Scenario-Driven Leadership Training.

Leadership programs are often organized around topics or competencies, tied to the organization’s strategy or a leadership competency model.

Coaching. Accountability. Strategic thinking. Developing others. Leading change. Enterprise mindset.

All of those capabilities matter. But leaders rarely deploy them one at a time. They experience them bundled together inside difficult decisions.

Consider this situation: A high-performing employee is ready for a cross-functional opportunity, but losing that person now would significantly hurt the team’s results. What should the leader do?

That is not simply a “coaching” problem. It can’t be met with coaching skills alone.

It involves developing people, succession, internal mobility, accountability for near-term performance, enterprise thinking, and perhaps even the leader’s willingness to absorb some personal risk for the long-term good of the organization.

And that is exactly why it can be such a powerful learning experience.

Instead of building leadership training around isolated competencies, we should build it around the recurring decisions and tensions leaders actually face — and then strengthen the relevant competencies.

Leadership happens when two good things compete.

Many leadership decisions are difficult — not when there is a clearly right option and a clearly wrong one. That’s an easy decision. They are difficult when two legitimate priorities are in tension.

On a recent leadership development project, we worked with our client’s leaders to identify exactly these kinds of moments. They posed questions like:

• What is best for my team is not necessarily what is best for the enterprise. Which should take priority?
• I can make the decision myself or let my people make it. How much autonomy should I create?
• Speed is one of our strategic imperatives. And we can move faster, but doing so introduces more risk. When should we slow down, stop, or escalate?
• I need results now, but developing my team will take longer. When should I coach rather than simply get the work done?
• The existing approach works, but there might be a better one. When should I preserve stability, and when should I push for change?
• I do not have all the information I want, but waiting has consequences too. When is there enough information to act?
• A strong performer is delivering results, but their behavior is damaging the team. What counts as successful performance?
• A change is good for the enterprise but disruptive for my people. What is my responsibility to both?

These real things our clients struggle with reveal an important truth: competencies tell leaders what they should be capable of doing. Real situations force them to decide when, how, and at what cost to apply those capabilities.

Design the module around the dilemma.

Traditionally, we start with a competency, like “Developing People.” We teach the skills involved, a framework for development planning, and eventually use realistic scenarios to practice some of the skills, like coaching conversations.

Now, turn that inside out.

• Begin with a situation the leader might find themselves in, like, “One of your high performers wants a developmental move. Would you let them go?”
• Then complicate the situation. The employee is critical to an important deliverable. There is no obvious successor. This new role would be good for the employee and good for the company. But the leader’s performance goals are still on the line.
• Work through the options, teaching elements of coaching, succession planning, mobility practices, enterprise thinking, delegation, accountability, and talent development.

You’re teaching capabilities, but each capability now has a reason to exist. It helps the leader make a consequential decision.

From a design perspective, you’re using real-life scenarios and teaching skills that support it, not teaching one skill and finding a scenario that requires that skill. You’re turning design inside out.

The leaders in our client project framed this tension as “today’s performance versus tomorrow’s capability“ and used it to pressure-test whether the organization’s leadership principles and competencies actually provided useful guidance.

That is a much stronger foundation for learning than starting with a list of competencies and looking for activities to teach them.

Start leadership program design differently.

Begin with discovery.

Don’t begin by asking, “What leadership topics should we cover?” Instead, ask, “What are the hardest recurring decisions our leaders have to make?”

Talk to leaders at different levels. Interview executives, managers, HR partners, and people who regularly work across functions. Look for moments when priorities compete and reasonable leaders might make different choices.

Ask questions such as:

• Where do leaders most often get stuck?
• What decisions create the most debate?
• Where do good intentions lead to inconsistent behavior?
• What do leaders tend to optimize for — their team, the customer, the quarter, or the enterprise?
• Where do you wish leaders exercised more judgment instead of simply following a rule?
• What situations separate your strongest leaders from everyone else?

The goal is not to collect dramatic one-off stories. It is to find recurring tensions.

One client team, for example, surfaced tensions involving speed versus risk, local performance versus enterprise value, control versus empowerment, present delivery versus future capability, standardization versus local judgment, certainty versus timely action, and candor versus commitment after a decision has been made.

Those tensions can become the architecture of the program.

Map the capabilities to the decisions.

Once you have the real situations, ask, “What does a leader need to know, believe, notice, and do to navigate this well?”

Take a decision like this: “My team can accelerate delivery by bypassing a process that feels unnecessarily conservative, and the risk seems manageable.”

Now several leadership capabilities become relevant at once: risk assessment, courage, accountability, understanding guardrails, escalation, and balancing urgency with trust.

In building training around this scenario, you’re not only teaching leadership capabilities; you’re pressure-testing organizational principles, creating clarity, and enabling action.

The competency model still matters. In fact, it becomes more useful. But it moves from being the table of contents to being part of the toolkit leaders use to work through realistic decisions.

Make the practice feel consequential.

A good scenario should not have an answer that becomes obvious after 30 seconds. It should contain legitimate competing priorities, require judgment, and force the learner to face the consequences for different stakeholders.

For example:

• A leader initially decides to keep the high performer on the team for six more months. Then new information arrives: The employee has been approached by another company. What now?
• The leader decides to support the move. Then the team misses an important milestone. How does the leader explain the decision? What should have been done earlier to build bench strength?

What responsibility does the leader have for the consequences, based on each decision? This is where leadership development starts to resemble leadership itself.

The goal is not to teach the scenario.

Of course, leaders won’t encounter the exact same case after they return to work. That’s not the point. The scenario is a practice field – it helps leaders develop ways of thinking they can reuse when the details change.

A well-designed module might leave them with questions such as:

• What am I optimizing for?
• Which stakeholder am I overlooking?
• Is this a short-term problem or a long-term one?
• What risk am I accepting by acting or not acting?
• Which decisions should I make myself, and which should I delegate?
• Am I protecting my team, or building capability for the enterprise?

