Innovo

Introduction

The rapid evolution of cloud-based enterprise applications—such as Oracle, SAP, Salesforce, Microsoft Dynamics, ServiceNow, Workday, and other SaaS platforms—has placed unprecedented demands on IT teams. With frequent software updates, security patches, and new feature releases, organisations struggle to keep pace with ever-changing environments while ensuring reliability, security, and compliance.

Traditional test automation, which relies on script-based testing and predefined test cases, is no longer adequate. These rigid frameworks falter under continuous changes, leading to increased maintenance costs and delayed deployments. The solution? Agentic AI-driven automation, a transformative approach to autonomous, adaptive, and intelligent software testing.

At Innovo, we are at the forefront of this transformation, leveraging our expertise as the only UiPath-certified and vendor-backed provider in Australia to help enterprises revolutionise software delivery with Agentic AI automation.

The Challenges of Keeping Up with Cloud Application Releases

1. High Velocity of Cloud Software Updates

• Cloud platforms like SAP, Oracle, Salesforce, and Microsoft roll out quarterly updates.
• Businesses must rapidly validate new changes to prevent disruptions while maintaining operational efficiency.
• Legacy test automation frameworks struggle to keep pace, leading to broken scripts and high maintenance overhead.

2. Increased Complexity of Enterprise Applications

• Cloud applications interact with on-premise systems, APIs, third-party integrations, and custom configurations.
• End-to-end testing requires understanding application logic dynamically rather than relying on static test cases.
• Traditional automation cannot handle the evolving data models, business processes, and UI elements.

3. Rising Testing Costs & Resource Constraints

• Continuous regression testing demands significant manual effort, leading to higher costs and delays.
• Enterprises must balance speed vs. quality, often compromising one for the other.
• Human testers struggle to scale testing across multiple applications and geographies.

4. Risk of Business Disruption & Compliance Failures

• Delayed testing leads to higher risks of production defects, causing revenue loss and reputational damage.
• Industries such as banking, healthcare, and government require strict compliance with security and data privacy laws.
• Inadequate testing exposes organisations to cybersecurity vulnerabilities, compliance breaches, and costly fines.

How Agentic AI-Driven Automation Solves These Challenges

Unlike traditional automation, Agentic AI-driven testing brings intelligence, adaptability, and scalability to test execution. It introduces:

1. Autonomous Test Execution & Self-Healing Scripts

• AI-driven bots can detect changes in cloud applications and dynamically adjust test cases without manual intervention.
• Unlike traditional scripted testing, Agentic AI adapts to UI changes, API modifications, and business rule updates.
• Example: When Salesforce updates its UI components, Agentic AI automatically recognises new elements, reducing script failures.

2. Continuous Learning & Predictive Intelligence

• Agentic AI learns from past test failures and optimises test execution over time.
• Machine learning algorithms prioritise high-risk test cases, ensuring maximum coverage with minimal effort.
• Example: In an SAP S/4HANA update, Agentic AI can predict which workflows are most likely to break and target them first.

3. Intelligent Decision-Making & Context-Aware Testing

• Unlike static test cases, Agentic AI makes real-time decisions based on application behaviour, user actions, and test outcomes.
• It identifies anomalies and adapts test coverage dynamically, ensuring the most critical areas are validated.
• Example: In Oracle Cloud ERP, AI-driven automation can adjust test cases based on dynamic workflow rules to ensure compliance.

4. Integration with DevOps & Continuous Delivery Pipelines

• Agentic AI integrates with CI/CD pipelines, DevOps tools, and release management systems.
• Automated feedback loops enable faster defect detection, reducing the time-to-market for new releases.
• Example: When Workday releases a payroll update, Agentic AI can validate tax calculations in real time across geographies.

5. Scalability Across Cloud & Hybrid IT Landscapes

• Agentic AI supports testing across multi-cloud, on-premise, and hybrid enterprise architectures.
• Works across ERP, CRM, HR, ITSM, and custom applications, providing end-to-end automation coverage.
• Example: In a large-scale Microsoft Dynamics deployment, Agentic AI can execute tests across multiple regions, tenants, and configurations.

Why Innovo is Leading the Agentic AI Revolution

1. UiPath-Certified & Vendor-Backed Expertise

• Innovo is the only UiPath-certified and vendor-backed service provider in Australia for Agentic AI testing.
• Our exclusive partnership enables us to deliver cutting-edge AI-driven test automation solutions.

2. Proven Success in Large-Scale Enterprise Testing

• We have successfully implemented Agentic AI automation across SAP, Oracle, Salesforce, Microsoft, ServiceNow, Workday, and more.
• Our team brings expertise in test governance, compliance, and cloud application validation.

3. Full-Scale Test Data, Environment & Release Management

• Powered by Enov8, we provide integrated test data management (TDM), environment management, and automated release orchestration.
• Ensures test environments are always available, secure, and aligned with production-like conditions.

4. Cost Reduction & Faster Time-to-Market

• 30-50% cost reduction in software testing by eliminating manual testing inefficiencies.
• 50% faster release cycles, ensuring continuous delivery of high-quality cloud applications.
• 70% improvement in defect detection, reducing production failures and rework.

Conclusion: The Future of Software Testing is Here

Traditional testing methods will become obsolete as businesses migrate to the cloud and adopt agile, DevOps, and AI-powered transformation strategies. Agentic AI-driven automation is the only way forward—enabling enterprises to keep pace with cloud application release cycles, reduce costs, and ensure software quality at scale.

At Innovo, we are driving this revolution. With our UiPath-backed Agentic AI expertise, cloud testing capabilities, and AI-powered automation solutions, we empower businesses to move faster, reduce risk, and stay ahead of change.

Next Steps: Future-Proof Testing with Innovo

We invite CIOs, IT leaders, and software teams to explore how Agentic AI automation can transform software delivery and accelerate digital transformation.

📩 Contact Innovo today for a consultation on how we can help you embrace the future of AI-driven test automation.

Software quality assurance provider, Innovo, has appointed Hamish Leighton as its new CEO, succeeding Nick Finlayson, who has led the company for the past 10 years.

