Australians are being encouraged to take their fitness journey into the community with the launch of the Active Locals app, the result of an ambitious collaboration between HCF, Australia’s largest not-for-profit health fund, and global technology consultancy PALO IT. In a technology landscape increasingly focused on leveraging AI for business productivity, the Active Locals initiative stands out—not simply as a digital platform for connecting exercise enthusiasts, but for its pioneering use of an AI-driven methodology that experts view as a potential game-changer for how enterprise software is built.
The development of the Active Locals app leveraged a proprietary approach termed "Gen-e2" (Generative AI Enhanced Engineering). Unlike conventional software development that uses AI tools as supplementary add-ons, the Gen-e2 methodology integrates AI deeply into every phase of the development lifecycle. The result is an “AI-first” paradigm, moving away from merely augmenting existing practices to fundamentally reimagining them.
Tom Robinson, Head of Innovation at HCF, described this as a “drastic shift from traditional ways of delivering software,” citing a host of promising benefits. Most notably, the team reported an 86% productivity gain compared to established agile delivery models—bringing the app from concept to launch in just four months. While such numbers are striking, they merit scrutiny and further validation across future projects and independent case studies. However, early indications from HCF and PALO IT reflect notable improvements in project velocity, code quality, and team satisfaction.
A growing body of research underscores the link between social engagement and physical activity. By leveraging this relationship, Active Locals seeks to offer a differentiated value proposition compared to more generic fitness apps, which often focus exclusively on individual progress or tracking metrics. The promise is not just improved health outcomes for users, but potentially stronger, healthier communities overall.
What does this mean in practical terms? The Gen-e2 stack folds AI into planning, coding, testing, quality assurance, and even team collaboration. Microsoft Copilot, the generative AI solution developed by Microsoft, played a significant role in this process. As part of a two-year partnership between PALO IT and Microsoft, the project is one of the first globally to utilize such a comprehensive AI-first approach powered by Copilot.
Process innovations like automated sprint planning, exploratory test case generation, and on-the-fly code refactoring were all enhanced by the synergy between AI and human teams. Instead of relying on AI for isolated tasks, the Gen-e2 approach allows AI systems to work holistically alongside developers, product managers, and designers.
Public details about how this productivity was quantified are limited. Stakeholders cite factors such as increased delivery speed, fewer bugs, higher developer satisfaction, and smoother project coordination. Independent validation by third parties—and more projects using Gen-e2 or similar methodologies—will be crucial for substantiating these claims and understanding potential trade-offs, such as the learning curve and initial setup costs.
For product leads like Liam Gilligan at HCF, the payoff is not only in speed but in redefining internal standards: “Building Active Locals using the Gen-e2 approach allowed us to deliver more and faster. It has transformed our view of what efficient, high-quality product delivery can look like.” Such testimonials carry weight, especially as organizations worldwide search for practical, measurable ways to embrace AI before competition leaves them behind.
For Australian enterprises, healthcare providers, and IT consultants, the Active Locals story offers both a promising case study and a set of cautionary lessons as they contemplate their own digital transformation journeys. With the global race for AI-driven innovation accelerating, methodologies like Gen-e2 could soon become a blueprint for modern software delivery—if, and only if, initial results can be generalized and risks managed with diligence.
Yet, as with all disruptive technologies, some skepticism remains prudent. The productivity gains trumpeted by its advocates must withstand both external validation and the test of time. Beyond technical capability, questions of explainability, team culture, and ethical administration remain ongoing challenges.
Nevertheless, the Active Locals app demonstrates what’s possible when visionary organizations are willing to take calculated risks on emerging technology. As more firms analyze the outcomes of the HCF and PALO IT partnership, the next chapter for AI-driven software development in Australia could be written by those who treat AI not just as a toolset, but as a strategic collaborator—integral to every code commit, every product sprint, and every user experience delivered.
In a digital era defined by accelerating change, those who move fastest—and most responsibly—are likely to lead. For now, the Active Locals experiment bears close watching, its lessons potentially shaping the contours of enterprise technology far beyond Australia’s shores.