Those questions travel.

Build leadership training from the work backward.

There is still a place for leadership frameworks, competencies, and tools. But they should support the work, not organize it.

Start with what leaders actually have to decide. Find the recurring tensions. Build realistic scenarios around them.

Then bring in the coaching model, decision framework, communication technique, leadership principle, or competency that helps leaders navigate the situation well.

Because leaders do not experience leadership as a series of topics. They experience it as a series of choices, and our leadership programs should prepare them accordingly.

What the Best Frameworks Get Right.

Let’s be honest: Somewhere out there, another organization has a competency model that feels cleaner, sharper and more actionable than yours.

The roles are clearly defined. Skills, behaviors and proficiency levels are transparent. Development paths make sense. There are learning programs that support it. Managers actually use it. Employees understand it.

And meanwhile, in your organization … the competency model is either generic, outdated, overly complex, inconsistently applied, or still sitting somewhere in draft form.

If that sounds familiar, you’re not alone.

The Quiet Struggle Behind Competency Models

Competency models sit at the intersection of strategy, talent and behavior, which is exactly why they’re so hard to get right.

They’re expected to:

• Define what “good” looks like
• Align with business strategy
• Be usable for hiring, development and performance
• Work across roles, levels, and geographies

That’s a tall order.

Much like change management, organizations often jump to the visible elements — wordsmithing competencies or building frameworks — without first building the underlying structure that makes them usable and sustainable.

The result? Models that look great on paper but don’t drive results.

So, if you’ve ever felt “competency model envy,” you’re not alone. Most organizations are still figuring this out. That’s why we spoke to Shari Chernack, Chief People Officer at Isaacson, Miller and advisor on talent and transformation, who has seen a lot of competency models succeed and fail. She revealed that the best competency models have seven elements in common.

What the Best Competency Models Get Right

Strong competency models are designed for action. Here’s a practical framework to guide you:

1. Clarity Over Completeness

The best competency models resist the urge to include everything.

Organizations often start with a core set of competencies but gradually add technical competencies and role-specific requirements until the framework becomes too large and difficult to use. Strong competency models focus on:

• 6-12 core competencies per role or level
• Clear, distinct definitions
• Minimal overlap
• Consistent organizational needs

If managers can’t explain the competencies without looking them up, the model is too complex.

If your organization needs a more sophisticated model, start small and evolve it over time. Progress matters more than perfection. Set the right foundation, then iterate.

2. Behavior-Based (Not Buzzword-Based)

“Strategic thinking”
“Collaboration”
“Leadership presence”

These sound valuable, but without observable behaviors, they are difficult to apply consistently.

Competencies can oftentimes be so broad and high level that employees don’t even know what they mean. Employees need clarity on what competencies mean within their specific roles.

Strong models define:

• What the competency looks like in practice
• What strong performance looks like
• What falling short of expectations looks like

This is where models shift from abstract to usable.

For example, “strategic thinking” becomes: “Anticipates future conditions and translates insights into priorities, trade-offs, and actions that drive performance.” Or “Weighs short-term wins against long-term impact and makes explicit trade-offs.”

3. Connected to Organizational Values and Differentiators

One of the most common challenges organizations face is understanding where competency models fit within the broader talent ecosystem.

Organizations already have mission and vision statements, core values, skills taxonomies and business goals. Without clear alignment, competency models can feel like just another framework layered on top of existing processes.

Competency models operationalize your values. Or, as Chernack says, “The values are how we live and the competencies are how we work.”

Competencies translate values into specific, observable behaviors that employees can understand, apply and develop. Competencies answer the question: What does this value look like in action?

Strong competency models make this connection explicit. For example, Chernack recalls a time where she rolled out a competency model in-house. “It actually laddered right up to our core values,” she said. “So it was the behaviors underpinning the values and then everything that we did around leadership development was tied to those competencies.”

The best competency models also reflect what makes an organization uniquely successful.

Organizations should avoid borrowing external competency frameworks or relying on generic industry language. The result is a model that could belong to almost any company, and that’s not what you want. Competency models should reflect what drives success inside the organization itself.

To identify those differentiators, organizations can examine top performers and ask:

• What do our best people do differently?
• What behaviors led to our biggest successes?
• What behaviors do we want others to replicate?

Behavioral interviews and structured discussions with high performers can help uncover these patterns.

When competency models align with both organizational values and real drivers of success, they become more than a framework. They become a system that connects culture, performance and strategy.

4. Aligned to Real Work

Competencies should be specific and grounded in practical examples and role-specific expectations. They should reflect how work gets done. That means:

• Grounding competencies in real job tasks
• Incorporating input from high performers
• Reflecting the realities of different employee groups

For example, what “communication” looks like for a corporate employee may be very different from someone in the field, and your model should reflect that.

A broad communication competency might read: “Conveys information clearly, concisely and with intent. Adapts message, tone and medium to drive understanding and action.”

For a corporate executive: “Synthesizes complex information into clear messages that align diverse stakeholders and drive decisions and execution.”

For a field team lead: “Delivers clear, timely and actionable information that drives team understanding, performance and consistent execution.”

5. Scalable Across Levels

A strong model shows progression. Instead of rewriting competencies for every level:

• Use consistent competency categories
• Differentiate by depth, scope, and complexity
• Show how expectations evolve from entry-level to leadership

This creates clarity around growth and makes career paths visible.

For example, a new leader’s strategic thinking competency might be, “Recognizes patterns in day-to-day operations and makes proactive adjustments to improve near-term performance and team effectiveness.”

For a senior leader, it could be: “Interprets broader business trends and cross-functional dynamics to set direction, allocate resources, and drive long-term performance across multiple teams or locations.”

6. Launched, Adopted, Integrated

If your competency model lives only as a PDF, it’s already failing.

