The gap between what’s trending at industry conferences and what’s actually being used at work is wider than ever.

While event agendas are packed with AI showcases, immersive reality, and buzzword-rich innovation sessions, we wanted to find out whether the day-to-day reality inside L&D teams is actually different. So, we asked 556 learning professionals what they’re really using and how long they’ve been using it.

What we found is both grounding and revealing. Beneath the hype is a practical, steady adoption curve, a few late bloomers finally having their moment, and, yes, some shiny new technologies.

What we did (& how it’s different)

Between February and March 2025, we surveyed 502 L&D professionals and 54 learning technology providers, covering 43 technologies. But here’s the twist: We didn’t ask what people plan to do or hope to implement. We asked what their organizations are actually using—and for how long. Then, by separating out provider responses from L&D practitioners, we zeroed in on what’s really happening inside organizations, not just what’s being pitched.

The 4 tiers of adoption

To make sense of the data, we grouped technologies into four categories based on actual adoption rates:

• Mainstream (80%+ adoption): These are the tools that nearly every organization is already using. They’re foundational to most learning ecosystems and have proven their long-term value.
• Common (50–80% adoption): These tools are quite common across the field. They’re familiar but not universal.
• Selective (20–50% adoption): These tools are adopted by organizations based on specific needs, budgets, or strategic priorities.
• Niche (<20% adoption): These tools have limited adoption but may serve specialized needs.

‘Tried & true’ technologies still going strong

Six technologies stood out with near-universal adoption. You won’t find anything flashy here—just dependable tools doing dependable work. Among the most prevalent tools are:

• Learning Management Systems (LMSs)
• SCORM-based eLearning
• Live polling and quizzing tools

They’re not the newest thing, but they solve everyday problems. And that’s what drives adoption. In fact, the more practical the use case, the more likely a tool is already in place.

The ‘late bloomers’ are having a moment

Some of the fastest-growing tools aren’t new. They’re just finally catching on. After years of hovering in the background, these technologies are gaining real traction. Tools showing growing adoption (more than 20% report adopting them in the last five years) include:

• Generative AI for content development
• Screen readers and accessibility solutions
• Microlearning solutions

Some of these tools have been available for a while. What changed? Awareness, priorities, and infrastructure finally aligned. Adoption doesn’t happen when the technology is ready. It happens when organizations are ready to implement it.

AI & other learning tools are on the rise

Not everything gaining momentum is brand new, but many tools are clearly on the move. At least 20% of L&D professionals told us that their organizations plan to adopt some technologies that are either built on AI or have become significantly more scalable because of it, such as adaptive learning systems and automated content curation.

But it’s not all about AI. Several other tools—while not inherently AI-based—are also seeing renewed interest and may already benefit from AI enhancements. In this category, we saw technologies like digital badging and microlearning solutions.

What’s motivating technology decisions?

As organizations consider which tools to adopt next, it’s helpful to understand what’s actually driving their decisions.

When it comes to adopting new technology, L&D professionals told us they are driven by learner impact, not just cost savings. Their top motivations for adopting new learning technology include:

• Increasing learner engagement
• Improving performance
• Delivering personalized experiences

Operational benefits like budget savings and better reporting still matter, but they’re secondary. It’s a good reminder that technology adoption is most successful when it starts with the learner.

The usual suspects are still in the way

The biggest blockers to technology adoption remain stubbornly familiar. When we asked both L&D and their vendor providers, they pointed to the same top barriers to adoption:

• Limited budgets
• Time constraints and competing priorities
• Lack of leadership buy-in

A bit farther down on the list of concerns, L&D teams often cite privacy and security concerns as barriers to adoption. Technology providers, meanwhile, feel more pressure to demonstrate a clear return on investment.

What this means for your technology strategy

Whether your organization is just getting started, ready to experiment, or already running a mature learning ecosystem, this report offers practical guidance on where to focus next.

Download the complete Workplace Learning Technologies Adoption report to explore:

• Detailed adoption data across all 43 technologies
• Side-by-side views from L&D professionals and technology providers
• Strategic takeaways to shape your learning technology investment decisions

Explore technology adoption strategies at the Learning Leadership Conference

If you’re eager to explore future-ready technologies further, join Megan Torrance at the “Learning Guild Research” session on Oct. 2 at the Learning Leadership Conference, and the full-day add-on event, Pillars of Learning: Technology, on September 30!

