When developing technology architecture, you focus on the underlying environment that everything else relies on—the infrastructure. This includes the computing resources (servers, virtualization, or cloud services), storage, network design, security controls, recovery and resilience plans, and the management tools that keep systems running. The infrastructure shapes performance, scalability, cost, and reliability, and it determines how well applications, databases, and integration layers can function at scale. Applications are the software that runs on top of this foundation, databases provide data storage on that foundation, and integration enables components to communicate; all of these depend on a solid infrastructure to deliver the needed performance and availability. So, considering infrastructure upfront sets the stage for a stable, scalable technology architecture.

In the ADDIE model, the phase where you put the plan into action and ensure that the learning environment and technology are ready is the implementation phase. This stage is about delivering the training and making sure all logistics are in place so the session can run smoothly. That includes confirming that equipment such as computers, projectors, audio tools, and any simulation or interactive devices are available and functioning, as well as verifying that all software applications, LMS access, and other tech used for the training are working properly. By focusing on readiness and delivery, you minimize technical disruptions and support effective learning. Earlier phases serve different purposes: analysis identifies needs and constraints; design maps out objectives and instructional strategy; development creates and tests materials. But the actual setup, deployment, and troubleshooting of the training environment and tools happen during implementation.

Process redesign is about deliberately altering how a workflow operates to improve performance, with the expectation of a better outcome. It inherently involves making a change to the process itself—reordering steps, adding or removing steps, or changing who performs them. At the same time, redesign is driven by the goal of achieving improvements such as faster processing, fewer errors, or higher data quality. So, it includes both a change in the process and the anticipation of a better result. If you focus only on outcomes without changing the workflow, that isn’t redesign; if you only change something without aiming for improvement, it isn’t a redesign either.

A business-driven sequence is used: begin by mapping the organization’s business processes and goals, then design the information architecture to support those processes, and finally define the technology architecture that will enable the information and workflows. Starting with how the business operates ensures you capture the actual needs, constraints, and value streams. With that context, you can model data and information flows that truly support decision-making and process execution. Only after the information needs are clear do you select and align the appropriate technologies, ensuring platforms, applications, and infrastructure fit the defined processes and data requirements. If you start with technology, you risk committing to solutions before understanding process needs or data requirements, leading to misaligned or wasted investments. If you jump to information architecture without grounding it in real business processes, the data models may not reflect how work is actually performed. This ordered approach—process first, information second, technology third—helps ensure coherence, traceability, and effective implementation.