We spoke about what public sector agencies should consider when building citizen-centric services. Integrating technology into organisational processes requires a similarly strategic approach that considers immediate needs, emerging enablers, and futuristic innovations.
Here is a comprehensive look at what public sector organisations should consider when integrating technology into processes.
1. Process Essentials: Laying the Groundwork
The immediate view focuses on deploying technologies that are widely adopted and essential for current digital service provision. These foundational technologies serve as the backbone for enhancing process efficiency.
- Code. At the most basic level, the foundation is built on code – the programming languages and frameworks used to create digital services. This includes technologies like HTML, CSS, JavaScript, Java, Python, etc. A typical approach is to have a front-end web layer for the user interface and a back-end application layer for processing.
- Monolithic ERP. These systems are also crucial, especially in the early stages. These integrated software suites help manage core functions like customer management and document handling. They provide comprehensive, pre-built solutions that can be customised to specific needs. ERPs enable organisations to effectively manage complex processes from the start.
2. Emerging Catalysts: Accelerating Processes
As organisations establish foundational technologies, they should look towards second-generation enablers. Although less mature, these technologies offer emerging digital opportunities, and can significantly enhance service differentiation, through improved processes.
- PaaS. As digital services mature, organisations can leverage platform-as-a-service (PaaS) solutions hosted in the cloud. PaaS provides greater scalability, flexibility, and reduced infrastructure management overhead compared to custom development approaches. Adopting a microservices architecture on PaaS allows for developing independent components that can be updated independently, promoting continuous improvement. This modern, modular approach is highly efficient.
- Low Code/ No Code. LC/NC platforms further simplify application development by providing intuitive, visual tools that don’t require extensive coding expertise. They build on PaaS capabilities while minimising the need for deep technical skills. These environments also facilitate collaboration by enabling partners and third-parties to easily create custom solutions that integrate with the organisation’s systems. This spurs innovation through an ecosystem of complementary apps and services.
3. Future-Forward Capabilities: Next-Gen Processes
The futuristic view focuses on forward-looking technologies that address long-term roadblocks and offer transformative potential. These technologies are currently speculative but hold the promise of significantly reshaping the market.
- Complex RPA and ML. Robotic process automation (RPA) and machine learning take technological maturity to the next level by automating routine tasks and optimising decision-making through intelligent algorithms. The integration of RPA with machine learning goes beyond simple automation to enable more complex, data-driven decision processes across the workflow. Analysts predict that by 2025, up to 50% of work could be automated this way, drastically improving efficiency.
- Enterprise-Wide Microservices Architecture. An enterprise-wide microservices architecture represents an advanced approach suitable for collaboration between agencies, technical service providers, and partners. Each microservice is designed to be independently deployable, testable, and focused on specific capabilities. This decentralised model allows services to be updated or replaced without disrupting the entire system, enhancing resilience. On a PaaS platform, it enables an agile, scalable approach aligned with modern e-government needs.
- Industry Cloud. The Industry Cloud is essentially a highly configurable PaaS solution, designed to meet the specific needs of not just one government agency or jurisdiction, but with adaptability for broader use.
Ecosystm Opinion
A comprehensive roadmap should outline how to build upon current process foundations with emerging catalysts like cloud platforms and low-code development, while actively preparing for future-forward capabilities around automation, microservices architectures, and industry cloud solutions.
By taking a long-term, systematic approach to integrating technology at every stage of the process lifecycle, agencies can cultivate an adaptable digital process ecosystem that continually evolves in lockstep with technological innovation. The goal is to foster processes that don’t just endure disruption, but fundamentally improve because of it – cementing organisational resilience and agility for decades to come.
Over the past year, many organisations have explored Generative AI and LLMs, with some successfully identifying, piloting, and integrating suitable use cases. As business leaders push tech teams to implement additional use cases, the repercussions on their roles will become more pronounced. Embracing GenAI will require a mindset reorientation, and tech leaders will see substantial impact across various ‘traditional’ domains.