The Learning Leadership Conference is at the Rosen Centre in Orlando, October 1—3. Register today!

Download the Full Report

The post What 556 L&D Professionals Are Using (Not Just Talking About) appeared first on Learning Guild.

The L&D landscape is evolving faster than ever, with AI at the forefront. But how are organizations actually adopting these technologies? What are the real motivations, and what barriers are holding us back? And what can we learn from the adoption experience of our existing technologies? 

This new research report cuts through the noise to give you the data-driven insights you need. Authored by industry experts Megan Torrance and Lauren Milstid, this is your essential guide to understanding the current state and future trajectory of learning technology adoption. 

Here’s what you will find inside: 

• Which technologies are truly mainstream, common, selective, or niche in L&D organizations today.
• What technologies your peers are adopting, and what’s on the horizon, to inform your own tech ecosystem strategy.
• Why organizations are adopting new tech (engagement, performance, personalization) and the common hurdles they face (budget, time, buy-in, security).
• Where AI is truly impacting learning technologies, even in unexpected places, and how to harness its power.
• How your organization’s technology adoption compares to a comprehensive survey of over 500 L&D professionals.

This report is packed with data, analysis, and expert recommendations to help you make informed decisions, optimize your tech stack, and drive real learning outcomes. Don’t just react to change; lead it!

The post Workplace Learning Technologies Adoption appeared first on Learning Guild.

Visit the website of any major technology consultancy firm and search for “trust in AI.” You’ll get articles full of essential and valid topics, such as explainability, security, data privacy, reliability. These are the cornerstones of technical trust, and they’re critically important. We want to know the AI models are sound, the data is safe, and the output isn’t unintentionally biased.

But there’s a big hole in that conversation—and it’s a human-sized one.

As learning & development (L&D) professionals, we often sit in the space between systems and people. And while IT teams can (and should) build systems that are secure and stable, they can’t do the human work of trust-building for us. That part’s on us and our colleagues in HR, Org Dev, and change management.

So when our organizations start rolling out AI-enabled tools, it’s not enough to check the box on governance and hope the humans will catch up. If we want AI adoption to stick, we must think about how people experience trust and how we help leaders show up in trustworthy ways.

Because if there’s one thing we know from decades in L&D, it’s this: People don’t trust what they don’t understand, don’t feel connected to, or don’t feel safe questioning.

Why trust feels so fragile in the AI era

AI is moving fast, faster than many people are comfortable with. And while the promise of increased efficiency, personalization, and insight is real, the fear is real too: Fear of being replaced. Fear of being surveilled. Fear that decisions are being made behind closed doors without understanding or input.

All of this puts trust on shaky ground. And once trust is lost, it’s tough to get back.

As our organizations launch into AI initiatives, including chatbots, analytics, workflow automation, and skill-mapping tools, we need a human trust strategy alongside the technology rollout.

That starts with understanding how trust works on the people side.

The trust triangle: Three legs we can’t skip

Harvard Business School professor Frances Frei’s Trust Triangle gives us a simple, powerful framework for understanding what makes people trust others, especially leaders in moments of change. It’s like a three-legged stool: strong when all parts are solid, and wobbly when any one of them is weak.

The three components?

1. Empathy
2. Logic
3. Authenticity

Let’s look at how each one plays out in the context of AI—and how we, as L&D professionals, can help leaders and teams strengthen their foundation of trust.

Empathy: Show that you see me

This is the one most leaders do feel but don’t always know how to express. It’s the sense that “you get me.” It’s the feeling that you understand what I’m going through, what I’m afraid of, and what I hope for.

As AI reshapes jobs and workflows, people want to know:

• Do you care about how this change affects me, not just the bottom line?
• Will I still have a role here?
• Am I being asked to use tools I don’t understand, without support?

And this isn’t just about emotions—it’s about perception. Even if leaders deeply care, if they don’t communicate that care clearly and consistently, it doesn’t land.

L&D opportunity:
We can coach leaders on how to communicate with transparency and compassion. We can design training and communicate in ways that make empathy visible. We can ask, out loud, “How will this rollout feel for different teams?” and build experiences that meet people where they are.