Leighton brings a wealth of experience scaling businesses in the software testing industry, having previously founded and served as COO at Ampion. At the time he was instrumental in driving business growth to over 650 staff and overseeing its subsequent sale to Wipro.

His visionary leadership and deep understanding of industry trends position him ideally to lead Innovo into its next phase of growth, while Finlayson will continue with the business focusing on customer development and sales.

“His strategic insight and proven leadership make him the perfect choice to build on Nick’s legacy and drive the company forward, achieving Innovo’s vision,” Chairperson Paul Thorley said. “Nick will continue in the business focusing on our client relationships and remain on the Board,”

Finlayson has been at the helm of Innovo, overseeing a period of significant expansion and transformation.

“It’s never a straight line, even with all the best laid plans but it’s very satisfying,” he told ARN.  

“It’s good to hand it over and contribute to where the business needs to go, because we think we can double it, if not more, again in a year.”

Under his leadership, the company has scaled from startup, to a recognised front-runner in software testing. It has also strengthened its market position and fostering a culture of excellence and innovation.

“I am incredibly proud of what we have accomplished at Innovo over the past 10 years,” Finlayson said.

“It has been an honor to work alongside such a talented team. When we broke the eight-figure revenue number, I was always focused on bringing in a CEO.

“We started the search six months ago and luckily reconnected with Hamish. I have worked with Hamish many times, both competing and in partnership, over the years, and I am confident that Hamish is the right person to lead the company into its next chapter.”

Finlayson said the business had reached a certain point and the search began for a new CEO to take over and take the business to the next level.

“There comes a time in every founder’s journey when stepping aside for an experienced CEO is the best decision for the business,” he said.

“Hamish’s remarkable track record of scaling and transforming companies makes him the perfect choice to lead Innovo into its next chapter. With Paul Thorley’s strategic guidance as chairman, I am confident Innovo will achieve our ambitious targets”.

Leighton added the opportunity to work at Innovo and with Finlayson again, drew him into the role. He will be focused on creating the building blocks for the company’s next stage of growth.

“I’m fortunately coming in at a really exciting time for Innovo and there’s a significant pipeline of opportunities,” he said. “We’re going to have a shift in focus in the next 12 months and really scale the business quickly. It’s a fantastic challenge.”

Leighton highlighted the business’ depth of technical talent and helping organisations transform their quality assurance work.

“What we’re going to need is increased capacity to deliver. We’ve been growing our depth of talent and new partnerships with vendors that want us to grow and grow capability around their tool sets to help them deliver to clients as well,” he said.

Introduction: The Hidden Bottlenecks Holding You Back 

CIOs are under pressure to release software faster, cut costs, and improve quality—all while staying compliant and secure. The business landscape is evolving in unprecedented ways. Increasing competition, regulatory demands, and consumer expectations compel enterprises to deliver high-quality software faster than ever before.  

In response, many organisations are investing in test automation, agile methodologies, and DevOps pipelines, expecting these investments to immediately impact release speed and software quality.  

Yet, despite these substantial investments, teams still encounter: 

❌ Delayed releases due to unstable test environments 

❌ Flaky automation from unreliable infrastructure 

❌ Developers stretched too thin, expected to build, test, and debug 

❌ Critical defects slipping into production 

The problem? Tools don’t fix broken processes or flawed thinking. 

1. The myth that “Developers Can Handle All the Testing”

🚀 Flawed Thinking: ”If developers write the code, they should test it too. Dedicated testers slow things down.” 

Many organisations see software testing as a cost rather than an asset. Some IT leaders believe developers ought to own everything testing—eliminating the need for dedicated testers. The argument is that: 

• Developers understand the code best and can test it effectively. 

• Automation will replace manual testing, reducing the need for specialist testers. 

• Testing is a bottleneck; removing testers will accelerate delivery. 

🛑 Reality: While developers have the deepest understanding of their code, this can be a weakness in testing. Developers often suffer from confirmation bias, meaning they test based on how they expect the software to behave rather than how it might fail. Dedicated testers bring a fresh perspective, focusing on edge cases, real-world user behaviour, and system-level interactions that developers might overlook. Removing testers increases risk, not efficiency. 

2. The Role of Fully Automated Test Data & Environments, Led by Enov8

🚀 The Emerging Challenge: Environments & Data Bottlenecks Kill Testing 

Most organisations struggle with test data and environment bottlenecks, leading to: 

❌ Delays in building stable test environments—teams waste days waiting for setup. 
❌ Unreliable automation due to missing or incorrect test data—tests fail for misleading reasons. 
❌ Compliance risks—GDPR, CCPA, and regulatory constraints impede the use of production data. 
❌ Unrealistic testing conditions—lower environments do not align with production. 

🛑 The Reality: Traditional test data and environments hinder your progress. Test automation is only as effective as the data and environments that support it. This is why leading organisations automate their test data and environment management from the ground up. 

💡 How Enov8 Is Building a Fully Automated Solution for Data & Environments 

Through automation, Enov8 is revolutionising Test Data & Environment Management by providing: 

✔ Comprehensive Observability: Gain deep insights into your IT landscape through advanced modelling, architectural analysis, and operational intelligence, which facilitates proactive issue resolution and informed decision-making. 

✔ Streamlined Environment Planning & Coordination: Employ centralised governance to effectively plan, coordinate, and standardise all testing environment and deployment activities, minimising setup times and preventing resource contention. 

✔ Automation-Driven Efficiency: Integrate demand management with your toolchain to automate deployment processes, standardise operational procedures, and boost productivity across development and testing teams.  

✔ Simplified Test Data Management: Implement strong solutions for test data masking, synthetic data generation, and provisioning, ensuring compliance with data protection regulations while reducing the complexity and costs associated with test data management.  

3. Risk-Based Testing: Not Every Bug Matters

🚀 Flawed Thinking: “We need to catch every bug before release.” 

Many organisations believe that testing aims to identify every single bug—which is neither realistic nor necessary. The assumption is: 

• The software should be completely defect-free before going live. 

• More testing always equals better quality. 

• The cost of post-release defects is always greater than the cost of exhaustive pre-release testing. 