Chernack has seen good competency models go to waste. Many organizations invest in developing competencies, but they don’t invest in rolling them out. This is where change management helps. Employees and managers need to understand the purpose of the model and how to use it.

It is also helpful to involve people closest to the work during development and rollout. Competency models built without input from employees and managers often struggle to gain traction.

The best models are integrated into:

• Hiring and interview guides
• Performance management processes
• Learning and development programs
• Succession planning efforts

Think of competency models as infrastructure, not documentation.

7. Easy to Use Daily

The ultimate test to determine if a competency model is good is whether a manager can use it in a one-on-one conversation without preparation. If not, simplify. Because adoption is what drives value.

How to Get Started

You don’t need a six-month project to build a better competency model. In fact, speed can be your advantage. Here’s a practical way to get traction fast:

Step 1: Start with one role (or job family).

Pick a high-impact role or a role undergoing change. Build your model there first, then expand.

Step 2: Identify top performers.

Ask:

• What do the best people in this role do differently?
• What behaviors drive success?

Focus on reality, not theory.

Step 3: Define core competencies.

Group knowledge, skills, abilities and behaviors into themes:

• Decision-making
• Communication
• Execution
• Collaboration
• Leadership (if applicable)

Keep it tight.

Step 4: Write observable behaviors.

For each competency:

• Define 3-5 clear behaviors
• Make them specific and actionable
• Avoid jargon

If you can’t observe it, it’s not a behavior.

Step 5: Validate with managers.

Before rolling anything out:

• Test it with people leaders
• Ask: Does this accurately reflect what we want for this role? Would this be valuable in hiring or performance management?
• Refine based on feedback

Remember: usability beats elegance.

Step 6: Pilot and iterate.

Treat your model like a product:

• Launch it in one area
• Gather feedback
• Improve it

The best competency models are built over time, not in a single workshop.

Progress Over Perfection

Competency models often feel like something organizations need to “get right” before launch. They don’t.

Organizations that make the most progress treat competency models the same way they treat any change. They start with a clear structure, focus on what matters most, and build momentum through use.

Success doesn’t come from winning over everyone immediately or perfecting every detail upfront. It comes from building something practical that employees and managers will actually use.

So, if you’re feeling competency model envy, take it as a signal — not of failure, but of opportunity.

Because the gap between “what exists today” and “what is needed tomorrow” is exactly where better design (and better outcomes) begin.

Want to explore this topic in more detail or learn more about Emerson? Hop on his calendar: Book a meeting with Rich

How I use my team of (secret) agents.

When I wrote my blog post, AI Tools and Tips for Beginners, I was aiming to show others how I use Gen AI to improve my efficiency and quality of work.

But Gen AI has changed drastically since then. Hastily produced AI outputs in our personal and professional lives have given me AI fatigue. I feel more skepticism of content than I used to. I find myself asking, “Other than the prompt, was a human involved during the creating this?”

As a result, the way I work with AI has evolved. So I’d like to revisit the topic: How do I use Gen AI to improve my efficiency and quality, while keeping it authentically “me?”

My Team of Custom Agents

For most projects, I “staff” my team with custom agents. It allows me to keep my prompts and outputs organized and keeps the custom agents focused on the specific tasks I need to complete my work.

For an eLearning project, I typically will design a custom agent for each of these roles:

• Project Assistant / Thought Partner
• Editor
• Technical Expert

As a source of entertainment, I like to give each of them a personality and a picture. Not required, of course. 😊

Team Member 1: Project Assistant / Thought Partner

Purpose

• Helps organize your thoughts.
• Suggests ways of presenting content.
• Completes time-consuming tasks that slow down development time.
• Generates knowledge-check questions at various difficulty levels.
• Drafts feedback text for correct, partially correct, and incorrect responses.
• Builds branching scenarios with decision trees, consequences, and scoring rules.
• Turns decision trees into a table that maps nicely to Storyline slides, layers, and triggers.

Example Prompts

• “Here are my learning objectives. Generate 10 knowledge check questions at mixed difficulty levels.”
• “Create a branching scenario with three decision points. Include consequences and scoring.”
• “Turn this decision tree into a table that maps to Storyline slides and layers.”
• “Based on this topic, suggest three interaction types and why they would work.”

Pointers

Since my way of working is to throw a bunch of thoughts and ideas at the wall and see what sticks, this agent is instrumental in helping me organize thoughts and come up with a shell for the eLearning. I constantly ideate with it to get an output I am happy with.

Team Member 2: Editor

Purpose

• Writes on-screen copy that matches a specific tone.
• Drafts voiceover scripts that map to each slide or interaction.
• Generates microcopy: button labels, instructions, error messages, and branching feedback.
• Helps with grammar, sentence flow, and the like.
• Does not rewrite my content; simply does an editing pass.

Example Prompts

• “Do a clarity pass on this copy. Keep my tone and do not rewrite it from scratch.”
• “Give me options for this sentence. Keep it conversational and direct.”
• “Tighten this paragraph for on-screen reading. Keep the meaning the same.”
• “Rewrite this for a different audience: new managers. Keep it simple and specific.”
• Or my favorite “No, you’re adding too much fluff. Less fluff. This is my stuff!”

Pointers

Be nitpicky with the output you want. A common trick is to tell the agent, “give me five options” then select a few that work for you. From there, adjust to your liking and run it through the editor to spot-check for errors in grammar, flow, etc.

Team Member 3: Technical Expert

Purpose

• Helps when I have an idea in mind but cannot think of a straightforward way to prototype or build it.
• Has broad knowledge of eLearning authoring tools.
• Takes a first pass at JavaScript, HTML, or CSS code if needed.
• Verifies and helps troubleshoot hurdles I’m experiencing.
• Helps translate build ideas into steps that improve efficiency and user experience.

