantify the improvements in your business workflows by comparing and analysing performance across the transformation journey.
  • Experiment and adapt: As with all medium-to-long-term business strategy-related undertakings, you rarely lay the golden egg on your first try. It would help if you approached business transformation from a humble and receptive standing. Lessons will be learned, and you need to incorporate newfound wisdom into the rest of your journey. This means not rigorously sticking to a methodology or framework and forcing it if it looks like it’s not working but changing things up when it seems like there are better routes to where you want to go.

 Amongst a limited group of organisations that understand and have developed technology solutions to support Digital Business Transformation is UiPath; providing a raft of technology capabilities that are focused on addressing business challenges and are aligned to the ways in which organisations operate

Conclusion

If you need more convincing, look at your peers. 56% of businesses prioritise digital transformation, and spending in this field is expected to quadruple to $6.8 trillion by 2023, with 87% of leaders envisioning digital transformation to disrupt their industry. This means you must start thinking about digital transformation today to avoid falling victim to disruptive forces.

However, up to 70% of digital transformations still fail simply because organisations lack the proper experience, ambition, and know-how to realise their ambitions. This is where it pays to work with a seasoned partner like Innovo that can guide you on your intelligent automation, tooling and business focused digital transformation journey.

Contact us today to see how our digital transformation services in Australia can help you realise your vision.

Automation and AI are two of the most common buzzwords that come up regarding the digital transformation and modernisation of organisations. However, it’s often hard to equate what high-level explanations of these technologies mean regarding how it affects your day-to-day operations.

To make matters worse, there’s now a new buzzword that businesses need to get familiar with: Intelligent Automation, or IA. It’s no wonder that these technologies are often misunderstood or referred to interchangeably.

While automation aims to make our processes faster and more efficient, AI aims to make them smarter and more valuable. Intelligent Automation is the latest revolution that promises the best of both worlds if organisations can get it right.

What is Automated Intelligence?

Intelligent Automation (IA) is the use of automation technologies, such as artificial intelligence (AI), business process management BPM, and robotic process automation (RPA), to achieve end-to-end business process automation and accelerate digital transformation. The goal is to streamline and scale decision-making within the organisation by freeing up resources and eliminating operation efficiencies.

Simply put, it’s the use of cutting-edge technologies to solve low-level business problems. For example, Machine Learning, Natural Language Processing, Structured Data Interaction, Intelligent document processing, and RPA. Due to its reliance on intelligence-based technologies, IA is also sometimes called cognitive automation.

Typically, five components come together to enable Automated Intelligence:

  • Artificial intelligence (AI)
  • Robotic Process Automation (RPA)
  • Business Process Management (BPM)
  • Tools, and
  • Data

Organisations use IA in a wide range of applications where speed, accuracy, and scalability are of the essence.

Benefits of Automated Intelligence?

  • Reduce operational costs: According to KPMG, organisations can reduce costs by up to 75% by implementing IA. Roland Berger estimated that organisations could reduce operating costs by 40%.
  • Save time: IA frees humans from managing time-consuming and repetitive processes, especially back office tasks. It can reduce the time spent on processes by 40-70%.
  • Reduce the impact of human error: A high volume of repetitive tasks creates the perfect recipe for human error. Automated, rule-based processes are much less error-prone, especially regarding governance and compliance measures.
  • Maximizing the value of business data: Bad data costs businesses as much as $9.7 billion and the economy $3 trillion annually. IA helps companies collect, process, store, reconcile, and analyse data at scale to higher standards.
  • Improving customer experience: By accelerating speed-to-market and data quality, IA helps businesses deliver improved and more valuable customer experiences. In turn, this increases growth and revenue.

What is Artificial Intelligence (AI)?

A simple definition of AI would be a collection of technologies that work together to mimic human intelligence. We typically think of AI in terms of being able to mimic human behaviour.

However, the vast majority of AI systems used by businesses today are deployed to solve specific problems. Such tools are tasked with solving complex problems that would typically require human-level intelligence. The general intelligence needed to mimic human behaviour is not necessarily efficient or desirable in these circumstances.

In theory, there are three things an AI should be able to do:

  • Analyze data and identify patterns
  • Learn from its previous actions or experiences
  • Self-select responses or strategies based on desirable/non-desirable outcomes.

That’s why AI often goes hand-in-hand with machine learning. Machine learning defines the ability of computers/machines to learn to solve a problem without being explicitly programmed to.

AI is generally divided into three categories:

  • Artificial narrow intelligence (ANI) – AI that’s designed to specialise in a specific task or field.
  • Artificial general intelligence (AGI) – AI that can adapt and showcase expertise in various situations, like humans.
  • Artificial superintelligence (ASI) – AI that can master a vast range of specialised disciplines, exceeding human capabilities.

