Corporate training
Corporate training. Three threads: AI literacy your team can apply day-to-day, AI for corrosion R&D as a working practice, and hands-on workflow sessions on your own data. Scoped to your team's level and your scientific context — not a generic course on AI or corrosion.
Outcomes
- Read and audit AI-driven analyses — know what to trust and what to push back on.
- Gain a shared vocabulary for AI in corrosion projects, so cross-functional conversations don't stall on terminology.
- Reduce time spent on first-pass analyses, and time spent correcting them.
- Scope AI projects internally — proposing problems where AI proposals can be audited at acceptable cost.
- Carry the methodology forward — extending it after we hand the engagement over.
Audience leveling
- Corrosion specialists and ICs — practitioners doing the analyses; learn what AI proposes, what to question, and how to fold AI into existing methods.
- Senior corrosion researchers and PIs — deepen judgement on AI-driven work; calibrate when AI-proposed models can be trusted at scale.
- R&D managers and team leads — a working model for scoping AI work, allocating review effort, and reporting AI-assisted results to stakeholders.
Format & delivery
- Delivery modes: in-person at your site, remote-live over video, or hybrid where part of the cohort joins remotely.
- Cohort size: typically 4–12 participants per session for hands-on workflow work; larger groups (up to ~30) for AI-literacy briefings.
- Typical duration: half-day briefings to multi-day workshops; full programmes run 2–6 weeks with spaced sessions.
- Bespoke or catalogue: we usually design the programme around your team's level and your domain. A short catalogue of standard sessions exists for fast turnarounds — ask in your enquiry.
- On-site logistics: for in-person delivery, your facility provides the room, screens, and any restricted-data environment. We supply materials and pre-session reading.
Syllabus topics
Indicative topics — the syllabus is designed per engagement, scoped to your team's level and domain.
- AI literacy for materials and corrosion managers.
- What AI is good at, what it isn't — for corrosion-specific data.
- Reading and auditing AI-generated analyses.
- AI-proposed models — when to trust them, when to push back.
- Reusing historical experimental data at scale.
- Combining AI proposals with established corrosion methodology.
- Working with your own data: a guided AI-assisted analysis session.
- Scoping an AI project internally — what to ask before you commission.
Below the line: how we design the syllabus, customise it per engagement, and evaluate learning.
Pedagogical approach
Training is based on adult-learner principles in a corrosion-specific frame. Sessions are case-based — each module is built around a real or representative R&D problem, with learning happening by working through the AI-assisted analysis under guided review. Workshop format dominates: short instructional segments alternate with hands-on work. The pedagogical lineage tracks the methodology published on the parent /how-it-works page — what is taught is what we apply ourselves.
Customisation methodology
Programmes are scoped per engagement against four axes: audience level (from corrosion ICs to senior PIs), domain vertical (steel, marine, aerospace, semiconductor, energy), the team's existing R&D maturity around AI tooling, and the data-policy constraints in your environment. The last axis often determines delivery — whether sessions run on your own data, on representative public datasets, or under restricted-data protocols. See /data-handling for how we handle your data.
Evaluation & follow-up
Each programme closes with a post-training feedback loop — written participant feedback plus a short team-level capability check where relevant (for example: can the team audit an AI-driven analysis end-to-end).