🛑 Reality: Not all bugs are equal. Testing is about assessing business risk, compliance, and user impact—not finding every minor issue. Risk-based testing helps teams: 

✔ Collaborate with the business to understand the most critical functions and workflows.
✔ Prioritise testing efforts based on business impact, compliance requirements, and known risk areas.
✔ Accept that some defects will exist, but ensure they are either low-impact or well-documented.
✔ Use AI-driven automation (like UiPath) to continuously adapt and improve test coverage based on real-world usage. 

4. The Future: AI-Driven Testing with UiPath

🚀 The Next Evolution in Test Automation: Self-Learning AI 

Traditional test automation relies on rigid scripts, so if the application changes, tests fail. Agentic AI testing eliminates this fragility by dynamically learning from user interactions and self-healing test cases as systems evolve.  

A new wave of AI innovation powers this shift: Agentic AI. Agentic AI refers to self-learning AI that autonomously adapts test cases based on real user interactions, eliminating the fragility of traditional automation. 

✔ Learning dynamically from real user behaviour 

✔ Auto-adjusting test cases as software evolves 

✔ Predicting failures before they happen 

🛑 The Reality: AI-Powered Test Automation is a Game Changer 

Most test automation today relies on pre-scripted logic—meaning: 

• If a UI button changes, the test breaks. 

• If a workflow is modified, tests must be manually updated. 

• If new failure scenarios emerge, traditional test automation doesn’t detect them. 

Agentic AI testing changes this.  

💡 Why UiPath Leads in Agentic AI Testing: 

✔ UiPath utilises AI-driven object recognition and process mining to enhance test resilience.   

✔ Models based on machine learning anticipate test failures before they occur.   

✔ Agentic AI bots analyse user journeys to identify patterns that automatically generate and optimise test cases. 

UiPath is at the forefront of this shift, enabling AI-driven test automation that continuously adapts to system changes in real-time. 

5. How Innovo Leads the Cultural Shift While Empowering Tool Use

🚀 Flawed Thinking: “Buying the best tools improves software quality.” 

This belief leads to: 

❌ Over-investment in tools without fixing underlying cultural issues
❌ Automation initiatives failing due to a lack of strategy and leadership
❌ Siloed teams where developers, testers, and ops don’t collaborate effectively 

🛑 Reality: Tools fail without cultural change. Successful companies don’t just buy tools—they enable cultural and process-driven change by: 

✔ Align testing with business objectives 

✔ Foster collaboration between Dev, QA, and Ops 

✔ Optimize processes before automating them 

💡 How Innovo Empowers Organisations to Succeed in Risk-Based Testing 

✔ Strategic Consulting: Innovo assists organisations in transitioning to a risk-based testing model. 
✔ Cultural Transformation: We collaborate with leadership teams to align testing with business objectives. 
✔ Process Optimisation: Ensuring that testing efforts concentrate on what truly matters. 
✔ Technology Enablement: Guiding teams in utilising automation to mitigate risk, not merely detect defects. 

🚀 The bottom line? 

Testing isn’t about perfection—it’s about delivering the right software with the right trade-offs at the right time. 

📩 Ready to transform your testing strategy? 🚀 Let’s discuss how Innovo can help you build faster, smarter, and more resilient software. 

In modern software development, speed and efficiency are no longer nice-to-haves; they’re essentials. DevOps has helped bridge the gap between development and operations teams, making delivery faster and smoother. However, one sticking point continues to hold teams back: managing and provisioning data environments.

Traditional methods of copying and preparing data are slow, clunky, and expensive. This is where data virtualisation comes into play, offering a smarter, faster, and more efficient way to handle data environments. Let’s explore the challenges and how data virtualisation is changing the game.

The Problem: Data Management Slowing Everything Down

Despite all the advances in development tools and processes, data management is still a major bottleneck. Creating environments with realistic data for development, testing, or troubleshooting is often a painful process. The delays and inefficiencies that come with traditional methods can have a knock-on effect on the entire DevOps pipeline.

Common Challenges:

1. Time-Draining Processes
   – Copying full databases can take hours or even days, leaving teams waiting around for environments to be ready.
   – Manual steps often add more delays and increase the chance of errors.
2. Huge Storage Demands
   – Every full copy of a dataset requires significant storage space, which quickly becomes unsustainable.
   – Scaling up to support multiple teams or projects pushes costs through the roof.
3. Outdated or Inconsistent Data
   – Datasets often fall behind production, leading to inconsistencies between environments.
   – Testing on outdated data can cause issues to slip through unnoticed.
4. Contention for Resources
   – Shared or locked resources create bottlenecks, slowing down workflows and increasing frustration among teams.

Secure and Efficient

Database Virtualization provides a fast and efficient way to create and manage data environments. By leveraging lightweight virtual clones, teams—or even individual developers—can quickly spin up their own environments without waiting for centralized resources or risking contention with others.

This approach ensures that:

– Data environments are provisioned quickly and independently: Teams or individuals can create environments as needed, avoiding delays caused by bottlenecks.
– Storage requirements are minimized: Virtual clones require significantly less storage, enabling cost-effective scaling.
– Teams can work in parallel: Developers can work without concerns over shared or locked resources, increasing productivity.
– Autonomy for developers: Individuals can access the data they need when they need it, streamlining workflows and eliminating dependencies on centralized processes.

The Solution: Data Virtualisation

Data virtualisation offers a fresh approach to these challenges by changing how data is accessed and managed. Instead of relying on full physical copies of a dataset, virtualisation uses lightweight snapshots and clones that behave just like the real thing. These virtual environments are quick to create, take up very little space, and can be tailored to meet specific needs.

How Data Virtualisation Helps:

1. Lightning-Fast Provisioning
   – Environments can be spun up in seconds, not hours.
   – Teams or even individual developers can create their own environments on demand, avoiding contention.
2. Minimal Storage Requirements
   – Virtual clones take up a fraction of the space that traditional copies would need.
   – Organisations can support multiple environments cost-effectively.
3. Automation-Ready
   – Most data virtualisation tools integrate easily with DevOps pipelines, CI/CD systems, and automation tools.
   – Environments can be provisioned automatically, ensuring they’re always ready when needed.
4. Always Up-to-Date
   – Real-time snapshots ensure data environments reflect the latest state of production.
   – Fewer bugs arise from mismatched or stale data.

