Turning Training Duplication into Decision-Ready Evidence
Hundreds of courses can be examined through one consistent analytical approach.
Overview
A UK Defence training organisation was reviewing a large and complex course portfolio. Its challenge was not simply to reduce course numbers, but to understand where training genuinely overlapped, where material could be shared or modularised, and where different delivery approaches might release capacity without weakening operational outcomes.
Agilexe applied AI-enabled portfolio analysis to turn dispersed course and objective data into structured, decision-ready evidence. The work gave senior stakeholders a clearer view of potential duplication and change opportunities while preserving ownership of curriculum decisions within the training organisation.
Challenge
The organisation’s portfolio contained hundreds of courses and thousands of Training Objectives developed across different schools and specialisms. Similar requirements were often expressed in different language, making comparison difficult through conventional spreadsheet or keyword analysis.
Leaders needed to identify:
- Potentially duplicated or closely related training.
- Material that could be shared between courses.
- Opportunities for modular or alternative delivery.
- The likely scale of each opportunity in training time.
- The operational context needed before any change could be approved.
The task required more than identifying matching words. Any useful analysis had to recognise semantic similarity, distinguish foundational knowledge from specialist competence, and present evidence that subject-matter experts could challenge and validate.
Solution
Agilexe configured its training portfolio analysis capability around the organisation’s Formal Training Statement and course data.
The capability compared Training Objectives across course boundaries, grouped related requirements, and presented potential areas of commonality through a visual, evidence-led dashboard.
Findings were translated into practical measures, including the proportion of analysed training represented and the theoretical training time associated with each opportunity.
Rather than prescribing course changes, the system presented a structured range of options. Training authorities could examine the underlying objectives, understand why they had been grouped, and decide whether consolidation, shared delivery, modularisation or no change was appropriate.
Impact
Analysis of the initial customer dataset identified approximately 24% shared potential across the courses examined.
This finding represented a decision-support opportunity rather than an assumed saving. The organisation retained control over which recommendations should progress, supported by clearer evidence of their likely effect.
The work demonstrated how large training portfolios can be reviewed at a strategic level without reducing complex curriculum decisions to a simple automated answer.
It provided a repeatable way to move from fragmented course data to prioritised, quantifiable opportunities for expert review.
Key Takeaways
Consistent analysis
Hundreds of courses can be examined through one consistent analytical approach.
Deeper comparison
Semantic comparison reveals commonality that keyword searches can miss.
Quantifiable opportunities
Potential change is quantified in curriculum and training-time terms.
Expert-led decisions
Training authorities retain ownership of every decision.
Explore What Your Training Data Could Reveal
Discover how Agilexe’s AI-enabled analysis capabilities can help identify opportunities across complex training portfolios, providing clearer evidence to support informed decisions.
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