From configurable tools to AI engineering software that does the work
AI engineering software is a new generation of design and automation tools that not only suggest commands but also interpret engineering data, generate code, build projects and configure systems with minimal manual setup, shifting everyday workflows from hand-built configurations to automated project generation and intelligent design assistance. This shift is visible across mechanical CAD, industrial automation and building design platforms, where AI is being embedded directly into core environments instead of sitting on the sidelines as a generic chatbot. The point is not novelty; it is to reclaim the large blocks of time engineers spend on drawings, boilerplate PLC code and repetitive project setup, and turn those hours into system-level thinking. Vendors that used to sell feature lists are now selling outcomes: fewer clicks, fewer errors and workflows that finish in hours instead of days when AI is allowed to handle the heavy lifting.
CAD platform AI: Solid Edge starts automating the mechanical grind
In mechanical design, Siemens’ Designcenter Solid Edge shows how CAD platform AI is moving from gimmick to workflow reshaper. At the Realize Live 2026 event in Detroit, product leaders described three AI features that aim straight at the most tedious parts of CAD work. The AI Design Copilot begins life as a support chatbot but is expected to evolve into an agent that can execute commands on behalf of the user, with the team "working toward" having that capability ready before the next Realize Live conference. Magnetic snap already offers a practical win: drag a part into an assembly and the software automatically mates it with what it believes are the correct constraints, a small but telling example of design automation tools embedded in normal assembly work. The most aggressive feature is automated drawings, which takes a 3D model, selects sheet size and views, chooses scales and applies dimensions without human intervention. When customers are spending "25 to 30 percent of their time creating drawings" that could instead go into innovative work, automating that chunk is not a nice-to-have; it is an economic necessity.
Automation engineering: AI that reads XML, AML and builds TIA Portal projects
The more radical change is happening in industrial automation, where Siemens’ Eigen Engineering Agent is designed to plan, execute and validate engineering tasks instead of merely suggesting code snippets. Introduced in April 2026 and already in use at more than 100 companies across 19 countries, it runs against TIA Portal and targets PLC programming, HMI visualization and device configuration, with reported speeds two to five times faster than manual workflows and up to 50 percent efficiency gains in engineering. The latest update adds ECAD integration and standards-compliant automated project generation so the agent can exploit the information that exists before a single rung of logic is written. It now reads XML and AML electrical design files, identifies inconsistencies, adds devices, configures connections and generates PLC tags directly from the hardware topology. This cuts the miserable hours of retyping tag names and hunting wiring changes between ECAD tools and automation platforms. On top of that, engineers can describe a machine in plain language and get a Siemens Automation Framework–based TIA Portal project, ready for further development. If that sounds like the AI is doing the engineer’s job, it is more accurate to say it is doing the engineer’s paperwork.
Why automated project generation changes collaboration, not just speed
It is easy to frame these features as simple time savers, but the deeper impact is on how teams collaborate around design intent. Electrical engineers, automation programmers and mechanical designers have long worked in separate tools with fragile handoffs; every translation between ECAD files and PLC tag lists was another chance for inconsistency and rework. When an AI agent can read the same XML or AML file, push devices and tags into TIA Portal, and build a standards-compliant project from a shared machine description, the data becomes a common language rather than a pile of disconnected documents. Likewise, CAD platform AI that auto-mates parts into assemblies and generates drawings reduces the friction between conceptual design and documentation. This does not erase the need for expertise, but it does reset where that expertise is applied. Engineers move from caretaking data structures to defining behavior and constraints, while AI takes over the rote translation work that used to dominate early project stages. That is not a productivity tweak; it is a quiet redesign of engineering culture.
What comes next: AI as a hands-on colleague in everyday tools
The common thread across these moves is clear: engineering software vendors are betting that AI should act like a colleague embedded in the tool, not an abstract oracle in a browser tab. Siemens’ Solid Edge team openly describes a near-future Design Copilot that will "go execute the command for you," turning help text into direct action inside the CAD environment. The Eigen Engineering Agent already operates that way, interpreting requirements, writing control software, configuring systems and checking them against quality criteria so engineers can focus on higher-level decisions. The reported 80 percent improvement in overall solution quality suggests this is not only faster but safer for projects that used to rely on manual setup and ad hoc standards adherence. The lesson for engineers and managers is straightforward: if your design automation tools are still limited to macro recorders and templates, you are competing against teams whose AI engineering software is now reading your machine descriptions and building entire projects before you have finished defining your tag naming convention. The conclusion is uncomfortable but necessary: resisting AI in everyday engineering platforms is no longer a cautious stance; it is a strategic risk.