Example Prompts

• “Here’s what I want the interaction to do. What’s the simplest way to build this in Storyline?”
• “Help me brainstorm ways I can develop this interaction with very few end user clicks.”
• “Draft JavaScript that will complete this function: (Enter what you’re trying to accomplish here).”
• “Troubleshoot this issue. Here’s what I tried and what happened.”
• “Here are two build approaches I have for this interaction. Why would I opt for one over the other?”
• “Here’s a screenshot of all my variables, please create a (variable name).”

Pointers

As with the others, always scrutinize the output you get. Keep working on your prompts — this is a skill that you will need to develop too!

My Workflow

Here’s an example scenario:

• I gather all the content and materials I’ve obtained from the project team (humans, not agents).
• I create a chicken scratch storyboard (throwing a bunch of pasta at the wall).
• I go to the Project Assistant to help plan the build (scenario, branches, learning objectives, structure).
• I pass it to the Editor to fix copy and help the language flow.
• I create a draft storyboard using content created by me and vetted by the first two agents.
• I take another close look at the copy and make edits based on my preferences and the preferences of my client.
• I send it to the project team for QA and feedback.
• I apply the feedback.
• I send it back to the project team for another quick QA, then send it to the client. (Usually at this point, the technical aspect is still very simple and straightforward.)
• After everyone’s approval, I take the storyboard and ask what would the build look like in a perfect world?
• I use the Technical Expert to help put it on paper. After a few iterations, the storyboard copy is complete; I send the next draft to the client with the visual ideas (i.e., they click here, they enter this, they drag and drop this…).
• Once the storyboard is finalized, I begin developing the course prototype or mockup.

The Bottom Line: Keeping It Authentically Me

I’ll leave you with two tips:

1. Start out right. Prompting is a skill that you must build – as you continue ideating with your agents, you will build confidence and improve your prompt outputs.
2. Use your agents. Constantly ideate with your agents. And give your agents a chance to help you become more efficient.

I’m a big fan and constant user of Gen AI. Learning professionals should commit to learning how to use it better as AI – and humans – evolve.

What Businesses Need to Know.

For learning and development (L&D) leaders, AI offers both disruption and opportunity. It’s reshaping how organizations design, deliver, and personalize learning programs.

But AI isn’t just a tool; it’s a catalyst. It’s shifting the role of learning from a static curriculum to a dynamic, context-aware ecosystem. That change requires thoughtful leadership and a strategic lens.

Here’s a closer look at the trends shaping AI-enabled learning, the pros and cons, and practical guidance for those looking to deliver the very best to learners.

TREND 1: The Rise of Agentic AI in Learning

Imagine an AI agent that watches a learner do an activity in a work simulation, adjusts the activity’s difficulty based on what the learner does, and offers custom feedback pulled from the organization’s best practices.

That’s not science fiction. Organizations are already using agentic AI: autonomous agents powered by large language models (LLMs, like Chat GPT) that can perform complex tasks with minimal human direction.

These AI agents can do things like:

• Serve as personalized virtual coaches or tutors.
• Simulate decision-making scenarios in real time.
• Guide people through learning paths based on their performance and preferences.

Cool. But why use them? AI learning agents can reduce the burden on human facilitators, increase scalability of learning programs, and enable more just-in-time, responsive learning.

But they also require oversight — both ethical and instructional — to make sure the learner’s experience aligns with learning objectives and organizational values.

TREND 2: Generative AI for Content and Graphics Creation

Instead of spending weeks developing a deck, what if your instructional designers could prompt AI to create a first draft, then refine it based on audience needs?

Generative AI — tools like ChatGPT, DALL·E, and others — has unlocked new ways to design and develop learning programs.

For example, L&D teams are using AI to:

• Draft learning objectives, course outlines, and microlearning content.
• Generate infographics, diagrams, and training visuals.
• Translate and localize learning materials faster and more affordably.

This saves time and money. It’s especially good for fast-moving organizations that have to train employees on tools, policies, or compliance issues that are constantly changing.

Teams should use Gen AI carefully, though. If you lean on it too heavily, you get generic, impersonal learning. You need human instructional designers to assess learning needs and ensure relevance, nuance, and tone. AI can create content and , but it can’t (yet) think like your learners or reflect your culture.

Why are organizations leaning into learning AI?

1. Speed and Scale: AI helps learning teams move faster and serve more learners without increasing L&D headcount.
2. Personalization: AI enables content and experiences to adapt in real time to learner needs, improving engagement and retention.
3. Data-Driven Insights: AI can turn learning analytics into actionable conclusions. For example, it can identify where learners struggle most and why.
4. Cost Efficiency: Automating lower-value tasks (like building quizzes or frees up humans to focus on high-value learning strategy, planning, and design.

What should we watch out for?

1. Technical Quality: AI-generated content can be inaccurate or misleading. It needs a human reviewer. This is a big risk for programs like compliance or technical training.
2. Image Quality: Some tools also have a long way to go before they can generate custom images that match your needs. You might get two-tailed dogs or graphics that come close, but aren’t what you want, even after a lot of direction.
3. Equity and Bias: AI tools can reflect and amplify bias present in training data, potentially reinforcing stereotypes or excluding certain learner perspectives.
4. Over-automation: Delegating too much to AI can diminish human connection, which is critical in programs like leadership development and behavior change.
5. Data Privacy: As AI tools collect and process learner data, organizations must stay vigilant about data security and compliance.

Here’s our advice.

AI has incredible potential to enrich learning, but it also raises critical questions. Here’s how to approach it strategically:

Start with purpose.

Avoid the trap of adopting AI for AI’s sake. Ask: What learner experience are we trying to improve? What business outcome are we driving? Then explore how AI can accelerate that result.

Keep humans in the loop.

AI should augment, not replace, the expertise of learning professionals. Use AI for generating ideas, content, and information, but leave strategy, design, reviews, and facilitation to the humans.