Logic: Help me understand the ‘why’

This leg is all about the reasoning behind decisions—and how clearly that reasoning is shared. People don’t just want to know what is changing; they want to understand why this is the right path and how the decision was made.

And with AI? The logic isn’t always intuitive. “We’re using this tool because it streamlines hiring decisions” might sound reasonable to leadership, but front-line managers and recruiters might be wondering, “Based on what data? And what if the algorithm misses something I’d catch?”

L&D opportunity:
This is where AI literacy, proficiency, and fluency come in. We can help the organization understand how these tools work, what they can and can’t do, and where human judgment still matters. We can equip teams to ask smart questions and engage with the tools, not just react to them. And we can help leaders tell a clearer, more grounded story about why the AI is here in the first place.

Authenticity: Be a real person

This leg is about alignment between what you say and who you are. It’s the gut check that tells us, “I believe you.”

And here’s where things can get especially tricky with AI. If people think the company is saying one thing (“We value our people!”) but doing another (quietly implementing AI to cut jobs), the authenticity leg breaks fast.

L&D opportunity:
Authenticity isn’t something we can fake. But we can help create the conditions for it. That means designing learning and communication experiences that feel real, not over-polished. It means inviting leaders to show their thinking, share their learning process, and acknowledge what they don’t know. It means creating safe spaces for questions, resistance, and dialogue.

Because when we see someone navigating change with integrity, it builds trust, even when the change itself is hard.

Bringing it all together: Trust by design

Trust doesn’t happen by accident. It happens on purpose, through the choices we make in how we communicate, how we teach, how we support, and how we listen.

As we prepare our organizations for AI transformation through reskilling, retooling, and rethinking workflows, let’s also prepare our leaders to be trustworthy guides through the change.

And let’s ask ourselves:

• Are we helping people see where they fit in the future?
• Are we making space for both excitement and concern?
• Are we designing trust into our skilling, onboarding, and communication plans?

Because technology will keep evolving. But trust? That’s ours to build.

Keep learning

Want to explore how L&D can drive responsible AI adoption? Check out our recent articles on Skilling Up for AI Transformation and Impact Zones for L&D. Or explore our W.I.S.E. A.T. A.I. framework to learn how to lead with purpose in the age of AI.

Join Megan at the Learning Leadership Conference, October 1–3, in Orlando, Florida, where she will facilitate a full-day seminar on incorporating AI and other emerging technologies into your learning strategy. Pillars of Learning: Technology is a pre-conference learning event on September 30. Register for Pillars of Learning: Technology and the Learning Leadership Conference today!

Image credit: antoniokhr

The post Trust in the AI Rollout appeared first on Learning Guild.

We’re well past the moment of wondering if AI will affect learning and development (L&D). It’s already here—showing up in tools, workflows, learner experiences, and even the work itself.

But when everything’s shifting all at once, it’s hard to know where to start.

We’ve found that it helps to explore AI in L&D through three distinct but overlapping zones of impact. These AI Impact Zones give us a way to name what’s happening, plan where we’re going, and take more strategic steps forward.

Each zone reflects a different way AI is changing how we design, deliver, and drive learning. They’re not a roadmap or maturity model; they’re a lens. And in most organizations, all three are in motion at the same time.

Let’s break them down.

Zone 1: AI in L&D’s workflow and productivity

This is where most teams encounter AI first, embedded in tools we already use or added to our daily routines to speed up repetitive tasks.

It often starts with writing support, image generation, video narration, translation, and summarization. AI takes on the heavy lift in early content development—drafting copy, generating assets, or tidying up SME input. From there, it stretches into evaluation and analytics: summarizing learner feedback, identifying patterns, or flagging trends we might’ve missed in the spreadsheet scroll.

It’s easy to focus on efficiency. Faster timelines. Smoother QA. Automated alt text. All good things. But the real shift in this zone comes when AI frees us up to focus on more strategic contributions. When it handles the first draft of a storyboard or the first pass at a dataset, we can spend our energy designing learning that actually drives behavior change.