Benefits of AI

As you can see, the benefits of AI deliver many of the same benefits as intelligent automation. AI helps businesses achieve these improvements from a different angle:

  • Enhances decision-making: Thanks to machine learning, AI can surpass human decision-making in specialised areas. AI systems can coordinate data delivery, analyse trends, develop data consistency, provide forecasts, and quantify uncertainties to make accurate decisions.
  • Completes tasks faster: By leveraging the processing power of modern computers, the idea is that AI can learn to tackle complex problems in much less time than a human would. It can also scale problem-solving by multi-tasking complex processes.
  • Improve customer experiences: For example, chatbots combining AI with Natural Language Processing (NLP) can give customers human-like advice or guidance without waiting for a live operator. Endpoint AI can also give customers access to advanced computing fast and on-demand.
  • Minimizing errors: The more complex the problem, the more likely errors will occur. AI minimises errors thanks to fewer data processing mistakes and greater efficiency.

The Difference Between Automated Intelligence and AI FAQ

You probably still have several questions surrounding automation, AI, and IA. Here are some answers to the most frequently asked questions regarding the subject:

What is RPA? Is it the simplest form of automation?

If you’ve done some reading on the subject, you’ve probably also seen RPA. RPA, or robotic processing automation, is software used to create what are essentially software-based robots or bots. They are used to automate simple software tasks and mimic human actions at a basic level.

RPA bots tend to focus on carrying out repetitive, rule-based tasks. An RPA bot typically specialises in carrying out specific tasks using specific technologies.

In most ways, Intelligent Automation is a combination of RPA and AI. AI allows RPA bots to execute more complex processes while using the same ability to carry out human-like actions.

What are automation services?

Also called “service orchestration,” service automation involves coordinating multiple processes to produce the desired result without human action. Service automation is more complex than task automation, as a service can consist of various tasks working in tandem. However, it’s less advanced than intelligent automation because it doesn’t require advanced decision-making.

Still, automated services are nearly ubiquitously used by businesses today to streamline and simplify many everyday activities.

Examples of automated services:

  • Submitting a repair request via a digital portal.
  • Booking a conference room or event space in advance.
  • Ride-sharing or food delivery services.
  • Online streaming platforms, such as Netflix.
  • Automated AirBnB check-ins/check-outs.

Is artificial intelligence part of automation?

No, a system can be automated without using artificial intelligence. However, as mentioned, AI is one of the core components of automated intelligence and can be considered a pre-condition for it.

Intelligent Automation combines the best aspects of automation with those of AI. It gives automated processes the ability to “think” and “learn” to improve their accuracy, efficiency, and value continuously.

“Intelligence” is the operative word that connects AI with IA. Without AI, automation is limited to only the most low-level, mundane tasks. Intelligent automation, on the other hand, leverages the power of AI to solve higher-order problems at scale.

What are examples of AI?

Here are some of the most common examples of how AI is being implemented today:

  • AI-powered media analysis: AI can be used to automatically determine the contents of an image, video, audio, or other media file. It can then automatically tag, sort the media or create metadata regarding it. For example, detecting media that may contain adult content to block it from being uploaded to your site.
  • Medical diagnosis: Specialized AI systems can help doctors diagnose patients by analysing symptoms, blood samples, CT scans, MRIs, etc.
  • Autonomous driving: Self-driving or completely driverless cars are powered by AI systems that can navigate a car based on environmental sensors, GPS information, etc.
  • Fraud: AI is increasingly used in banking and financial services to detect fraud. AI can assess transaction data and compare it to past fraud data to flag suspicious behaviour.
  • Insurance: Assessing risk and calculating rates based on it demands extremely complex calculations dependent on many data points. This is the type of computational exercise AI excels at.
  • Engineering: AI can use past models to validate designs before they are built and tested. For example, specialised AI systems can use information from wind tunnel tests to assess the aerodynamics of a new aeroplane design.

What are intelligent automation examples?

  • Manufacturing: Speed up production by automating repetitive tasks and avoiding human error in daily processes.
  • Insurance: Insurers can use IA to calculate payments, make predictions used to calculate rates, and maintain governance and compliance. IA allows them to use the analyses done by AI to execute these operations at scale for multiple contracts simultaneously. Firms can use this to create automated document generation workflows.
  • Data capture: Intelligent automation can speed up capturing, organising, storing, and assessing huge amounts of data. For example, you can automatically extract remittance and payment data from submitted documents and integrate it into your financial systems.
  • Customer communication management: IA tools can generate personalised and customised content and send it to individual customers. The system can ensure that the right people get the correct information at the right time.

Conclusion

In summary, while IA and AI are two separate disciplines, they are tightly coupled. In some ways, IA is simply techniques and technologies that can be used to scale AI’s decision-making powers to operate under high-volume conditions.

Combining automation and AI can empower businesses by freeing their workforce to deal with strategic challenges. At the same time, it will increase productivity, speed-to-market, and efficiency while reducing costs, time spent on processes, and the risk of human error.

If you are interested in learning more about our Intelligent Automation services, contact us for more information.