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Legacy software testing has always been a challenging task for professionals in the industry. With outdated frameworks and complex coding structures, ensuring the quality and efficiency of these systems has often been a time-consuming and error-prone process. In today’s fast-paced digital landscape, software development and testing teams face immense pressure to deliver high-quality products at an unprecedented pace. Traditional software testing and DevOps methods are no longer sufficient to meet the demands of continuous improvement and frequent software releases in a SaaS powered world. It’s time to embrace the power of AI-driven tools and methods to transform your testing and DevOps workflows. 

With the advent of AI-driven QE solutions like UiPath, the landscape of software testing is rapidly changing. Cutting-edge technologies are revolutionising the way testing is done, providing more accurate results in a fraction of the time. This blog will serve as a professional guide to understanding and implementing AI-driven solutions in legacy software testing, allowing you to stay ahead of the curve in this ever-evolving industry. 

The importance of effective software testing in today’s technology-driven world 

Effective software testing is crucial for business success in today’s fast-paced digital landscape. Ensuring seamless functionality and efficiency is vital with the increasing reliance on SaaS-based systems and frequent releases. Without rigorous testing, organisations risk facing costly errors, security breaches, and reputational damage. Fortunately, AI-driven solutions have revolutionised software testing, offering unprecedented speed, accuracy, and scalability. By embracing these cutting-edge tools, you can not only fortify your legacy software but also pave the way for future innovations. 

Challenges with Legacy Software Testing and DevOps 

Several challenges plague legacy software testing and DevOps. Testing practices reliant on manual testing are a significant bottleneck, being time-consuming, error-prone, and often resulting in delayed releases. Limited test coverage and inadequate test data lead to defects and bugs. Siloed teams and inefficient communication hinder collaboration and productivity. Infrastructure spending is excessive, and resource utilisation could be more efficient. Developers are wasting too much time on manual testing and environment management, taking away from developing new automation. Slow release management processes make it difficult to release software quickly and respond to changing market conditions, removing the advantages of a CI/CD and DevOps culture. 

All of this sounds negative, but it’s the reality in many organisations, large and small. 

How can AI-driven software testing benefit us? 

The combination of AI-driven test creation, self-healing automation, and robust release management practices offers numerous benefits. AI-driven test creation automates test case generation, saving time and effort, and covers a broader range of scenarios and edge cases, resulting in improved test accuracy and reduced false positives for less expended effort. 

Seamless tool integration provides connectivity with test environments and data tools, automates environment spin-up and commissioning, and enables self-healing data refreshes that minimise downtime and errors.  

Self-healing automation scripts free up developers’ time by ~30% to focus on new automation development, reduce manual testing and environment management efforts, and improve overall team productivity and efficiency. Ultimately enabling your software engineering teams to do more for less. 

Robust release management processes streamline releases with automated workflows and approvals, reducing release timelines by up to ~75% with continuous integration and delivery, improving collaboration and communication across teams with clear release visibility. By leveraging these cutting-edge solutions, organisations can transform their testing and release processes, achieving unparalleled efficiency, accuracy, and productivity. 

Benefits of Effective Environment Management and Data Compliance 

Effective environment management and data compliance can bring significant cost benefits to organisations. By optimising resource utilisation, environment management can help reduce infrastructure spending and minimise waste, leading to improved resource allocation. Additionally, data compliance measures such as obfuscating data for testing can ensure data security and privacy, reducing the risk of data breaches and non-compliance penalties. Virtualising data can help minimise infrastructure costs, resulting in a more efficient and cost-effective testing environment. By implementing these measures, organisations can achieve substantial cost savings while maintaining the highest levels of data security and compliance. 

Unlocking continuous testing and improvement 

By harnessing the power of AI-driven testing, organisations can unlock the benefits of continuous testing and feedback, enabling faster detection and resolution of defects and bugs, ultimately leading to improved product quality and reduced risk. As a result, data-driven insights facilitate data-driven decision-making, and AI-driven testing and DevOps enable experimentation and learning, fostering a culture of continuous improvement and innovation. This synergy of continuous testing and improvement empowers teams to iterate and refine their products and processes, driving growth and success in an ever-evolving landscape. 

For software testing professionals, this is an ideal environment to provide real, tangible benefits to an organisation. With AI-driven testing, they can focus on high-value tasks, drive efficiency, and deliver top-notch products that meet the highest standards of quality. By embracing AI-driven testing, software testing professionals can take their skills to the next level and make a real impact on their organisation’s success. 

Unlocking the Value in Your Organisation: 5 Key Steps 

As a Software Testing Professional, you play a critical role in ensuring the quality and reliability of software systems. By following these key steps, you can unlock the full potential of AI-driven testing and DevOps in your organisation, driving efficiency, productivity, and success. 

1. Assess and Identify

Evaluate your current testing and DevOps workflows, identifying areas for improvement and potential AI-driven solutions. As a testing professional, you know how manual testing and processes can be time-consuming and prone to errors. AI-driven testing can help you automate repetitive tasks and focus on high-value testing activities. 

2. Choose AI-Driven Tools

Select AI-driven testing and DevOps tools that align with your needs, such as test creation platforms and automation tools like UiPath. With the right tools, you can streamline your testing processes and improve test coverage and accuracy. 

3. Integrate and Automate

Integrate AI-driven tools with your existing workflows, automating testing, environment spin-up, and data refreshes. Automation can help you reduce testing cycles and improve testing efficiency, allowing you to focus on more complex testing activities. 

4. Monitor and Optimize

Continuously monitor and optimise your AI-driven testing and DevOps workflows, refining processes and improving outcomes. As a testing professional, you know how important it is to continuously improve testing processes and adapt to changing requirements. 

5. Implement Robust Release Management

Streamline release processes with automated workflows and approvals, reducing release timelines and improving collaboration. With robust release management, you can ensure that software releases are timely, reliable, and meet the required quality standards. 

By following these steps, you can unlock the full potential of AI-driven testing and DevOps in your organisation, driving efficiency, productivity, and success. As a Software Testing professional, you can play a key role in leading this transformation and ensuring the quality and reliability of software systems. 

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.

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 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.

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.