At the beginning of LDT 513, I already understood that training was not always the answer. What changed throughout the course was my understanding of how much evidence and how disciplined an investigation needs to be before I can confidently determine the right solution. Needs assessment has changed the way I view the role of a learning designer. Our responsibility is not simply to create effective learning experiences; we must first determine whether learning is actually the solution. This requires defining the performance gap, gathering evidence, identifying root causes, and separating what the data tells us from what we think the organization should do next.
In my current role at American Express, one of the most important things I have learned when speaking with a client is to listen to their entire need rather than jump to conclusions and start thinking about solutions after hearing only the first 90 seconds of the conversation. The same principle applies when stakeholders bring a problem or situation to a learning designer. My job is not to immediately start designing a solution but to dig deeper, ask the right questions, gather evidence, and identify the root cause at the center of the problem.
My experience with leadership is that they often want a solution developed yesterday because they are under pressure from lagging sales numbers, missed quarterly expectations, high colleague turnover, or low customer and colleague satisfaction scores. The Five Steps of Needs Assessment gave me a structured process for diagnosing performance problems and taught me to resist the temptation to mistake the most visible symptom for the root cause. That makes the next part difficult to sell, but it is necessary: we have to slow down the move from problem to solution long enough to gather the evidence.
The Forbin International case reinforced this lesson for me because rushed equipment training initially seemed like an obvious cause of the production and safety problems. However, the needs assessment required me to look beyond that first assumption and investigate inconsistent safety expectations, limited feedback, equipment reliability, workload, communication, and other workplace conditions before making recommendations. I now see diagnosis as a process of gathering, comparing, and interpreting evidence to determine which intervention makes the most sense.
Rather than immediately asking, “What training should we create?” my first question has become, “What is causing the performance gap, and what does the evidence tell us we should do about it?”
One of the biggest lessons I learned in this course is how to better distinguish between a true training need and a performance problem that training cannot solve. I now look at whether employees lack the knowledge or skills needed to perform the job or whether something in the work environment is preventing them from performing successfully. In the Forbin International case, employees clearly needed more hands-on practice with the new equipment, which supported the need for a training solution. However, other issues, such as inconsistent safety expectations across shifts, limited supervisor feedback, workload, communication, and unclear procedures, could not be solved simply by putting employees through another course. My needs assessment plan helped me recognize that factors such as staffing, equipment reliability, policies, supervision, workflow, and communication systems should be treated as non-training performance factors rather than automatically labeled as learning gaps.
This changed the way I think about the role of a learning designer. The goal is not to prove whether training is needed; it is to determine where training can make a difference and where another type of intervention is required.
Forbin ultimately demonstrated that the answer could be a combination of both. Hands-on equipment training and supervisor coaching addressed knowledge and skill gaps, while standardized safety procedures and point-of-use job aids addressed broader workplace conditions. Instead of viewing every performance problem through a training lens, I now ask myself, “Is this something employees do not know how to do, or is something preventing them from doing what they already know how to do?”
A recent example from American Express reinforces this point. The company is transitioning from Webex to Microsoft Teams, and a performance issue surfaced during client meetings. Some customer-facing colleagues had difficulty connecting their headsets to Teams because the setup process is slightly different from Webex. Leadership initially assumed this was a training issue and recommended reviewing the existing training materials. That review helped correct a few problems, but the larger issue remained: the audio and video were out of sync, creating a noticeable delay between what participants heard and what they saw on screen.
Further investigation showed that the problem was not primarily a training gap but a system issue. Customer-facing colleagues are required to use a VPN-style calling program that records conversations, and that software must also connect with Teams. The interaction between the two systems was causing the audio and video delay. This example reinforced the importance of diagnosing the performance problem first. Training can address a knowledge or skill gap, but it cannot fix a technology or system limitation.
My understanding of evidence has also grown throughout this course, especially in how evidence is collected and interpreted. I found semi-structured interviews particularly valuable because each participant was asked the same initial questions, creating consistency, while follow-up questions could be tailored to their specific role and responses. In the Forbin case, this approach allowed me to compare perspectives among employees while still delving deeper into their individual experiences.
I also learned that one comment does not automatically become a finding. Evidence must be compared across participants, analyzed for recurring themes, and, whenever possible, supported by other sources. This helped me better understand the progression from data to themes, from themes to findings, from findings to conclusions, and from conclusions to recommendations.
What I learned in this course will change how I approach both my current work and my future role as a learning designer. Instead of beginning with, “What should we create?” I want to begin with, “What problem are we trying to solve?” I will use the needs assessment process to define the performance gap, involve the right stakeholders, gather evidence from multiple sources, and determine whether the root cause requires training, a non-training solution, or a combination of both.
Most importantly, I want to bring a more consultative mindset to learning design. A stakeholder may ask me for a course, video, or job aid, but my responsibility is not simply to build what was requested. My responsibility is to ask the right questions, challenge assumptions when necessary, and use evidence to recommend a solution that will actually improve performance. This course has helped me see that sometimes the most valuable thing a learning designer can do is determine that training is not the answer, or that it is only part of the answer.
AI Disclosure Statement: I used ChatGPT, an artificial intelligence tool developed by OpenAI, to assist with grammar, sentence structure, organization, and improving the overall flow of this blog post. The ideas, professional examples, course reflections, and conclusions presented in the post are my own.
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