What It Means for DevOps

The benefits of Database Virtualisation ripple through the entire DevOps process and integrate seamlessly with essential Test Data Management (TDM) tools like Security Profiling and Masking. Together, these technologies create a streamlined and secure workflow for managing data environments.

Key advantages include:

– Faster Development Cycles: Developers can quickly spin up their own test environments, reducing wait times and eliminating bottlenecks.
– More Frequent Testing: Testers can run cycles earlier and more often, catching issues sooner with realistic, production-like datasets.
– Improved Security: When paired with TDM tools, data can be profiled and masked to ensure sensitive information is protected in every environment.
– Smoother Feedback Loops: Continuous testing and deployment pipelines become more efficient, accelerating delivery.
– Cost Savings: Reduced storage needs and optimised workflows free up budgets for other priorities.

Real-World Example

Imagine a project where multiple teams are working on different features simultaneously. Each team needs its own database environment for testing and debugging. Traditional methods could take days to set up those environments, with storage costs piling up. With Database Virtualisation, those environments can be provisioned in seconds, using a fraction of the storage space, keeping everyone moving and on track.

Why It Matters

Data management is often the unsung hero or villain of the DevOps lifecycle. Get it right, and everything moves faster and smoother. Get it wrong, and the delays can grind progress to a halt. Data virtualisation offers a modern, efficient solution to these long-standing problems, helping organisations work smarter, not harder.

By removing bottlenecks, cutting costs, and ensuring seamless integration with Test Data Management tools, data virtualisation enables teams to focus on what really matters: building and delivering great software. For organisations aiming to stay competitive in today’s fast-paced market, it’s a no-brainer.

Ready to Take the Leap?

If your current approach to data management feels like it’s holding you back, it’s time to rethink your strategy. Data virtualisation is more than just a technology shift—it’s a way to unlock speed, agility, and efficiency in your DevOps processes. Isn’t it time you left the old bottlenecks behind?

Introduction

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.

Conclusion

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.

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.

2. 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.

3. 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.

4. 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.

5. Measurement and Improvement

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

Challenges

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

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

Automation is a central component of most organisations’ digital transformation strategy today. While stakeholders at all levels are eager to reap the benefits of automation, synchronising, collaborating, and managing automation across all parallel automation initiatives is a significant challenge. If that sounds familiar, then your next step should be utilising an automation hub to manage the automation pipeline.

The case for business automation

Most business leaders today recognise that business automation, in all of its many forms, is not just a buzzword. In fact, it’s a genuine and influential part of digital transformation that helps businesses be more agile, faster, profitable, and competitive.

For many businesses, robotic process automation (RPA) is the embodiment of the promise of automation. RPA is the process of building, implementing, and deploying software bots that emulate human actions to automate repetitive, manual processes. However, other types of more advanced automation, especially intelligent automation (IA), are also gaining ground.

Most businesses today recognise the potential of automation. At least 53% have already started their RPA journey, and if the current trend continues, RPA adoption will be near-universal within five years. It’s easy to see why top performers in the implementation of RPA have seen a 4x ROI from their RPA investments, while the average business has experienced a ~2x ROI. What’s more, RPA benefits are most commonly felt in challenging yet increasingly vital business areas, such as compliance (38%) and productivity (27%).

It’s no wonder the potential economic impact of knowledge work automation is expected to be $5-7 trillion by 2025.

RPA, and other forms of automation, are vital because they help businesses operate more efficiently and do less with more. It’s estimated that 10-25% of employees spend their time on repetitive computer tasks, while IT departments spend 30% of their time on basic, low-level tasks. As a concrete example, businesses typically pay $5- $25 for manual invoice processing.

That’s a lot of waste in terms of time, money, and human resources. Today, 70-80% of these mundane, rule-based processes can be automated with trivial ease. As you can see, the problem lies less with being able to implement RPA solutions than with identifying the myriad opportunities for automation and managing the automation journey.

The challenge of affecting digital transformation through automation

The main problem for organisations looking to implement automation as part of their digital transformation is how broad it is. Most organisations have tens, if not hundreds, of potential use cases for RPA or other forms of BPA. Some are confined to specific teams or departments, while others are organisation-wide. Others may pertain only to a single enterprise app, while others span an entire tech stack.

There are so many types of business automation that it’s hard to track them all. For example, marketing automation, HR automation, accounting automation, process automation, and manufacturing automation, to name a few.

With so many potential opportunities for automation, the immediate problem is how to find and define automation use cases. This can be particularly challenging in large organisations with complex employee and managerial hierarchies. Understandably, C-suite executives may not be savvy to the immediate automation needs of ground-level manufacturing staff, for example, and vice versa.

So, the first problem to solve is the ability to create automation requests from both a top-down and bottom-up approach.

However, even when you identify a specific use case for automation, that opens up a whole other can of worms:

Where do you begin to implement all of these automation processes? How do you track your progress across all automation processes? How do you make sure all the relevant stakeholders are involved? How do you ensure compliance and collaboration to implement them efficiently and with minimal disruption? And, more importantly, how do you determine whether a specific automation process was successful?

Depending on the organisation’s size, digital maturity, and internal automation expertise, the lack of oversight can be a significant stumbling block on the road to large-scale automation. Clearly, organisations need some platform to centralise and manage the entire automation journey.

What is an automation hub?

The goal of an automation hub is to help streamline and accelerate the adoption of process automation initiatives. It facilitates every step of the journey of adopting a new automated process, from ideation to planning to implementation to validation. It allows all stakeholders, including end users, automation experts, developers, and decision-makers, to collaborate on the automation process.

We’ll cover everything that an automation hub does in-depth below, but it broadly consists of three main facets:

• Collaborative process identification
• Automation pipeline management
• Process repository tools

In a way, these tools and features allow the automation of the automation adoption process. As the name suggests, it’s a one-stop, centralised, collaborative “hub” for managing all automation processes. It’s a single point of contact between all individuals involved in creating and using new automation initiatives. This includes business users (e.g., employees), developers, project managers, CoE (Center of Excellence) teams, and automation experts.

What does an automation hub do?

