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People leave managers, not companies.
If you’ve worked on manager development, you’ve likely heard this before. It’s often used to explain turnover, engagement, or burnout. It also points to a deeper challenge: changing how people manage is hard.
Manager capability is reflected in how people respond in everyday work situations where context and relationships matter. Conversations about feedback, expectations, conflict, or tradeoffs under pressure rarely follow a script.
Capability develops when people are supported over time as they try new behaviors in real situations, see the consequences, and adjust their approach.
That’s easier said than done.
Connecting learning to performance depends on understanding where work breaks down, which capabilities matter most, and how those capabilities are reinforced day to day. This guide walks through how training programs are built to support that kind of change and produce measurable impact.
Start with needs analysis
Learning programs that change how people perform at work begin with a clear understanding of the problem they are meant to address. Needs analysis builds that understanding by focusing on capability and behavior in real work contexts.
This isn’t a quick exercise. It draws from multiple data sources, lived experience, and evidence of where performance breaks down. When done well, it creates focus and makes every design decision that follows easier.
- Define the performance context
Start with where the work happens and which outcomes matter most. Identify the capabilities most closely tied to those outcomes, and name the moments where performance makes the biggest difference.
Example: Managers are expected to set clear goals and support growth, but teams report inconsistent expectations and uneven feedback during quarterly planning cycles. - Gather evidence from multiple sources
Combine qualitative and quantitative inputs to understand what’s happening in practice. Talk with workers, managers, and Business Business Partners / People Partners, and review performance metrics, engagement data, help desk questions, and other recurring signals.
Example: A People Partner interviews 15 middle managers and reviews recent engagement data to understand where development conversations tend to break down. - Synthesize patterns across inputs
Look for themes that appear consistently across sources. AI can help cluster open-text feedback, summarize interview transcripts, and surface recurring patterns, while keeping links back to the original evidence.
Example: Survey responses and interview notes are analyzed together, revealing repeated challenges around clarifying expectations for new team members. - Validate insights with stakeholders
Share early findings with people close to the work to confirm accuracy and fill gaps. Focus on whether the patterns reflect lived experience and where behavior is most constrained.
Example: Managers confirm that goal-setting conversations are rushed and often avoided during busy planning periods. - Define clear capability statements
Translate insights into capability statements that describe observable behavior and decision-making in context. These statements guide learning design and clarify what success looks like.
Example: Instead of “improve feedback skills,” the capability becomes “apply structured, frequent feedback conversations that increase team confidence and reduce miscommunication.” - Align on success metrics early
Decide how you will know the program is working before designing learning experiences. Focus on signals that reflect behavior change and performance, not just participation.
Example: Fewer repeated questions about expectations, improved engagement scores related to clarity, and faster ramp-up for new team members.
Define the capabilities you want to build
A capability describes how people are expected to act, decide, and respond in real situations.
Clear capabilities give learning programs something concrete to work toward and something observable to measure against. They also create alignment early, before content, formats, or tools enter the picture.
Strong capability statements answer a few simple questions:
- In what situations does this matter?
- What behavior or judgment should show up?
- What does “good” look like in practice?
This clarity helps learning move beyond explanation and toward performance.
Example: In a manager development context, a capability might describe how someone approaches a feedback conversation, prioritizes work under pressure, or adapts their communication style based on the situation. That framing creates a direct line between learning and day-to-day performance.
Build capability through practice and feedback
Capabilities develop through use. People learn how to perform by trying things out, seeing the consequences, and adjusting their approach over time.
Effective learning programs make space for this cycle. They include opportunities to:
- practice decisions in realistic scenarios
- receive feedback from peers, coaches, or the system
- reflect on what worked and what didn’t
- repeat the behavior in slightly different contexts
These experiences don’t need to be complex. What matters is that they are intentional and tied directly to the capability being built.
Example: For feedback skills, learners might work through short scenarios that mirror real conversations. They make choices, see how those choices land, and get guidance on alternative approaches. Over time, this builds confidence and judgment that transfer more easily to real work.
Reinforce learning over time
Capability strengthens through repetition and reinforcement.
Learning programs that support real impact return to the same capabilities across multiple touchpoints. This might include:
- short refreshers at key moments
- follow-up scenarios that increase complexity
- peer discussions or cohort check-ins
- reflection prompts embedded into everyday work
This layered approach keeps learning present as conditions change and respects how people actually work, learn, and forget.
Example: A manager might revisit core capabilities at different points in their journey, such as when they become a first-time manager, take on a larger team (e.g., become a manager of managers) or transition into a new function.
Deliver learning where work happens
Once capabilities, practice, and feedback are clear, technology determines whether learning actually shows up at the right moments.
Effective learning programs use technology to reduce friction, reinforce consistency, and support reuse. The goal isn’t to add more platforms. It’s to place learning where people already work and return to it when they need reinforcement.
Here’s how teams do that in practice.
- Embed learning into existing workflows.
Learning is linked from tools people already use, such as internal knowledge bases, collaboration tools, or role-specific dashboards. Short lessons, scenarios, or refreshers are easy to access without leaving the flow of work.