Source: Mi-3.com.au. HCF and Palo IT launch fitness meet-up app with AI-driven methodology | Mi3
A New Approach: The Gen-e2 Methodology
The development of the Active Locals app leveraged a proprietary approach termed "Gen-e2" (Generative AI Enhanced Engineering). Unlike conventional software development that uses AI tools as supplementary add-ons, the Gen-e2 methodology integrates AI deeply into every phase of the development lifecycle. The result is an “AI-first” paradigm, moving away from merely augmenting existing practices to fundamentally reimagining them.Tom Robinson, Head of Innovation at HCF, described this as a “drastic shift from traditional ways of delivering software,” citing a host of promising benefits. Most notably, the team reported an 86% productivity gain compared to established agile delivery models—bringing the app from concept to launch in just four months. While such numbers are striking, they merit scrutiny and further validation across future projects and independent case studies. However, early indications from HCF and PALO IT reflect notable improvements in project velocity, code quality, and team satisfaction.
Social Fitness, Powered by AI
Active Locals is not just a fitness meet-up app; it’s a platform where social connection becomes the engine of physical activity. The app utilizes a matching algorithm to connect users with local groups according to shared interests and comparable fitness levels. This AI-matching system aims to remove the intimidation and isolation that people sometimes experience when trying out a new sport or returning to exercise after a break. Users can join running clubs, walking groups, or organize their own fitness meet-ups—helping Australians get moving while forging new social bonds in their communities.A growing body of research underscores the link between social engagement and physical activity. By leveraging this relationship, Active Locals seeks to offer a differentiated value proposition compared to more generic fitness apps, which often focus exclusively on individual progress or tracking metrics. The promise is not just improved health outcomes for users, but potentially stronger, healthier communities overall.
Engineering Disruption: AI as a Project Partner
The Gen-e2 methodology, as used by HCF and PALO IT, represents a growing trend: moving beyond the use of AI as a clever assistant to making it an integral, decision-making partner throughout the software development process. According to PALO IT’s co-founder and Regional Managing Director, Tanguy Fournier Le Ray, most methodologies “merely bolt AI tools onto existing processes.” In contrast, Gen-e2 “reimagines the entire development lifecycle to become AI-first.”What does this mean in practical terms? The Gen-e2 stack folds AI into planning, coding, testing, quality assurance, and even team collaboration. Microsoft Copilot, the generative AI solution developed by Microsoft, played a significant role in this process. As part of a two-year partnership between PALO IT and Microsoft, the project is one of the first globally to utilize such a comprehensive AI-first approach powered by Copilot.
Process innovations like automated sprint planning, exploratory test case generation, and on-the-fly code refactoring were all enhanced by the synergy between AI and human teams. Instead of relying on AI for isolated tasks, the Gen-e2 approach allows AI systems to work holistically alongside developers, product managers, and designers.
Claims and Caution: Scrutinizing Productivity Gains
The claim of an 86% productivity improvement is headline-worthy. For an industry often criticized for missed deadlines and ballooning costs, AI-first methods that can meaningfully accelerate delivery are tremendously appealing. However, it’s essential to contextualize this figure. As with many first-mover claims, the measured productivity gain reflects the specific environment, team dynamics, and project scope at HCF rather than being universally replicable. Software productivity is notoriously difficult to measure, often depending on subjective reporting, baseline team maturity, and the novelty factor of new technologies.Public details about how this productivity was quantified are limited. Stakeholders cite factors such as increased delivery speed, fewer bugs, higher developer satisfaction, and smoother project coordination. Independent validation by third parties—and more projects using Gen-e2 or similar methodologies—will be crucial for substantiating these claims and understanding potential trade-offs, such as the learning curve and initial setup costs.
