TrainingZone

Organizations invest heavily in training, yet new research from Training magazine and Panopto reveals a growing disconnect between content creation and discoverability. Based on insights from 400+ L&D leaders, this report explores the business impact of the findability gap—the space between available training and employees’ ability to locate and apply knowledge when they need it.

I went to my doctor last week. Before I was fully in the office, they told me to take two aspirin and come back tomorrow, if it still hurts. I went back the next day. Same advice. Two more aspirin. After a few days I left my doctor. My old doctor probably still thinks their treatment worked. It sounds ridiculous, yet in L&D we can sometimes do something similar: prescribe the training before we have properly diagnosed the problem, and sometimes lose the employee along the way.

When people are asked to reskill, they are not just learning new tools. They are relearning how to feel competent. As AI accelerates the pace of workplace change, experienced professionals face a particular challenge: the skills that earned them credibility over years or decades offer little advantage in mastering unfamiliar technology. Research suggests many older workers actually want AI training, but the deeper struggle is rarely about willingness.

Organisations invest significant time, energy and money developing leaders. Indeed, the global corporate leadership training market is estimated at approximately US$44 billion in 2026. Leaders attend workshops. Organisations build competency models, launch leadership academies and provide books, online courses, assessments and coaching. And yet one of the most persistent challenges in leadership development begins after the programme ends.

Leadership programmes tend to be highly effective at increasing knowledge. But they don’t always succeed in providing the practical tools needed to help people develop behaviours that could improve their leadership. I discovered this during my 35 years in management working in both the private and public sectors. In the end I went outside the workplace to learn and develop those tools.

Whether it’s navigating the opportunities and disruption created by AI, responding to geopolitical uncertainty or managing workforce fatigue, today’s directors are being asked to make increasingly complex decisions against an ever-changing and challenging business landscape. Doing so effectively requires more than technical expertise. It requires leaders to understand how people think, feel and respond, as well as to recognise the impact their behaviour can have on those around them.

Science, technology, engineering and maths (STEM) touch almost every part of business. From AI to energy, healthcare and finance, few leaders are insulated from the opportunities and complexities STEM creates. Yet organisations (particularly those outside of technical industries) do not always view a STEM background as a marker of leadership potential among current or future employees. In fact, many capabilities from science and engineering translate remarkably well into business leadership.

Organisational change inevitably creates uncertainty and people naturally start to worry about what that change means for them personally. They begin to ask what it could mean for their role, their team and their future. We experienced this at HSS Training while the wider HSS Hire Group was going through restructuring. Questions began filtering up through the business and people understandably wanted to know what it meant for them.

AI is rapidly transforming the workplace, creating new opportunities for productivity, innovation and growth. Yet as organisations invest heavily in AI technologies, a critical challenge is emerging: workforce capability is struggling to keep pace. Research from the Chartered Institute of Marketing (CIM), conducted with YouGov, highlights a growing disconnect between boardroom ambitions and employee readiness.

Recently I conducted a research project that required reading and synthesising large volumes of source material. First, I scanned each source on my own, no AI allowed. I took my own notes and relied upon my own sense making before I opened the tool. It was slow and often frustrating, but that’s the point. I needed to know what I thought before a model influenced me. Only then did I bring AI in. Not to do the work but to challenge myself and find what I missed. Why did I do this?