Do it ethically.

Build a governance model for AI in learning. This should include guidelines on transparency, accuracy, bias mitigation, and data use. Involve a variety of voices to pressure-test your standards.

Upskill your teams.

AI is here to stay, and L&D professionals need new skills like prompt engineering, AI tool evaluation, and content curation. Invest in upskilling your team so they can get the most of their AI tools.

Focus on behavior change.

AI can support learning, but it doesn’t drive behavior change on its own. Organizations still need thoughtful change management solutions to translate knowledge and skill into sustainable performance.

AI is not replacing strong learning leadership from humans. As tools evolve, the real differentiator will be your team’s ability to connect new technologies with timeless principles: clarity of purpose, learner empathy, and behavior-based design.

AI can help you write the script faster, but you still need to direct the play.

From Training to Performance.

Every year, government agencies invest heavily in training. They develop courses, fill their LMS platforms, and send employees through hours of instruction. Yet many leaders still ask: Why aren’t we seeing better performance results?

They’re seeing the gap between learning and doing.

In high-stakes, high-accountability environments like government, this gap isn’t just frustrating, it is lost opportunity to truly develop people potential and deliver on the promise of exceptional government services.

Agencies don’t just need people to know policies, systems, and processes; they need them to apply that knowledge accurately and consistently.

So, what’s getting in the way and how can we fix it?

The Challenges

Training in a Vacuum

Too often, government training is disconnected from daily work. Employees learn about systems or policies in theory, not in the context of real scenarios. That abstraction makes learning easy to forget and hard to apply.

Over-reliance on Information Transfer

We often assume that knowing leads to doing. But behavior science says otherwise. Knowing the steps to a process doesn’t guarantee following them correctly under pressure. Training that emphasizes knowledge over performance misses the point.

Lack of Reinforcement

Learning fades without follow-up. When there’s no coaching or accountability, people quickly revert to old habits, especially when those habits feel faster or safer.

Cultural and Systemic Barriers

Sometimes the problem isn’t training—it’s the environment. Employees may be trained to collaborate, but if performance systems reward individual output, they won’t change. When systems, leadership behaviors, and training are misaligned, progress stalls.

Bridging the Gap

Training and performance support must be designed with work performance in mind, from the start. Here are five ideas and two technologies to make that shift:

Meet people where they are.

The Five Moments of Need model by Bob Mosher and Dr. Conrad Gottfredson reframes learning as a continuous process. It identifies five key moments when people most need support: when learning something new, wanting to learn more, applying knowledge, solving problems, or adapting to change. The key is to align learning strategies to the moments of need. Formal training is important when learning something new, while on-demand performance support helps with application and change.

Example: A federal HR specialist uses formal training to learn a new hiring system (New), quick reference guides while processing applications (Apply), and updated resources when policy guidance changes (Change).

Build for behavior, not just knowledge.

Start with the end in mind: What do we want people to do differently? Define the critical behaviors, then design training that helps people practice and get feedback.

Example: Instead of lecturing on conflict-of-interest policy, simulate real scenarios where employees must make judgment calls and receive feedback in real time.

Align training with systems and culture.

If training teaches one thing but systems reward another, behavior change won’t stick. Before designing new learning, ask: What are we rewarding?

Example: Review performance metrics, SOPs, and feedback loops to ensure they reinforce the new behaviors, not the old ones.

Ground training in the real world.

Learning should feel familiar and actionable. Use real cases, language, and systems employees recognize.

Example: Partner with front-line managers to identify everyday challenges and build your training activities around them.

Reinforce with coaching and feedback.

Learning is a process, not an event. Incorporate follow-up discussions, manager coaching, and check-ins that show the behavior still matters.

Example: Create a “learning transfer plan” for supervisors to reinforce key behaviors during team meetings.

Make learning and performance support personal.

A Learning Experience Platform (LXP) delivers personalized, just-in-time learning tailored to each employee’s role, goals, and recent activity. It moves learning from compliance-driven to performance-focused. These systems are more affordable and capable than ever and provide a flexible, mobile resource tailored to the individual.

Example: A Contract Officers Representative (COR), updating a contract record might receive a recommendation for a short video on managing vendor performance — right when it’s most relevant.

Remove friction wherever you can.

A Digital Adoption Platform (DAP) provides step-by-step, on-screen guidance inside the applications employees use every day. This helps them perform tasks accurately without leaving their workflow.

Example: When a federal employee logs into a new acquisition system, a DAP can guide them through creating a requisition, explaining each field along the way. The result: faster adoption, fewer errors, and greater confidence.

The Opportunity Ahead

Government agencies face complex challenges, policy shifts, tight budgets, legacy systems, and evolving missions. But they also have a tremendous opportunity to lead with intention.

When agencies shift their focus from training delivery to performance enablement, they close the learning–doing gap. They create not just informed employees, but capable, confident professionals ready to deliver excellence where it matters most: on the job.

Want to explore the topic in more detail with John? Hop on his calendar: Book a meeting with John

What CEOs Should Really Expect from L&D (and from AI).

The pace of change is brutal. Tech evolution, shifting customer demands, workforce expectations, economic pressure. Strategy is no longer just about picking the right path. It’s about having a workforce ready and able to walk it.

So, what’s the lever that drives speed, consistency, and alignment across your people?

Learning.

Not learning just for learning’s sake. Learning as a strategic investment with a measurable return.

Learning sits at the heart of every business operation. It needs deliberate integration. For example, every company strives for innovation, but that won’t occur without change and change never sticks without learning.

Here’s the issue through another lens: the number one driver of turnover (especially in younger generations) is a lack of career and growth opportunities. One way to counter this is a continuous learning environment with dedicated competency models and role-based curricula.

In other words, learning enables strategy and change, while also helping to keep your A-players in-house, fortifying your future.