Example: After a project kickoff, team leads receive a short refresher on setting expectations, embedded directly in their project workspace. - Use video for consistent explanation and reinforcement.
Video works well for shared language, demonstrations, and scenario walkthroughs. It gives teams a consistent reference point that can be revisited, shared, and updated as practices evolve.
Example: A short scenario video models how to handle a difficult conversation. Managers can watch it before a 1:1, then return to it later as a refresher. - Plan for localization from the start.
Delivery decisions should account for language, time zones, and cultural context early. Localization affects examples, scenarios, pacing, and tone, not just translation. For video, this often means subtitles or dubbing so content remains accessible and consistent across regions.
Example: The same scenario is reused globally, with subtitles or dubbing and region-specific examples layered in. - Support flexible pathways with shared structure.
Technology can help route people to what’s most relevant based on role, experience, or timing, while keeping the underlying capability framework consistent.
Example: First-time managers receive foundational content and guided practice, while experienced managers focus on advanced scenarios and peer discussion. - Design for reuse and iteration.
Learning assets are modular so they can be updated, recombined, or reinforced without rebuilding the entire program. This keeps learning current and reduces maintenance effort over time.
Example: A single scenario is reused across onboarding, refreshers, and cohort sessions, with updates applied once and reflected everywhere.
When these elements are aligned, reinforcement becomes part of the program. Technology helps make practice and feedback more consistent and accessible, while peer learning and live interactions provide the discussion and reflection that turn experience into capability.
Bringing it together
Learning programs that create measurable impact are built as systems. They connect real needs to clear capabilities, support practice and feedback, and reinforce learning where work happens.
Within that system, courses, one-off videos, scenarios, and exercises form the execution layer. They’re how capabilities show up in practice and how learning is experienced day to day. Their impact depends less on format and more on how clearly they map to real situations, observable behavior, and meaningful feedback.
This work takes intention and time. When it’s done well, learning stops feeling like an initiative and starts supporting performance in a way that lasts.
If you’re looking for a concrete place to start, try shaping one learning experience using a structured template like the one below (click edit to get started).
👉 For a deeper look at how to build these learning experiences in practice — including scripting, scenarios, and feedback design — check out this guide.
About the author
Learning and Development Evangelist
Amy Vidor
Amy Vidor, PhD is a Learning & Development Evangelist at Synthesia, where she researches emerging learning trends and helps organizations apply AI to learning at scale. With 15 years of experience across the public and private sectors, she has advised high-growth technology companies, government agencies, and higher education institutions on modernizing how people build skills and capability. Her work focuses on translating complex expertise into practical, scalable learning and examining how AI is reshaping development, performance, and the future of work.
What’s the difference between a course and a learning program?
A course is a structured learning experience with a defined scope and outcome. A learning program connects multiple learning experiences over time to build capability, support behavior change, and reinforce application in real work contexts. Courses are building blocks. Programs are how those blocks work together.
Why do many learning programs fail to create real impact?
Many programs focus heavily on content while underinvesting in practice, feedback, and reinforcement. When learning is disconnected from real work or treated as a one-time event, it’s unlikely to translate into lasting behavior change, regardless of topic or format.
What makes a learning program effective in the workplace?
AI video technology eliminates the traditional barriers to creating multilingual training content by allowing you to generate videos in over 140 languages from a single script. Simply write your training content once, then select different AI voices and languages to create localized versions in minutes rather than coordinating expensive reshoots with native speakers. This approach maintains consistent messaging across all markets while allowing for cultural adaptations in examples and scenarios that resonate with local teams.
The real power comes from combining this localization capability with template-based production. Create a master template with your company branding and structure, then clone it for each new training module and language variant. This systematic approach means a training video that once took weeks to produce in multiple languages can now be created, localized, and deployed globally within days, ensuring all employees receive the same quality training regardless of their location or language preference.
How do you measure the impact of a learning program?
Impact shows up through signals beyond completion rates. These include increased confidence, observable behavior change, reduced follow-up questions, faster time to competence, and performance outcomes tied to the original capability goals.
Do learning programs need to be personalized?
Yes. Research and practice both show that learning aligned to role, experience, and context is more relevant and more likely to transfer into work. Personalization helps learners focus on what matters most to them while still supporting shared standards and goals.
Should learning programs replace human coaching or peer learning?
No. Effective learning programs are designed to incorporate and amplify human coaching and peer learning, not replace them.
Programs provide structure, shared language, and consistent reinforcement. Coaching, feedback, and peer interaction bring that learning to life through reflection, discussion, and real-world application. When combined intentionally, learning programs support human moments that matter most while reducing the overhead of repeating explanations or starting from scratch each time.
This balance allows organizations to scale learning without losing the human elements that drive trust, growth, and behavior change.
When should learning programs evolve or be updated?
Learning programs should evolve as capabilities, tools, and contexts change. Programs built with clear structure and modular components are easier to update, allowing improvements to happen continuously without disrupting the overall experience.
