Every organisation in the water and infrastructure sector has a version of the same problem. There are engineers on the team, typically the most senior and experienced, whose knowledge is irreplaceable. They know why a particular process configuration works in one context but not another. They know the design choices that look correct on paper but fail in practice. They know the difference between what the textbook says and what the physical plant actually does. And they are approaching retirement age. 

The conventional response to this risk is succession planning and knowledge transfer: ensuring that experienced engineers have enough time with their successors to pass on what they know before they leave. This is necessary but insufficient. Tacit engineering knowledge, the kind that lives in judgment and pattern recognition rather than in documented procedures, does not transfer easily through mentorship alone. It is difficult to articulate, difficult to standardise, and difficult to transfer at the scale the current demographic shift demands. 

The more durable solution is not to transfer the knowledge between individuals, but to encode it in tools and systems that capture the underlying engineering logic and make it available independently of the people who originally developed it. This is the strategic rationale behind generative design platforms in the water sector, and it is one that deserves more attention than it typically receives in workforce discussions. 

What ‘Expertise’ Actually Means in Engineering Design 

To understand how engineering expertise can be captured and encoded, it is useful to be precise about what that expertise consists of. Engineering expertise in water and wastewater design has several distinct components. 

The first is process knowledge: understanding how different treatment technologies perform across different operating conditions, influent characteristics, and regulatory requirements. This knowledge is partly documented in technical literature, but the experienced practitioner’s contribution is knowing which documented parameters apply in which contexts, and how to adjust for the gap between textbook conditions and real-world variability. 

The second is decision logic: the rules of thumb, heuristics, and structured decision trees that experienced engineers use to navigate the thousands of choices involved in sizing, configuring, and documenting a treatment system. These are the choices that define whether a facility is genuinely fit for purpose or merely technically compliant. 

The third is institutional knowledge: the organisation-specific standards, preferences, and lessons learned that represent the accumulated experience of a team or company working in a particular regulatory context, with particular client types, and using particular equipment and technology preferences. 

All three components can, in principle, be encoded in a generative design platform. The process knowledge becomes the computational logic that the platform uses to size and configure treatment systems. The decision logic becomes the rule sets that govern how the platform navigates design choices. And the institutional knowledge becomes the configurable parameters that allow an organisation to express its own standards and preferences within the platform’s framework. 

How Transcend’s Configurator Approach Preserves Institutional Knowledge 

The Transcend Design Generator’s configurator capability is specifically designed to allow organisations to encode their own engineering standards, specifications, and design preferences into the platform. When a utility or engineering firm configures TDG according to its own design rules, it is doing something more than customising a software tool. It is creating a structured, machine-readable record of its engineering knowledge: a form of institutional memory that persists even when the engineers who developed those standards retire. 

This is a qualitatively different approach to knowledge management than documentation. A written design guide captures what an organisation does, but a configured generative design platform captures how to do it: the engineering logic that underlies each design decision, in a form that can be applied automatically to future projects. The knowledge is not just recorded. It is made productive. 

The Case of BRK Ambiental 

The value of this approach is illustrated by BRK Ambiental’s experience with TDG. When BRK’s engineering team adopted TDG, part of the implementation process involved configuring the platform to reflect BRK’s specific design standards and preferences. The result was not just faster design. It was more consistent design: outputs that reliably reflected BRK’s engineering knowledge, regardless of which team member ran the design. BRK’s engineering manager noted that the platform had become a reliable part of the team’s daily workflow, trusted precisely because its outputs were consistent with the team’s own standards. 

For an organisation facing the prospect of losing experienced engineers to retirement, that consistency has a specific value. When the engineers who configured the platform leave, their knowledge remains encoded in it. The platform continues to apply the design logic they developed, consistently, across every project that follows. The institutional knowledge does not retire with its originators. 

The Timing Challenge 

Capturing engineering expertise requires the participation of the experts who hold it. This creates a timing challenge: the most effective window for encoding expertise is while the engineers who carry it are still available to validate, refine, and extend the platform’s logic. 

Organisations that wait until experienced engineers are on the verge of retirement, or have already left, to begin this process will find it significantly more difficult. The knowledge that matters most is often the hardest to articulate: the judgment calls, the contextual adjustments, the recognition of when a standard approach is and is not appropriate. Capturing that knowledge requires sustained engagement with the practitioners who hold it, not a rushed knowledge transfer at the end of a career. 

The organisations that will be best positioned to manage the workforce transition ahead of them are those that begin the encoding process now, while their most experienced engineers are fully engaged and available to contribute. The window is not unlimited. With retirements accelerating across the sector, the urgency of this work is real. 

Beyond Retention: Building a More Resilient Organisation 

The long-term benefit of encoding engineering expertise in generative design tools is not just risk mitigation. It is organisational resilience. An organisation whose design capability depends on a small number of highly experienced individuals is fragile: it is vulnerable to retirement, turnover, illness, and the ordinary uncertainties of workforce dynamics. 

An organisation whose design capability is encoded in systematic tools is more resilient. It can onboard new engineers faster, because those engineers have access to a structured framework that encodes the knowledge they need. It can scale more efficiently, because design capacity is not constrained by the availability of senior engineers. And it can maintain consistency across projects, locations, and teams in a way that individually-carried expertise cannot guarantee. 

The silver tsunami is a crisis for organisations that have not prepared for it. For those that have encoded their expertise systematically, it is a challenge they are structurally equipped to manage. 

To explore how Transcend helps engineering organisations encode their design expertise into scalable, reusable platforms, visit transcendinfra.com/oem.