By looking at its actual capabilities, it may be easier to understand precisely what an automation hub does. While the range and depth of capabilities may differ from vendor to vendor and system to system, they generally offer the following essentials:

• Process Identification: Allows users (managers, employees, dedicated automation staff) to share ideas or requests for automation via a collaborative process.

• Pipeline Management: Map the entire roadmap and plan for a specific automation process. Define the expected cost, lead time, and benefits and track its progress in a single dashboard.

• Process & Documentation Bank: Store all documents and assets related to a specific automation process candidate in a single, centralised repository.

• Integration with Task Capture: Task capture tools streamline and improve communication between your automation experts and RPA developers. Automation or subject matter experts can specify best practices and suggestions as a process definition document or workflow diagram for developers to implement.

• Marketplace: Establish an organisation-wide marketplace of reusable components in a private repository. Users can create, store, find, and install components on an as-needed basis at any place, any time, anywhere.

• Gamified RPA Change Management: Support and motivate team members through the automation process using long-term engagement strategies. Clearly track progress and keep the focus on the next and most important milestones in the automation journey. Some gamification systems go as far as to allow you to implement points, badges, or ranking systems.

One important facet of most automation hub systems is that they encourage and facilitate collaboration through every step of the process. Users can share automation requests or ideas, action them, and provide feedback through voting systems or open-ended feedback mechanisms. The automation hub also maintains a single source of truth for all assets and information related to the automation process so that everyone is on the same page.

What are the benefits of an automation hub?

Everyone involved in the use, implementation, deployment, or effects of automated processes stands to benefit from the use of automation hubs:

• Business users:
  • Discover, submit, and collaborate on automation ideas
  • Manage automation opportunities in one place
  • Share subject matter expertise to inform the automation process
  • Increase ROI with automation process prioritisation
  • Maximise the impact of internally developed automations
  • Leverage existing automations

• CoE (Center of Excellence) leaders:
  • Identify what to automate as well as why, when, and how to automate it
  • Manage automation process pipelines
  • Ensure that automation is successful according to pre-determined metrics
  • Prove the positive impact of automation to leadership

• Citizen developers:
  • Build automation with clear specifications in mind
  • Easily build and share personal time-saving automations
  • Ensure that you build CoE-approved automations
  • Easily kick off, manage, collaborate on, and deploy automation projects

The net result will be fewer headaches for your organisation when implementing automation initiatives. Not to mention deploying automation solutions that are fit for purpose and meet the objectives of your boots-on-the-ground employees and high-level decision-makers.

One of the greatest benefits of an Automation hub is the capability to store and re-use assets. Whilst this especially beneficial for multi stream test activities such as regression testing across multiple teams/projects, further benefit and efficiency gains can be obtained through the re-use of these assets in areas outside of traditional test activities.

These can include Robotic Process Automation (RPA), DevOps and environment operations, data generation and validation, and standards conformance validation to name some examples. The ability to store and re-use these assets can contribute to not only overall effort reduction but lead to significant cost savings as well as quality increase through standardisation. In addition, the storage of such assets can also enable greater velocity, in effect providing a mechanism enabling a ‘test on demand’ function.

The test automation tools market to date has been focussed on test automation in isolation, however with greater emphasis being placed on interoperability and re-use of assets to drive automation as an outcome, tools vendors are researching and developing mechanisms to enable holistic automation capabilities.

Traditionally this has been focussed at the integration layer, enabling diverse tools to ‘talk’ to each other, however UiPath (a recognised leader in RPA) has taken this further by focussing on Intelligent Automation as a whole of business enabler.

This has led to the market leading development of an integrated tool suite that enables Automation hubs to develop and share assets across activity streams – whether that is RPA, testing, or other forms of intelligent automation such as Intelligent Document Processing, Process mining and Task Capture. As UiPath state, ““Bill Gates used to talk at Microsoft about a computer in every home. I want a robot for every person.” Daniel Dines, CEO

Organisation Automation Potential

While the potential of implementing organisation-wide automation is nearly infinite, substantial obstacles exist. The best way to improve your chances of success is to rely on a proven digital transformation partner with experience in automation. This is especially true if you want to take automation to the next level by evolving toward intelligent automation.

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

Businesses are always looking for ways to become more scalable, agile, productive, and competitive in a rapidly-evolving landscape. AI and automation have long been effective strategies to achieve those goals in the tech-savvy business playbook. However, treating them as separate entities limits their benefits to specific silos in the business. Businesses can achieve holistic improvements at every level by combining them and leveraging the power of intelligent business process automation. It’s no wonder that the Intelligent Process Automation market is expected to grow from $13.6 billion in 2022 to $25.9 billion in 2027.

Is automation part of digital transformation?

In short, yes. Automation is a core component of digital transformation. In fact, for many businesses, automation is the first and most vital component with the potential to impact everyday business operations positively. Automation also allows firms to extract more value from sophisticated digital transformation technologies, like AI, machine learning, and the cloud.

There are many different types of automation with varying degrees of sophistication. RPA (robotic process automation) is one of the most popular today. RPA automates repetitive and mundane tasks, such as invoice processing, onboarding, or data processing. Automating these processes is more efficient, frees employees to focus on strategic tasks, and eliminates errors.

RPA typically relies on “bots,” specialised software systems developed to solve specific human tasks. While it’s similar, RPA is distinct from BPA in several ways. BPA, or Business Process Automation, is another sub-field of automation that we’ll discuss further below.

Many other forms of automation are used in businesses today that permeate almost every department, task, or business process. For example, task automation, marketing automation, process automation, integration automation, AI automation, and more.

What are the main benefits of automation?

As mentioned, automation is a broad field. Different automation applications will lead to varying benefits for specific businesses, teams, departments, or stakeholders. However, in general, you can expect the following benefits from utilising automation:

• Lower operating costs
• Improved worker safety
• Smaller lead times
• Faster ROIs
• Increased productivity or output
• Improved quality of products and services
• Reduced employee burnout and higher happiness
• Maximising your employees’ potential
• Better adherence to governance and compliance
• Improved service reliability and consistency

In general, automation allows businesses to do more with less by eliminating the need for superfluous manual work. That means that employees have more time and energy to spend on strategic problem-solving and core business functions. Automated software systems can also execute certain tasks with greater speed, accuracy, and scalability than humans. For example, automated provisioning or manufacturing systems can run 24/7, not just within business hours.

