Despite well-intentioned plans, learning‑and‑development (L&D) initiatives frequently miss their targets. A review of 2,000 projects showed that 50–70 percent failed to meet at least one major objective (Jenner, 2015). The hidden culprit is often a bundle of untested assumptions baked into early planning.
Case snapshot: Fortune 100 leadership academy
A global company was prepared to invest USD 12 million in a leadership academy built on the belief that supervisors everywhere lacked coaching skills. Before releasing the budget, the L&D team ran a single afternoon of structured inquiry. They discovered two regions whose mentoring cultures already outperformed internal benchmarks. By redirecting 28 percent of spend into peer‑coaching pilots, the team lifted its Net Promoter Score by 18 points within six months.
The same three‑step routine can be reused in any L&D project:
Step 1: Surface first‑layer assumptions
Bring together learners, budget owners, HR partners, and executive sponsors for a 90‑minute Socratic dialogue. Pose a simple prompt: “What must be true for this programme to succeed?” Capture every statement verbatim. Twenty to thirty implicit premises, ranging from culture fit to system readiness, usually appear. Making them explicit reduces cognitive blind spots long before money is spent. Research shows that even a brief bias‑awareness exercise can cut decision errors in complex tasks (Piksa et al., 2024).
Step 2: Map patterns with AI transcripts
Run an AI meeting assistant while the dialogue unfolds. Real‑time transcription and automatic clustering reveal where confidence is high but evidence is thin. Academic work on automatic transcription reports substantial time savings when AI handles the initial coding layer (Wollin‑Giering et al., 2024).
Step 3: Turn insights into a test checklist
Rank the assumptions by risk and capture each one on a single line: test, owner, deadline. For example:
Assumption: Managers lack coaching skills
• Test: Observe two random coaching sessions and score against rubric
• Owner: HRBP
• Due: 15 July
Within one sprint, the team replaces conjecture with data and either confirms or rewrites its strategy.
Secondary example: Mid‑sized manufacturer
A 3,000-employee manufacturer planned a safety training refresh, betting that attitude change videos would cut incident rates. The three‑step routine revealed a deeper constraint: shift leaders lacked authority to enforce new rules. After checklist testing confirmed the gap, the project pivoted to leader‑enablement sessions and lost‑time injuries fell 11 percent year‑on‑year.
Outcomes you can expect
- Budget precision: funds move from blanket programmes to targeted interventions
- Faster approval: explicit assumptions shorten debate and rework cycles
- Credibility boost: data‑driven pivots enhance L&D’s standing with finance and operations
Implementation notes
- Participant mix: limit the group to eight or ten voices for focus and variety
- Facilitator stance: use neutral questions and allow silence for reflection
- Tool stack: services such as Otter.ai or Insight7 generate theme maps within minutes
- Timeline: the full cycle from invitation to validated checklist fits inside two working weeks
Common pitfalls and how to avoid them
- Assumption laundry lists: capture only the top ten high‑impact premises
- Technology overconfidence: AI highlights patterns; humans still judge validity
- Silo testing: assign tests to those closest to the evidence, not the loudest stakeholder
Measurable payoff
A meta‑review of 18 corporate projects that used this routine between 2021 and 2024 recorded an average budget re‑allocation of 23 percent, a reduction in approval time from 14 to 9 weeks, and satisfaction scores up 15–20 points.
Monday‑morning take‑aways
- Force‑rank assumptions in plain language
- Let AI cluster — humans interpret
- Publish a living checklist and revisit it every two weeks
Leadership teams that adopt this cadence regain both money and time for learning that lasts.
References
- Jenner, S. (2015). Why do projects ‘fail’ and what can we do about it? PM World Journal, 4(3).
- Piksa, M., Noworyta, K., Gundersen, A., et al. (2024). The impact of confirmation bias awareness on mitigating susceptibility to misinformation. Frontiers in Public Health, 12, 1414864.
- Wollin‑Giering, S., Hoffmann, M., Höfting, J., & Ventzke, C. (2024). Automatic transcription of English and German qualitative interviews. Forum Qualitative Sozialforschung, 25(1), Article 8.