AIOps and GenAI Synergy: Shaping the Future of IT Operations
When discussing AIOps adoption, there are commonly two responses: “Show me what you’ve got” or “We already have a team of Data Scientists building models”. The former usually demonstrates executive sponsorship without a specific business case, resulting in a lukewarm response to many pre-built AIOps solutions due to their lack of a defined business problem. On the other hand, organisations with dedicated Data Scientist teams face a different challenge. While these teams can create impressive models, they often face pushback from the business as the solutions may not often address operational or business needs. The challenge arises from Data Scientists’ limited understanding of the data, hindering the development of use cases that effectively align with business needs.
The most effective approach lies in adopting an AIOps Framework. Incorporating GenAI into AIOps frameworks can enhance their effectiveness, enabling improved automation, intelligent decision-making, and streamlined operational processes within IT operations.
This allows active business involvement in defining and validating use-cases, while enabling Data Scientists to focus on model building. It bridges the gap between technical expertise and business requirements, ensuring AIOps initiatives are influenced by the capabilities of GenAI, address specific operational challenges and resonate with the organisation’s goals.
The Next Frontier of IT Infrastructure
Many companies adopting GenAI are openly evaluating public cloud-based solutions like ChatGPT or Microsoft Copilot against on-premises alternatives, grappling with the trade-offs between scalability and convenience versus control and data security.
Cloud-based GenAI offers easy access to computing resources without substantial upfront investments. However, companies face challenges in relinquishing control over training data, potentially leading to inaccurate results or “AI hallucinations,” and concerns about exposing confidential data. On-premises GenAI solutions provide greater control, customisation, and enhanced data security, ensuring data privacy, but require significant hardware investments due to unexpectedly high GPU demands during both the training and inferencing stages of AI models.
Hardware companies are focusing on innovating and enhancing their offerings to meet the increasing demands of GenAI. The evolution and availability of powerful and scalable GPU-centric hardware solutions are essential for organisations to effectively adopt on-premises deployments, enabling them to access the necessary computational resources to fully unleash the potential of GenAI. Collaboration between hardware development and AI innovation is crucial for maximising the benefits of GenAI and ensuring that the hardware infrastructure can adequately support the computational demands required for widespread adoption across diverse industries. Innovations in hardware architecture, such as neuromorphic computing and quantum computing, hold promise in addressing the complex computing requirements of advanced AI models.
The synchronisation between hardware innovation and GenAI demands will require technology leaders to re-skill themselves on what they have done for years – infrastructure management.
The Rise of Event-Driven Designs in IT Architecture
IT leaders traditionally relied on three-tier architectures – presentation for user interface, application for logic and processing, and data for storage. Despite their structured approach, these architectures often lacked scalability and real-time responsiveness. The advent of microservices, containerisation, and serverless computing facilitated event-driven designs, enabling dynamic responses to real-time events, and enhancing agility and scalability. Event-driven designs, are a paradigm shift away from traditional approaches, decoupling components and using events as a central communication mechanism. User actions, system notifications, or data updates trigger actions across distributed services, adding flexibility to the system.
However, adopting event-driven designs presents challenges, particularly in higher transaction-driven workloads where the speed of serverless function calls can significantly impact architectural design. While serverless computing offers scalability and flexibility, the latency introduced by initiating and executing serverless functions may pose challenges for systems that demand rapid, real-time responses. Increasing reliance on event-driven architectures underscores the need for advancements in hardware and compute power. Transitioning from legacy architectures can also be complex and may require a phased approach, with cultural shifts demanding adjustments and comprehensive training initiatives.
The shift to event-driven designs challenges IT Architects, whose traditional roles involved designing, planning, and overseeing complex systems. With Gen AI and automation enhancing design tasks, Architects will need to transition to more strategic and visionary roles. Gen AI showcases capabilities in pattern recognition, predictive analytics, and automated decision-making, promoting a symbiotic relationship with human expertise. This evolution doesn’t replace Architects but signifies a shift toward collaboration with AI-driven insights.
IT Architects need to evolve their skill set, blending technical expertise with strategic thinking and collaboration. This changing role will drive innovation, creating resilient, scalable, and responsive systems to meet the dynamic demands of the digital age.
Whether your organisation is evaluating or implementing GenAI, the need to upskill your tech team remains imperative. The evolution of AI technologies has disrupted the tech industry, impacting people in tech. Now is the opportune moment to acquire new skills and adapt tech roles to leverage the potential of GenAI rather than being disrupted by it.