Strengths of the AI-First Software Model
1. Speed-to-Market
Delivering the Active Locals app in just four months provides a tangible benchmark for the power of integrated AI methodologies. Traditional enterprise app projects, even with agile practices, can often stretch to six months or more for a market-ready product, especially when complex matching algorithms are involved.2. Enhanced Code Reliability
By folding generative AI into the testing and refactoring stages, code quality can theoretically be improved—AI can exhaustively generate test cases, instantly identify potential vulnerabilities, and suggest fixes more quickly than human testers working alone.3. Developer Wellbeing
Increased “team satisfaction” surfaced as a key outcome. Developers often face repetitive or tedious tasks, which can lead to burnout. Generative AI absorbs some of this workload, freeing engineers to focus on creative problem-solving and high-value feature work.4. Continuous Learning
AI-first methodologies like Gen-e2 enable constant feedback loops. Every interaction, bug fix, and customer insight automatically becomes part of the data corpus AI draws on, allowing rapid refinement and continuous improvement.5. Strategic Partnerships
The involvement of Microsoft Copilot provides an extra layer of intelligence and support. Working directly with Microsoft ensures early access to advanced features, security updates, and potentially a quicker path to resolving any incompatibilities or performance bottlenecks.Risks and Open Questions
1. Overreliance on AI Tools
As organizations embrace AI-driven development en masse, there’s a risk of over-delegating critical architectural and security decisions to algorithms not yet mature enough to handle all edge cases. Ensuring that human oversight remains robust is vital.2. Vendor Lock-In
With Microsoft Copilot playing such a central role, customers may become dependent on Microsoft’s AI stack. If future platform or pricing changes arise, or if Microsoft pulls back features, organizations could face disruptions or significant migration costs.3. The Black Box Problem
Gen-e2, like many generative AI solutions, can struggle with explainability. Developers and testers may find it challenging to understand why certain code was generated or why particular architectural decisions were made by the AI, complicating debugging and compliance auditing.4. Ethical Implications
As AI-generated code and decisions proliferate, questions around data privacy, bias in matching algorithms, and the transparency of user-facing features become ever more important. Regulators and users alike will demand evidence that such systems are robust, fair, and secure.5. Team Adaptability
Transforming a traditional development team into one able to harness an AI-first workflow requires significant training and mindset change. The initial productivity gains may be tempered by a “learning curve dip” as teams get used to new processes, AI-specific bugs, or novel forms of collaboration.Industry Impact: Signaling a Paradigm Shift
PALO IT has previously worked with highly respected clients such as UNICEF, Total Energies, Standard Chartered, and Grab. By bringing the Gen-e2 methodology to an Australian healthcare organization, both firms are clearly signaling their intent to make AI-first development the new normal, not an exception reserved for tech giants.For product leads like Liam Gilligan at HCF, the payoff is not only in speed but in redefining internal standards: “Building Active Locals using the Gen-e2 approach allowed us to deliver more and faster. It has transformed our view of what efficient, high-quality product delivery can look like.” Such testimonials carry weight, especially as organizations worldwide search for practical, measurable ways to embrace AI before competition leaves them behind.
Looking Ahead: Scaling AI-First Innovation
The collaboration between HCF and PALO IT is positioned as a stepping stone, not a one-off milestone. Both firms have indicated that the partnership lays groundwork for future projects leveraging the Gen-e2 methodology, with a clear ambition to “scale our Gen-e2 methodology to continue delivering cutting-edge solutions.”For Australian enterprises, healthcare providers, and IT consultants, the Active Locals story offers both a promising case study and a set of cautionary lessons as they contemplate their own digital transformation journeys. With the global race for AI-driven innovation accelerating, methodologies like Gen-e2 could soon become a blueprint for modern software delivery—if, and only if, initial results can be generalized and risks managed with diligence.
Conclusion: A New Standard or an Early Experiment?
The launch of the Active Locals app is a significant milestone, not only for HCF and its mission to foster healthier communities, but also for the broader Australian tech and health sectors eager to capitalize on next-generation development paradigms. The Gen-e2 methodology, powered in part by Microsoft Copilot, exemplifies the power and potential of true AI-first software engineering.Yet, as with all disruptive technologies, some skepticism remains prudent. The productivity gains trumpeted by its advocates must withstand both external validation and the test of time. Beyond technical capability, questions of explainability, team culture, and ethical administration remain ongoing challenges.
Nevertheless, the Active Locals app demonstrates what’s possible when visionary organizations are willing to take calculated risks on emerging technology. As more firms analyze the outcomes of the HCF and PALO IT partnership, the next chapter for AI-driven software development in Australia could be written by those who treat AI not just as a toolset, but as a strategic collaborator—integral to every code commit, every product sprint, and every user experience delivered.
In a digital era defined by accelerating change, those who move fastest—and most responsibly—are likely to lead. For now, the Active Locals experiment bears close watching, its lessons potentially shaping the contours of enterprise technology far beyond Australia’s shores.
Source: Mi-3.com.au. HCF and Palo IT launch fitness meet-up app with AI-driven methodology | Mi3