What is business process automation?

Business process automation uses automation technology to complete business processes with minimal human intervention. It’s distinct from task or RPA automation because it’s not focused solely on a single task, team, or department. Business processes can span multiple departments through complex workflows that achieve a particular business goal.

For example, BPA can involve the end-to-end process of creating and releasing a new product. In this case, as soon as the product team comes up with a new idea, they can kick off a process that sends the idea to marketing or management for approval. From there, the system will automatically initiate a new development project and inform the relevant parties. The process can then go through a complex series of development -> test -> feedback -> rework according to the business’ production lifecycle.

BPA is also used in applications for HR and employee onboarding, marketing customer onboarding, and IT and technical support. Many of these processes rely on routing documents or approval steps through multiple steps in a pre-defined workflow. In turn, BPA can kick off and rely on multiple RPA systems that handle specific tasks along the way.

What are the benefits of business process automation?

The benefits of business process automation are similar to those of automation in general. However, there is a greater focus on standardising key business process that simplifies and streamlines complex internal workflows. The benefits may be harder to quantify directly because they improve the organisation’s overall efficiency instead of improving metrics for a specific task.

That being said, here are some of the key benefits of implementing business process automation:

• Saving time
• Increased profits
• Higher productivity
• Improved efficiency
• Minimising occurrence of errors
• Improved governance and compliance through standardisation
• Improved internal auditing
• Enhanced customer experiences
• Scalability

What is intelligence process automation?

Intelligent process automation (IPA) involves integrating multiple technologies to automate complete, end-to-end business processes. In some ways, it combines RPA and BPA with another distinct AI field, called intelligent automation (IA).

IA combines automation (RPA, BPA) with intelligence-based technologies, like AI and machine learning. Today, IA is also evolving to span cutting-edge AI fields, such as natural language processing and computer vision, to execute more sophisticated and specialised tasks. IPA goes one process further by applying IA to end-to-end business processes, as discussed above under BPA.

To see why intelligent business process automation is needed, let’s look at a typical invoice processing example. It’s easy to imagine a BPA workflow that eventually routes an incoming invoice through the relevant parties to authorise a payment. However, at some point, someone will still need to manually review the invoice for compliance and ensure it’s error-free.

An intelligent business process automation system would include an intelligence-driven system capable of automating the invoice checking process. So, a business would enjoy the benefits of both a conventional BPA solution as well as an AI solution.

In a way, IPA represents what’s currently the peak of combining intelligence with automation to improve business value. It brings together many different components, including:

• Artificial intelligence: Complex algorithms combined with machine learning to analyse data, predict outcomes and accelerate decision-making.
• Business process management: Automating workflows to improve efficiency, standardisation, flexibility, and scalability within an organisation.
• RPA: The use of specialised software tools to complete back-office tasks in an efficient and scalable way.

Applications for IA exist in nearly every industry or business. However, it’s currently being heavily implemented in fields such as automotive manufacturing, healthcare, and insurance.

What are the benefits of intelligent automation for businesses?

In some way, IPA has the potential to deliver the benefits of AI and automation at every level of the business. From mundane, repetitive back-office tasks to company-wide processes that involve logistics, everyday operations, production, and service delivery. With that in mind, here are the benefits of intelligent business process automation:

• Increased ROI: The combination of automation and IA allows businesses to scale production without negatively impacting quality or risk. Complex rule-based systems ensure compliance and accuracy, reducing the need for internal auditing of systems and processes. In short, this equals a higher yield while investing less time, money, and resources.
• Accurate decision-making: AI is maturing rapidly and is capable of increasingly complex decision-making in shorter timeframes and with less chance of error than humans. Combined with machine learning, AI systems can improve accuracy over time without manual intervention. The scalability of the cloud has the potential to exponentially increase businesses’ computing capabilities, allowing companies to get more value from their data faster. 
• Enhanced customer experiences: Endpoint AI can deliver the advantages of intelligent computing to customers and clients from close proximity. By bringing down lead times and increasing an organisation’s ability to analyse complex behavioural data, companies can also deliver more valuable and relevant products in less time.
• Compliance and governance: Due to regulatory pressures and heightened public awareness, businesses are under more pressure than ever to enforce proper compliance and governance, especially concerning sensitive data. However, it’s also becoming more important from a productivity standpoint as processes and technologies become more complex. AI has the ability to apply governance and compliance measures consistently without sacrificing efficiency.

According to a study by McKinsey, companies that implement IPA experience some pretty radical results:

• The ability to automate 50 to 70% of all tasks,
• Translating to annual run-rate cost efficiency by 30 to 35%,
• Reducing straight-through processing times by 50 to 60%,
• Leading to triple-digit returns on investments.

Conclusion

So, is intelligent automation necessary for digital transformation? Both the short and the long answer to this question is yes.

In one stroke, intelligent business process automation allows businesses to eliminate the inefficiencies associated with replicable, routine tasks as well as streamline and optimise their core business processes.

It’s quickly supplanting its predecessors, AI, RPA, BPA, and IA, as the way for businesses to revolutionise their operations by eliminating inefficiencies, attaining scalability and agility, driving customer value, and out-pacing their competitors.

The only catch is that implementing widespread IA at every level of business can be a real challenge. Many organisations don’t have the specialised AI expertise required to pull off this kind of digital transformation while addressing the business’ chief challenges and objectives.

Enlisting the assistance of an expert intelligent automation service provider, like Innovotech, will benefit a forward-looking business through accelerated uptake and a smooth implementation process. Not to mention consistent and goal-oriented outcomes thanks to the high-quality design & delivery solutions.

Regardless of what vertical or the scale businesses operate in, they are always looking for ways to be leaner and more efficient. The art of producing more from less is the key to unlocking innovation, agility, scalability, and the ability to provide more value from the same products and services.

Automation has long been a goal of businesses looking to save costs, increase productivity, and accelerate growth. Where past efforts have focused on industrial automation, we have now well and truly entered the age of intelligent automation.

