Test Data Management (TDM) is essential in software development and testing, ensuring data used in non-production environments is secure, relevant, and compliant with regulations. With the increasing emphasis on data security and privacy, the need to use obfuscated data during testing and development has never been more critical.

Why Use Obfuscated Data?

  1. Data Security: Obfuscation transforms sensitive data into a format unreadable to unauthorised users, minimising data breach risks during testing phases.
  2. Compliance: Regulations such as GDPR, HIPAA, and CCPA mandate stringent data protection measures. Using actual data in test environments can lead to compliance violations. Obfuscated data helps organisations stay compliant by ensuring that sensitive information is not exposed.
  3. Data Integrity: Testing with obfuscated data ensures that data integrity and quality are maintained, helping identify and resolve potential issues without compromising actual data.
  4. Risk Mitigation: By using obfuscated data, organisations can reduce the risk of exposing personally identifiable information (PII) and other sensitive data, which could lead to legal and financial repercussions.

The Challenge

Managing and securing test data is complex and challenging. Even major banks, government agencies, and the largest healthcare providers struggle with this. The complexity arises from the vast amounts of data these organisations handle, the intricacies of ensuring data obfuscation without losing data integrity, and the continuous evolution of compliance regulations. The challenge is compounded by the need to automate these processes while maintaining high-security standards.

A Public Safety Concern

This challenge is not just a technical issue but a genuine public safety concern. Most members of the general public and even many in the industry are unaware of how insecure their data can be in test environments. Data breaches in these sectors can lead to significant financial loss, identity theft, and compromised personal information. The stakes are high, and the consequences of non-compliance and data breaches are severe.

Why Innovo?

Innovo is the leading service company in Australia for data securitisation and data uplift programmes. Our expertise in TDM and data obfuscation ensures your data is secure and compliant with the latest regulations. We offer cutting-edge solutions to automate data masking, subset data effectively, and monitor test environments continuously. Our commitment to data security and compliance makes us the best choice for organisations looking to protect sensitive information and mitigate risks associated with data breaches. Innovo partners closely with Enov8 as we believe this is the most comprehensive and cost-effective ROI solution available in the market.


Incorporating TDM and using obfuscated data in testing and development practices is essential for maintaining data security, ensuring compliance with regulations, and mitigating risks associated with data breaches. Organisations prioritising these practices will be better positioned to protect sensitive information and maintain trust with their customers.

Penalties for Non-Compliance with PII Data Regulations in Australia

Australia imposes severe penalties for non-compliance with PII data regulations, including substantial fines, legal actions, and significant reputational damage. Due to the sensitive nature of the data they handle, non-compliance can be particularly damaging for organisations such as banks and healthcare providers. These sectors are under stringent scrutiny to protect PII, and any breach could lead to loss of customer trust, financial losses, and operational disruptions.

For more detailed information, please refer to Australian legal and regulatory resources on data protection.

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. 

  1. 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. 

  1. 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. 

  1. 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. 

  1. 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. 

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.




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.


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.



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.


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.


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.


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.


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.


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.


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:


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.


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.