Wabtec Digital Mine’s Product Engineering Department needed to increase engineering productivity and delivery predictability without compromising the reliability of products already operating in mines. EIC established a future-state blueprint of 42 AI systems across eight engineering areas, assessed their business value and implementation readiness, and organized transformation into a proposed 12-quarter roadmap.
Wabtec Digital Mine develops safety technologies for mining operations, including onboard collision avoidance and vehicle intervention systems, office and cloud applications, and machine vision capabilities. Its Product Engineering Department operates across Australia, India, and South Africa, maintaining existing product generations while developing the next Unified Safety platform and integrating acquired DotNetix technologies.
Established engineering procedures, independent quality assurance, and corporate cybersecurity controls provided important safeguards, but execution remained partially manual. Technical knowledge was concentrated among specialists, engineering practices varied across teams, and legacy architectures created significant maintenance and integration effort. Adding AI tools alone would not resolve these underlying constraints.
The same technical resources supported existing revenue-generating products, Generation 4 development, platform modernization, and acquired product integration
Requirements, designs, code changes, testing records, and release evidence resided in separate tools, requiring manual reconciliation and coordination
Inconsistent effort tracking and fragmented management information made it difficult to establish reliable engineering productivity baselines
Software Development scored 5.0 for value but only 1.67 for readiness. Solution Architecture, System Integration, Software Platform Engineering, Safety Algorithms Engineering, and System Testing each scored 4.6 for value and 1.33 for readiness. Controlled AI assistance and foundations come first.
Architecture documentation, technical decisions, engineering standards, and product-specific knowledge were distributed across systems and experienced specialists. Knowledge preparation moves into the first phases.
The Software Engineering SOP introduced in May 2026 was not yet applied consistently across all engineering teams and locations. Process standardization comes before automation.
Rather than selecting individual AI development tools, EIC defined how the department could operate as an AI-native engineering system.
The first phases build curated engineering knowledge, a consistently applied procedure, approved AI tools, and controlled system access. The highest-value AI systems depend on them.
No engineering tool was replaced, and the May 2026 Software Engineering SOP remained the operating baseline. Architecture approvals, independent QA, release authorization, and cybersecurity controls remain in place.
AI-assisted development, test preparation, engineering documentation, and knowledge retrieval in phase 1, with success measures and human oversight boundaries defined before broader use.
Throughout the roadmap, engineering professionals retain authority over architecture, safety-related judgments, independent verification, significant engineering changes, and release decisions.
The future-state blueprint identified 42 specialized and cross-functional AI systems across eight engineering areas. AI assistants, specialized agents, reusable skills, deterministic automation, knowledge systems, and human approvals would operate together within defined engineering boundaries.
Three targeted stakeholder interviews, with Engineering leadership, Software Engineering management, and Applications Engineering, and the available engineering documentation.
Established engineering procedures, existing software development and testing automation, technical systems, governance requirements, and manual coordination.
AI systems connected to existing engineering platforms, product knowledge, technical responsibilities, and human decision authorities.
A common five-point scoring framework separating strategically important opportunities from comparatively feasible starting points.
Software productivity and knowledge engineering first; hardware and firmware engineering, system integration, safety assurance, and Software Factory capabilities later.
The decisions and controls needed before progressing toward more autonomous execution.
The engagement completed the strategy, assessment, and transformation planning work. No controlled pilot had yet demonstrated operational results, so engineering productivity gains, cost reductions, and delivery improvements remain unmeasured.
Team Collaboration, Change Management, Business Documentation, Engineering Standards, UX/UI Design, Delivery Automation, and Process Engineering scored at or above the readiness midpoint of 3.0. Sixteen scored between 2.33 and 2.67, and 19 at 2.0 or below.
Their automation depends on stronger engineering knowledge, process consistency, system integration, or technical preparation.
The plan progresses from AI-assisted individual work to supervised multi-step workflows and, where approved, bounded autonomous execution. The final phase envisions integrating all 42 systems at their respective target automation levels.
Approved AI tools, controlled engineering-system access, curated technical knowledge, governance and review guidelines, trained pilot participants with productivity baselines, and corporate IT or vendor provisioning support where needed.
Evidence-based AI investment priorities. Business value, readiness, risk, and dependencies provide a structured basis for deciding what to prepare, pilot, and implement.
Reduced risk of premature automation. Engineering knowledge, process consistency, approved tools, system connectivity, and quality controls were established as prerequisites for reliable execution.
A clearer path to engineering productivity. AI opportunities to reduce repetitive development, testing, documentation, maintenance, and coordination effort are connected to the engineering capabilities required for measurable gains.
Stronger engineering alignment. Teams across Australia, India, and South Africa share one strategic model, aligned with Generation 4 development, DotNetix integration, legacy modernization, and ongoing product maintenance.
The lasting strategic result is a common blueprint, assessment framework, and implementation sequence. The transition to actual AI-native engineering operations depends on subsequent authorization, detailed design, controlled pilots, implementation, adoption, and measured performance.
The service behind this storyAI Organization Strategy →
“Our question was how to raise engineering productivity without putting the products our customers run at risk. What we got is an order of work our own architects can drive: AI assists first, the knowledge gets written down, and the decisions that matter stay with people.”
In a free consultation, we can review your engineering organization and discuss how to build a prioritized, risk-controlled AI transformation roadmap.