Today, automation is not only imperative in low-level applications but also for a company’s overall high-level strategic goals. Intelligent automation enables businesses to achieve greater levels of excellence by improving their decision-making, speed-to-market, customer experiences, and the capabilities of their employees.

The five components of intelligent automation influence every facet of the business:

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

Below, we’ll discuss precisely the five components of IA and how Intelligent Automation can help businesses adapt.

What is Intelligent Automation?

Not to be confused with artificial intelligence (AI), intelligent automation (IA) combines cutting-edge technologies to automate low-level tasks within a business context.

However, IA relies heavily on AI-based technologies, such as Machine Learning, Natural Language Processing, Structured Data Interaction, Intelligent document processing, and RPA. Because it relies on AI, IA is also sometimes called cognitive intelligence.

Businesses across various industries benefit from intelligent automation, including banking, finance, insurance, utilities, and more. IA is a vital thread of a larger digital transformation strategy for many businesses today.

IA systems can be as simple as intelligent chatbots or as complex as provisioning engines that commission/de-commission virtual environments on-the-fly based on real-time workload data.

Note that we’re focusing on Intelligent Automation here as a distinct discipline from robotic process automation (RPA) or hyper-automation. We’ll cover the details of how they differ in-depth in future articles.

For now, it’s enough to know that RPA is a sub-field of IA, alongside artificial intelligence and machine learning. Hyper-automation, on the other hand, is concerned with automating as many business and IT processes as possible. So, IA can be used to achieve hyper-automation of a company.

Hyper-automation holistically tackles the challenge of automation by also looking at it from a human and business process perspective, while IA is concerned chiefly with the implementation of automation technologies.

What are the Benefits of Intelligent Automation?

When intelligent automation is appropriately implemented, its benefits are felt across every area and level of the business. At its core, intelligent automation empowers your human resources with smart technologies and agile processes to make faster and more informed decisions.

• Reduce operational costs: According to KPMG, IA can help financial organisations cut costs by as much as 75%. Roland Berger found that companies implementing IA could save up to 40% annually and reduce the time spent on processes by 40-70%. IA technologies help optimise processes so that they can be scaled for smaller teams or more clients.
• Save time: Optimised and automated processes require less manual human interaction to function. RPA can also be used to completely automate specific repetitive, back-office tasks to free up employees to solve more strategic problems.
• Reduce the impact of human error: Repetitive, high-volume tasks create prime conditions for human errors to pop up. Human error is also especially prevalent in production and invoicing, two critical areas. Automated, rule-based processes eliminate human error risk while strictly enforcing built-in governance and compliance measures.
• Maximising the value of business data: IBM estimates that bad data costs individual businesses $9.7 million annually, costing the economy $3 trillion in total. IA has the potential to help companies to improve how they collect, process, store, reconcile, and analyse their data.
• Improving customer experience: Collectively, IA technologies enhance customer experiences by improving speed to market and allowing businesses to improve their products and services by better understanding their customers. High customer satisfaction means higher retention rates which means growth.

With a longer-term strategy in place, IA also holds potential benefits for employee morale, innovation, fraud monitoring and detection, and even cybersecurity.

The Five Components of Intelligent Automation

Now that we have a grasp on the role that IA plays in modern-day business let’s look at the nuts and bolts. Each company may (and should) have a tailored intelligent automation strategy that prioritises components based on their unique circumstances. However, these are the core five elements that make up a holistic approach to intelligent automation:

Artificial Intelligence

Businesses want to make better decisions, based on more information, within less time and with high accuracy. There is no better candidate to achieve that goal than through the use of Artificial Intelligence or AI. You can think of artificial intelligence as the brain and beating heart behind intelligent automation.

So, in this context, AI is concerned with tasks that are cognitive in nature, such as Optical Character Recognition (OCR), Natural Language Processing (NLP), Machine Learning (ML), and Intelligent Character Recognition (ICR)

AI is the cornerstone of scaling and extracting the maximum value from intelligent automation in a business. By combining complex algorithms with machine learning, companies can efficiently analyse structured and unstructured data at previously impossible scales.

As AI matures, it will be used to process data in increasingly complex and efficient ways. The result is more valuable, actionable, and timely access to information decision-makers can use. This is often what’s referred to as the AI “decision engine.”

Over time, this allows businesses to develop a knowledge base that can be a valuable resource for understanding past events and making future predictions. And, to produce formulas for accelerating the decision-making process even further.

AI is also not a one-trick pony but can be utilised within individual processes, teams, departments, or an entire organisation. AI not only has the potential to revolutionise business processes but also to improve the quality of life and productivity of employees and the value provided via customer experiences.

Robotic Process Automation (RPA)

If AI is the mind, then robotic process automation, or RPA, is the body. Unlike AI that’s primarily concerned with cognitive processes, RPA is involved with the automation of repetitive, rule-based processes. At a low-level, RPA bots are adept at mimicking human interaction.

RPA mostly takes the form of software robots, or bots, that are capable of carrying out back-office tasks. Think of high-volume, time-consuming processes that require little critical thinking. For example, data scraping, compliance reporting, customer order processing, claims administration, or scheduling systems.

To see why RPA is necessary, you only need to know that 50% of companies spend up to $25 per manually processed invoice. And that sales reps spend up to 64% of their time on non-revenue generating tasks.

The more cognitively demanding tasks are for humans or AI. However, they can be combined with AI or human resources to considerably scale up the completion of more complex tasks.

The main goal is to free up employees from having to deal with tasks that don’t require human-level intelligence. So, employees can use their time and decision-making capabilities where needed most. For example, where there’s a need for strategy, creativity, and innovation.

Because of the relative simplicity of these bots and the operations they are tasked with, they can be implemented relatively quickly and easily. RPA can also rapidly be scaled across projects, processes, or teams.

As a result, the ROI for implementing RPA can be quite high. According to the institute for RPA, businesses stand to enjoy immediate cost savings of between 25-50%. It’s no wonder that 20% of organisations adopted RPA by 2021.

Business Process Management (BPM)

In an effort to save cost, time, and effort, businesses are moving toward adopting the principles of Lean Programming. This involves an in-depth process of re-examining all business processes as well as their sub-routines and tasks. Not to mention how, when, and why humans are involved with various business processes.

