AI workshops for structural engineering firms

Who it's for

Practicing structural engineers already using AI tools in their calc workflow, or curious to start, who suspect they're only scratching the surface: treating it like a faster way to look things up rather than a tool that can give them a starting point for a whole project.

What we cover

• The gap between how most people use AI today (quick lookups, summaries) and what it's actually capable of: describing a project in plain language and getting a starting point you can refine, using real project types from the room
• Prompting for engineering work specifically: how to phrase a request so the tool understands your standards, your defaults, and what "done" looks like for your firm
• Building a verification habit that's fast, not anxious: how to check AI output efficiently so you're confident in it, without re-deriving everything by hand every time
• Where current tools fall short (speed, occasional wrong defaults) and how to work around known limitations without losing trust in the parts that genuinely save time
• A structured way to bring a junior engineer or EIT into the workflow using AI as a guided walkthrough, rather than throwing them in cold

Session outcomes

• A personal prompting reference sheet built around your own most common calc jobs
• A verification checklist calibrated to your own risk tolerance and project types
• A benefit you'll notice immediately: faster project setup and fewer repeated manual entries, without giving up the double-checking you already trust yourself to do

Who it's for

Principals, practice leaders, and operations managers at engineering firms where individual engineers are already experimenting with AI tools on their own, with no shared standard for what's acceptable, how outputs get reviewed, or where responsibility sits if something goes wrong.

What we cover

• The real risk in most firms right now isn't AI itself: it's ten engineers doing it ten different ways, with no consistency and no paper trail
• Where liability and professional exposure actually sit when an AI-assisted output feeds into a design decision, and what documentation protects you
• What separates a tool built for engineering accountability from general productivity software: the questions worth asking before you let your team rely on one
• A working session where you draft your own firm framework: what to automate, what needs mandatory review, and what stays off-limits entirely
• How to roll this out without over-restricting your team and losing ground to firms moving faster: sequencing a low-commitment pilot you can approve directly, rather than waiting on a lengthy internal sign-off process

Session outcomes

• A draft AI policy covering task categories, review requirements, and documentation standards
• A vendor evaluation checklist you can apply to any AI tool your firm considers next
• A policy document ready to bring to your team within two weeks, turning individual experimentation into one firm-wide standard everyone can point to

Who it's for

Engineers and practice leads doing high-volume, standards-driven work (utility sizing, load calculations, template-based documentation) where the inputs vary but the governing standards don't, and where the same manual process gets repeated project after project.

What we cover

• Why standards-based work is the best possible candidate for AI assistance: the rules don't change, only the building parameters do, so the labor is inherently repeatable, not inherently valuable
• Mapping your own repetitive workflows: identifying which tasks are pure standards-application versus which require genuine engineering judgment, so you know exactly where automation helps and where it shouldn't touch
• Going from a plan or a set of parameters straight to structured output: extracting what you need and applying your standards automatically instead of manually keying in the same values project after project
• Keeping quality intact at speed: building automation into your workflow without losing the verification step that makes the output defensible
• A live exercise reworking one of your own recurring, template-heavy tasks into a faster, AI-assisted version

Session outcomes

• A short list of your own repeatable tasks ranked by automation potential
• A working AI-assisted version of one real recurring workflow, built during the session
• A tangible benefit you can measure: hours reclaimed per week on the specific repetitive tasks you brought into the room, without giving up the standards-checking that protects your sign-off