Let’s look at what this all means for CEOs, and how AI can elevate the value without replacing the effort.

Is learning a good investment?

CEOs are rightly focused on business outcomes. So, when they invest in learning, they expect results. The smartest leaders tie learning to their top business priorities:

• Faster execution of strategy – A roadmap doesn’t move itself; people do.
• Improved productivity and performance – L&D should uplift KPIs, not just report completion rates.
• Leadership pipeline – Developing leaders internally beats expensive external hires and maintains culture more consistently.
• Cultural alignment – Learning reinforces values, creates clarity, and shifts behavior.
• Customer experience – Upskilled teams create brand consistency and stronger service.

Having a deliberate learning focus elevates every facet of a strong, sustainable operating model. Learning isn’t a perk or a perk-up. It’s how you future-proof your organization.

Can AI help?

When it comes to AI, the temptation is real: “Can’t we just use AI to build the training and get it out there faster?”

Yes and no.

AI is a powerful tool in the L&D arsenal. It can speed up content creation, personalize learning at scale, surface insights from learner behavior, and provide just-in-time support in the flow of work. It can help answer questions like: Who needs what? Where are our skill gaps? How do we tailor this for different learners?

But AI can’t do everything. It doesn’t replace the hard work of intentional design, alignment to business strategy, and change leadership. And it certainly doesn’t replace the human connection needed for coaching, accountability, or cultural transformation. Without a human in the loop, training can be built faster, but does that matter if it’s not any good, or not relevant to your people?

So, while CEOs should expect their L&D teams to integrate AI, they shouldn’t expect AI to replace the investment in learning. In fact, AI takes time and a thoughtful strategy to be effective. When done right, AI should amplify the ROI of your investment in learning by lowering barriers, increasing relevance, and helping learners succeed faster through on-demand support and reinforcement.

Don’t think of it as getting the same with less investment; think of it as getting more with a similar investment.

If we do it right, what should we expect?

Here’s a simple way to think about it.

Outcomes CEOs should expect from AI-powered L&D:

• Faster time-to-competency
• Smarter prioritization of learning needs
• More engaging and adaptive learning journeys
• Scalable performance support tools
• Actionable insights from learning data

Outcomes CEOs should not expect:

• Cultural transformation without human leadership
• Engaging training without cultural context and relevance
• Behavior change without reinforcement and practice
• Strategic alignment and organizational impact from disconnected training
• Automatic ROI from AI-only solutions

If your goal is a workforce that’s ready to execute at the speed of strategy, you have to build, test, and embed learning. AI helps you scale brilliance, but it doesn’t create it.

What’s the elevator speech?

The organizations that win with AI aren’t the ones that cut the most L&D costs. They’re the ones that scale the smartest. Learning isn’t just a cost; it’s the path to capability, resilience, and competitive advantage. AI simply helps us walk that path faster.

Don’t fall into the trap of thinking AI is your L&D strategy. Instead, build a strategy that AI can elevate. One that ties to business goals, prioritizes behavior change, and invests in your people.

Because learning isn’t overhead. It’s infrastructure.

It’s not an expense. It’s a growth engine.

And when done right, with the help of AI, it’s one of the most future-ready investments a CEO can make.

Four Practical Tips for Lasting Behavior Change.

Artificial intelligence is no longer a futuristic concept; it’s a present-day force reshaping industries, job functions, and workflows across the board.

Organizations often invest heavily in technology but overlook the most important part of successful adoption: people. The trouble is, while AI tools evolve at lightning speed, human behavior doesn’t change overnight.

To make AI truly work for the business, companies must help employees shift how they think, work, and collaborate.

That means addressing the fears, habits, and uncertainties that often accompany technological disruption.

Here are four practical ways to lead your workforce through this transition, building confidence, capability, and commitment along the way.

Involve employees early and often.

Change is easier when people feel seen and heard. Involve employees early, during the design, testing, and rollout of AI initiatives.

Ask what they need, what gets in their way, and how they imagine AI enhancing — not replacing – their talents and contributions. Co-creation builds trust and fosters ownership, turning skeptics into champions.

Give them ongoing, practical education.

Demystify AI from the start. Many employees hesitate because they don’t understand what AI is or how it applies to them.

Provide hands-on learning that lets people explore AI tools within the flow of their actual work. Encourage experimentation in low-risk environments and embed education into everyday tasks. When people understand the tools, fear gives way to curiosity, and resistance turns into engagement.

Highlight tangible, everyday value.

People adopt new behaviors when they see clear benefits. Don’t leave AI as an abstraction — show how it solves real problems.

Demonstrate how it reduces repetitive tasks, or speeds up decisions, or frees time for creative and strategic work. Use whatever example resonates with the employee. Connect AI directly to day-to-day responsibilities and you’ll build momentum from the ground up.

Anchor AI to strategic goals.

Behavior change sticks when it’s aligned with the company’s broader mission.

Tie AI initiatives to strategic goals like improving customer experience, boosting productivity, or accelerating innovation. And communicate that alignment clearly. When employees see how AI supports the company’s direction, they’re more motivated to adapt and contribute.

AI disruption doesn’t have to feel like a threat. It can be a powerful catalyst for growth when paired with intentional, human-centered change strategies.

By equipping your people with the knowledge, tools, and context they need, you’ll not only ease the transition, but you’ll unlock the full potential of your technology investment. The future of work isn’t just automated. It’s collaborative, strategic, and deeply human.

Here’s how to be a force for proactive capability-building.

If you were to describe your L&D strategy as a profession, would it be a firefighter or an architect?

Don’t get me wrong – firefighters are essential to the health and safety of any community. They are also professionals; there is a science to effectively extinguish a structure or landscape engulfed in flames. But firefighting is a fundamentally reactive job. They have to wait until something bad happens, then solve it as best they can.

Too often, learning and development professionals feel like firefighters.