Through this process, the performance, as well as the positive and negative contributors to said performance, are identified. The processes are then stripped of any redundancies or inefficiencies, and unnecessary human participation is replaced with AI or RPA. So, in the era of IA, business process management is also often referred to as “workflow automation” or “business process automation.”

The end goal is to limit time spent on non-value-adding activities and improve the efficiency and accuracy of essential processes. That’s especially true for processes involved in production or service delivery.

Aside from making employees more efficient, optimised business processes can also improve customer-facing experiences and increase the speed of business.

It’s estimated that inefficiencies cost companies between 20-30% of their annual revenue each year. So, this is one area that can yield massive results.

Tools

Companies look to specific technology solutions to solve challenges or address inefficiencies. Third-party tooling can offer cost-effective and high-ROI solutions on an as-needed basis.

That is particularly true as SaaS platforms and endpoint software are becoming increasingly advanced in terms of AI, machine learning, and automation.

However, companies must be careful about what tools they adopt and how they integrate them into their operations. Unconsciously adopting tools without thinking of the whole picture can leave independent teams operating disjointedly.

The company’s overall technology ecosystem can become siloed according to specific business functions. That can lead to company-wide visibility, data sharing, and collaboration problems. At some point, an overall technology strategy will be needed to ensure you maintain coherence across all your teams, systems, and processes.

That being said, tooling can result in some quick wins for businesses looking to make inroads into intelligent automation.

Data

Data is playing an increasingly important role in the success of organisations. It determines a business’ ability to strategise, make snap decisions, adjust to emerging trends, and offer value to its customers.

In fact, statistics show that data-driven businesses are 23x more likely to retain customers and increase their profits by up to 8%. What’s more, it has the potential to help companies to grow by better understanding their customers.

It’s no exaggeration to say that for many businesses, their data IS the business.

In the lifecycle of intelligent automation, data is what feeds all the other components. It’s used, consumed and processed using RPA and AI software. It’s also internal data regarding performance and other metrics that drive the acquisition of tooling as well as the optimisation of business processes.

As such, data collection tends to be constant and large-scale. Compliance and governance measures also need to be in place to ensure the data supply chain is transparent, trustworthy, and accessible. Automation and intelligent data handling are required to ensure businesses can extract value from their data efficiently, accurately, and within reasonable timeframes.

This creates a loop where AI, smart/predictive analytics, and automation can be used to scale data operations. At the same time, the enhanced data can be used to further AI systems through machine learning and further optimise business operations.

Conclusion

While we can talk about the individual components of intelligent automation as distinct concepts, they all have the same overarching goal. That is to help businesses foster greater efficiency, consistency, and productivity while saving costs and time and freeing up a company’s cognitive resources to focus on more strategic challenges.

As an immediate and short-term goal, IA will help businesses achieve bottom-line and top-line growth. Going forward, IA will also play a pivotal role in companies being able to outmaneuver competitors or industry disruptors.

If you haven’t done so, the best time to assess your business’ IA maturity and develop your own IA strategy is now.

Sydney start-up Innovo has won a race against time to enable 21 small Australian banks to become compliant with the country’s new Open Banking rules.

The DevOps and digital development specialist was tasked with bringing the institutions in line with regulations of the Australian Competition and Consumer Commission’s (ACCC) Consumer Data Right (CDR) rules before the deadline of 1 November.

Essentially, the rules cover the sharing of product reference data between banks in a standardised format to facilitate better product comparisons, according to the ACCC.

The 21 banks banded together to enlist Innovo after discovering the software platform provided for building out CDR capabilities was lacking a robust testing system to prove regulatory compliance.

For each individual bank to develop their own testing solution, it would be expensive, time-consuming, and potentially add risks.

Having missed the Phase One deadline of 1 July, the 21 banks had just four months to build their own testing software in order to meet the second phase deadline of 1 November.

Speaking to ARN, Innovo director Harold Bult explained that the challenge was to create a “smart solution” that would allow all the banks to test the platform independently, avoiding the time-consuming and manual processes that had hampered the Big Four banks in their initial testing phase last year.

“We used the test cases that the ACCC has put on their website and built them into an automated test solution that gives the banks the ability to run those tests in a relatively short time window,” he said.

In total, more than 90 people, mostly based off-shore, worked on the project, providing coding and development of the scripts.

The software solution is hosted on Amazon Web Services’ (AWS) EC2 and uses AWS Control Tower to manage multiple accounts.

Future operations will utilise additional AWS native services, leveraging both the cloud provider and the capabilities of AWS Premier Consulting partner Blazeclan.

Although the testing task may have seemed easier when completed with the first few banks, the Innovo team soon ran into difficulties when bringing the rest on board.

“In theory, everyone was thinking if it works for the two early adopters, then it will also work for the rest,” Bult said. “But it seemed a bit more complex than that, mostly from a coding perspective.

“We saw that the code is quite stable across the banks but needed to resolve varying configuration. Each bank has slightly different specific customisations in their environment.”

Bank of Victoria was one of the institutions to recently cross over the line and complete a successful test ahead of the November deadline.

Speaking to ARN, Bank of Victoria CIO Scott Wall explained that while the legislation of data sharing will be a “massive cost” to the company, the completion of the testing will enable them to start reaping some of the benefits.

“Now we can receive that data, which will probably be next year,” he said. “Then we can start developing new products and services ourselves.”

“Innovo was an untested partner for us,” he added. “But we signed up with them as they had a very clear-cut project plan, and they’ve certainly delivered. We’re very positive about that relationship and it was very collaborative.”

According to Innovo managing director Nick Finlayson, the testing solution will now be made available on AWS’ Marketplace, with potential for customers from across utilities to telecommunications to leverage it for their own CDR needs.

As of 1 November, all 21 banks have now passed the testing stage and can go live with their CDR capabilities, Finlayson told ARN.

“We shared the knowledge openly and did what was right for open banking and the CDR industry,” he added. “We spent over $1 million to build the solution just in development costs and yet we shared that, and each bank paid less than 10 percent of that to get the product and full compliance. We ensured they all got across the line. It was a true vendor and industry partnership.”

Eleanor Dickinson (ARN)