AI & Tech Institute: Tech Faculty
Refining Technology Roles For the Future
While the AI Faculty builds and deploys the core technology, the Tech Faculty focuses on applying AI across the existing technology landscape. We are backing the next generation of technologists – knowing that while not everyone will work in a direct AI role, everyone’s role will be fundamentally transformed by AI. Our mission is to drive this new way of working across every technology discipline.
The AI Capability Framework
Through our specialised AI Capability Framework, we precisely pinpoint how traditional tech roles are evolving. We then inject these insights directly into our Institute curriculums, innovating our education to build the exact skills required for the modern working landscape.
Cultivating an AI-First Mindset
The core focus of our talent assessment for Institute learners, is identifying individuals with the capacity for a vital mindset shift: the drive to adopt an AI-first approach to any technology discipline. Once identified, we build the advanced capabilities they need to succeed and lead in a transformed industry.
Where we build AI-native Tech capability
Our award-winning AI & Tech Institute utilises over a decade of training expertise, industry-seasoned instructors, and highly curated curriculums to accelerate consultant capability. We deliver elite upskilling and strategic growth across the following innovative disciplines:
Modern Software Engineering
These engineers use AI assistants and agentic tools (Copilot, Cursor, Claude Code) as constant collaborators across the full development lifecycle. They combine systems thinking with fluency in AI workflows, prompt engineering, and rapid iteration. Industry Outcome: Dramatically higher developer throughput, faster production cycles, and an organisational ability to ship more features with leaner teams.
Data Engineering
Data engineers build and maintain the pipelines, warehouses, and governance layers required to turn raw data into usable assets. Rather than building models, they use AI to accelerate pipeline development, schema design, and quality checks – while building the infrastructure that AI systems depend on. Industry Outcome: Faster, cleaner, and more trustworthy data foundations, satisfying the single biggest pre-condition for the AI stack. Without this robust data engineering underneath them, AI engineering, MLOps, and analytics all collapse.
Automation Engineering
These engineers leverage AI for automated test generation, defect prediction, and self-healing test suites. Sitting at the intersection of QA and AI tooling, they build the automated quality frameworks that allow teams to release faster without sacrificing reliability. Industry Outcome: Higher release frequency with absolute confidence. Test coverage that once took weeks to author is now generated and maintained in hours, freeing human talent to focus on complex edge cases. The downstream effect is fewer production incidents and faster time-to-value. In regulated industries, this role is critical for AI assurance – directly testing the AI systems themselves.
Business Solutions
Covering Business Analysis, PMO, and Change Management, these professionals ensure AI successfully lands within the business. Rather than building the technology, they map workflows, redesign processes around AI capabilities, manage stakeholder buy-in, and measure realised value. Industry Outcome: The critical difference between AI being bought and AI being used. Business Solutions practitioners convert pilots into rollouts, and adoption into measurable productivity, cost, or revenue impact.