How do we break out of this endless cycle of putting out one “fire” after another? Is there another way?

Yes. You can move toward being a trusted, proactive partner with the business. You can anticipate business needs, support strategy, and get the workforce ready for what’s next.

Understanding Reactive L&D

Consider this: How many times has your organization encountered a skill gap or compliance issue, then rushed to find or develop training? Reactive L&D is characterized by short-term fixes that address urgent problems but rarely contribute to long-term growth.

Does your team suffer from reactivity? Here are some of the symptoms.

• Crisis-Driven Initiatives: Training programs are launched in response to an immediate challenge, such as an audit or a sudden new regulation.
• Lack of Strategic Alignment: There is little connection between training programs and the long-term goals of the organization.
• Inconsistent Outcomes: Rapid, one-off training sessions often fail to create lasting change, resulting in a cycle of recurring issues.
• High Operational Costs: Continuously responding to crises can be much more expensive than investing in a sustainable, forward-thinking strategy.

It can feel like being in a perpetual state of emergency—always putting out fires and rarely planning ahead.

Why Proactive L&D Matters

Shifting to proactive L&D means becoming the architect of the organization’s future capabilities. Proactive L&D integrates strategically with business goals, ensuring that employees are equipped to handle current demands and prepared for tomorrow’s challenges.

What do you get from a proactive approach?

• Agility: Employees have the skills they need before a challenge arises, making the organization more nimble and ready to adapt.
• Cost Efficiency: By investing in long-term capability-building, companies can reduce the recurring costs of emergency training.
• Higher Employee Engagement: Training programs that align with career growth and strategic goals naturally boost motivation and job satisfaction.
• Future Focus: Building capabilities proactively ensures that the organization is equipped to fulfill its strategies, like innovation and industry dominance.

The benefits cannot be overstated: Proactive L&D organizations have higher credibility and value to the business than reactive L&D organizations. A proactive L&D team does more than prepare the workforce for change—it actively shapes the future of their industry.

How To Shift from Reactive to Proactive

We would all love to be on that L&D team, right?  So, how can your L&D organization break free from the cycle of reactivity and start paving a path toward proactive capability-building?

Here’s a roadmap:

1. 1. Align with strategic goals.
      • Collaborate with leadership. Make your case for a seat at the table. L&D as a strategic partner is in everyone’s best interest. When L&D is involved in long-term vision and strategic priorities, learning efforts will support them and investments will align with them. At a minimum, understand your organization’s long-term vision and strategic .
      • Define key competencies. Identify the skills and capabilities that will be critical for achieving future goals.
   2. Conduct a comprehensive needs analysis.
      • Develop data-driven insights. Use performance metrics, employee feedback, and market research to anticipate future skill gaps.
      • Conduct future-focused surveys. Regularly assess emerging trends and technology shifts that might impact your industry.
   3. Embrace flexible learning design.
      • Create modular programs. Develop flexible and scalable training modules that can be updated as trends evolve.
      • Use blended learning. Combine digital, self-paced modules with interactive, instructor-led sessions to cater to diverse learning needs.
   4. Establish continuous feedback loops.
      • Establish ongoing evaluation. Implement learning analytics dashboards that provide real-time insights into program effectiveness.
      • Make iterative improvements. Use feedback to continuously update and refine training programs, so they stay relevant and impactful.

By following these steps, L&D teams can shift their focus from “fighting fires” to fueling sustainable, strategic growth.

Practical Tools for Proactive L&D

To truly transform your L&D strategy, use modern tools and frameworks. Here are a few to consider:

• Capability Maps: Visual representations of the skills and competencies required at various organizational levels.
• Skills Assessments: Regular evaluations that identify both current proficiencies and future needs.
• Learning Analytics Platforms: Tools that collect and analyze data to measure learning impact, engagement, and retention.
• Predictive Analytics and AI: Technologies that forecast skill trends and help personalize the learning journey for each employee.

Embracing these tools will boost training effectiveness and make sure training supports your business strategies and outcomes.

So, are you ready to transform your L&D approach? Shifting from reactive training to proactive capability-building requires a fundamental change in mindset and a commitment to long-term strategic thinking. Instead of waiting for crises to force your hand, design learning programs that anticipate challenges, foster innovation, and empower your workforce for the future.

Be the architect of your organization’s success—not just its problem-solver. Step toward a future where every training initiative is a strategic investment in your people and the organization’s long-term growth.
 

Want to explore the topic in more detail? We’d love to chat: Grab a virtual coffee together

It’s a partnership, not a replacement.

In the ever-evolving world of Learning and Development (L&D), artificial intelligence (AI) is no longer a futuristic concept; it’s here, and it’s reshaping the way we deliver training, support employee growth, and drive organizational success.

But let’s get one thing straight: AI isn’t coming for your job. Instead, it’s here to augment human capabilities, empowering L&D professionals to focus on what they do best: connecting with people and fostering meaningful learning experiences.

Use AI as an assistant.

One of AI’s most practical uses in L&D is automating time-consuming tasks. Tools like ChatGPT or xAPI-driven analytics platforms can summarize learner feedback, generate initial course drafts, and even provide 24/7 learner support.

For instance, imagine an AI-powered chatbot answering routine questions about a leadership development program, freeing up L&D teams to focus on creating impactful curricula.

This isn’t about replacing human expertise; it’s about using AI to handle the mundane so humans can tackle the meaningful.

Personalize at scale.

AI also excels at personalization, something L&D professionals have long sought but struggled to implement and manage at scale.

Platforms like Docebo or Cornerstone OnDemand use AI to analyze learner behavior, recommend relevant content, and even adapt learning pathways in real time. This ensures employees receive the right learning at the right moment, creating a tailored experience without requiring L&D professionals to sift through mountains of data manually.

Make smarter decisions.

AI’s analytical power can transform how L&D measures success. Tools like Degreed or learningpool use AI to analyze complex datasets, such as skill gaps and learning outcomes, and translate them into actionable insights.

These insights enable L&D professionals to refine programs and align them with business goals, ensuring a direct impact on organizational success.

Keep the “human” in human capital.

The secret to using AI with L&D is remembering that AI thrives when paired with human expertise.

While AI can process data and generate solutions, it lacks the emotional intelligence and cultural sensitivity to create impactful learning experiences. L&D professionals understand organizational nuances, foster empathy, and build trust. Those are things AI just can’t replicate.

The most successful L&D teams of the future won’t be those who fear AI, but those who embrace it as a partner.

By automating repetitive tasks, personalizing learning experiences, and providing actionable insights, AI allows professionals to focus on what really matters: enabling human potential.

The challenge isn’t whether or not to adopt AI. It’s how we, as L&D leaders, integrate it thoughtfully to elevate our impact. It’s time to shift the narrative: AI isn’t a replacement for humans. It’s the ultimate collaborator.

Use AI to improve, assess, and promote your learning programs.

Artificial Intelligence (AI) is kind of a big deal these days. It has revolutionized data-driven decision-making. Companies can now handle way more data, make faster and more accurate predictions, and even automate much of the analysis.

AI-powered models are so much better at managing complex, large-scale, and unstructured data than the old methods, which helps companies be more agile, precise, and deliver more personalized experiences.

Have you ever noticed that once you finish watching one episode of your favorite Netflix shows, a recommendation appears that you just can’t ignore? That’s because Netflix deploys AI/ML and data science to match your preferences with existing content on the platform. And it delivers results for Netflix; they are now connected with 260 million engaged users, accounting for a total revenue of $33.724 billion in 2023.

“But Sadé,” I’m sure you are asking, “what does that have to do with learning and development?”

Here are a few questions.

• What if you could create a truly personalized learning experience by tracking each learner’s journey and adapting content based on their interactions and progress?
• How much more valuable would your course be if you could track learning experiences across multiple platforms and even offline activities?
• What if you could easily integrate learning data with other business systems like CRM or HR platforms to get a more complete picture of a learner’s journey and performance?
• Would you like to capture the real-world application of learning (like learners using those skills on the job) and then use those data to measure the effectiveness of your course?
• What if you could quantify your learning program’s benefit to the organization?

Would detailed learning data help you make decisions and improve your learning organization’s programs?

xAPI can help.

xAPI, or Experience API (sometimes called Tin Can API), is a way to track and record learning experiences. It lets you capture learning activities from all kinds of platforms and situations, whether they’re happening online or offline. With xAPI and AI working together, you can track exactly how learning experiences impact job performance, identify which training activities deliver the most value, and make data-driven adjustments to your programs.

This means you can clearly demonstrate the effectiveness of your L&D efforts, tailor learning experiences to each individual, and ensure that your investment in training is driving real business results.

Here’s an example.

A global sales organization wants to improve the effectiveness of its sales training program. They want to know which training modules are most impactful, how long it takes for new hires to become productive, and how to tailor training to individual learning styles. And, of course, they want better sales performance.

They track learning:

• Formal Training. xAPI tracks traditional e-learning courses, webinars, and classroom sessions, capturing data like completion rates, quiz scores, and time spent.
• Informal Learning. xAPI monitors social learning interactions, knowledge sharing, and peer-to-peer mentoring in collaboration tools.
• On-the-Job Learning. xAPI tracks real-world sales calls, customer interactions, and performance metrics captured by the company’s CRM.

They use AI-Powered Insights:

• Predictive Analytics. AI analyzes xAPI data to identify patterns and predict future performance. For example, the system flags learners who are struggling with a specific module and recommends alternatives and new training.
• Personalized Learning Paths. AI creates custom learning paths based on individual learner needs, preferences, and performance data.
• Real-Time Feedback. AI-powered chatbots give fast feedback and guidance to learners, helping them stay on track and address knowledge gaps.

And they get results:

• Better Sales Performance. Learners are directed to the most effective training modules, and they get a tailored learning experience – what they need, when they need it. This means faster onboarding and upskilling, which boosts productivity.
• Data-Driven Decision Making. xAPI and AI help the organization decide on training investments and assign resources to training efforts.
• Enhanced Learner Experience. Learners get the right learning paths for them and get personalized feedback while they’re learning.

But how? Here are some next steps.

1. Find your why. Consider the highest priorities for your company, your business, and the learners you serve. How is L&D enabling these priorities? Can you measure that? What measurements are you missing? What compelling stories of the value of L&D would you like to tell? Asking yourself these questions will help to build a business case for data-driven learning programs.
2. Start making connections. Many companies have already begun embracing a data-driven culture and started exploring Artificial Intelligence, even in its infancy. Your company is likely one. Tap in. Find out who works with data in your company. Connect with that person, team, or department within your company. Introduce yourself, find out what they are working on, and what synergies are possible — now or in the future. Making these connections will help you begin to plan and organize around the work to come and your people.
3. Assess your current tech. What are the capabilities of your LMS? Does it support xAPI? Does your company already have a Learning Record Store (LRS) in place? What information can be pulled from your company’s LMS? Is it connected to employee data? Is your company using a unified Enterprise Resource Planning (ERP) suite such as SAP or Oracle? What about a CRM? Consider all the places you could gain information about the employee experience and their work performance. We’ll need this information later so we can begin to feed the AI models targeted information to receive helpful insights.
4. Identify Gaps. After completing steps 1-3, you will undoubtably found some gaps, whether large or small. You might identify gaps in technology, trackable data, in-depth technical knowledge, data analytic skills, or bandwidth to take on this change. But don’t be alarmed; you’ve taken the essential first step. You can’t fix what you don’t acknowledge.

Using data and AI will help you create more powerful learning programs, programs that explicitly show the value of L&D in your organization.