At TorranceLearning, we’ve built internal AI partners to suggest performance outcomes from a body of SME content, generate client-specific assessments, draft alt-text, and assess consistency against defined standards. These tools help us work more reliably at speed and scale. They clear the deck so we can stay focused on the work that matters. You’ve probably done the same.

And this is also where we start to feel the tension. AI can amplify human productivity, but it can also prompt hard conversations about headcount, roles, and value. “Doing more with less” is a seductive phrase, especially when the “more” includes strategic thinking, collaboration, and leadership. The goal isn’t to protect roles. It’s to focus our time and talent on where they have the most impact.

Here are some ways AI is already showing up in learning design tasks:

• Learning design tools that suggest content outlines, learning objectives, assessments, or visual layouts
• Content creation tools with generative AI that produce video, voiceover, or images
• LMS or LXP platforms with AI features that support content tagging, translation, or summarization
• Feedback analysis tools that identify patterns, flag outliers, or summarize survey data

Zone 2: AI in the learner experience

Here, the focus shifts from behind-the-scenes development to the learner’s actual experience.

What’s powerful in this zone is how seamlessly AI can enhance that experience. It adapts in real time to individual needs, behaviors, and progress, without adding complexity or friction.

This is where adaptive learning paths, AI role-play partners, and smart nudges start to show up in digital platforms. It’s where learning gets personalized and adaptive—not just to a role or job function, but to the individual’s progress, behavior, preferences, and needs.

AI technology is evolving quickly, but it’s not plug-and-play. These tools only work well when the content strategy, data structure, and learning experience design are sound. This is where the old rule applies: garbage in, garbage out. AI might personalize or optimize the input, but if the input is weak, the output won’t be much better.

We’ve explored this zone through projects like our DemoFest-winning Emma AI Case Challenge, where we custom-built AI interactions into a digital learning experience. Learners respond to a scenario using free text, which is compared against expert-vetted responses to generate rich, AI-powered feedback.

Compared to a typical multiple-choice interaction, it took more from our SMEs to get this right—but the learning value has been worth it. Learners engage more deeply and receive feedback that feels personal, relevant, and actionable.

We then layered on AI analysis of learner progress and response data in a dashboard that uses generative AI to summarize insights and offer tailored recommendations for both learners and course owners.

If this zone is active in your organization, AI might show up like this:

• AI coaches that give targeted feedback after branching scenarios
• Role-play bots that provide real-time feedback and skill practice
• Dashboards that flag learner struggles before the final assessment
• Performance support tools that surface relevant content at the moment of need
• Just-in-time tools that suggest tips or next steps based on task context
• Adaptive systems that adjust content, pacing, or difficulty based on behavior
• Learning platforms that personalize experiences based on learner progress and preferences

Zone 3: AI in the business (and what it means for L&D)

When AI changes how work gets done, it also changes what people need to learn. This zone is the biggest and most disruptive, and it’s often unfolding outside L&D’s immediate line of sight.

In many organizations, teams are already adopting AI-powered tools to support core parts of their work. And in most cases, that shift happened before the emergence of GenAI.

Sure, sometimes people need help using the tool. But more often, AI changes the work itself—and that means our onboarding, behavior change, and performance strategies need to evolve.

We also need to rethink our own role. AI is changing how we define good performance. It’s changing how managers coach. It’s changing what skills matter, and when. And L&D has a critical role in helping people navigate that change with clarity and confidence.

To support the shift, we need to recognize where and how these changes are already unfolding:

• Shifts in performance expectations that require earlier, faster upskillingsometimes before L&D is even looped in
• Managers who need new coaching strategies or are asking for support in developing AI fluency for themselves and their teams
• Product teams using tools with AI-assisted insight generation to automate analysis and accelerate decision-making
• Algorithms in industries like insurance or finance review large volumes of claims or transactions to detect anomalies and prevent fraud
• AI tools in supply chain systems forecast disruptions and recommend changes to production or delivery plans

Put the zones to work

These zones aren’t theoretical. They’re where the work is already happening.

If you’re feeling overwhelmed, start small. Pick one project or workflow from each zone and ask:

• How is AI showing up here today?
• Where could it enhance, accelerate, or shift how we approach this?
• What would success look like, considering people, process, and impact?

AI won’t transform L&D on its own. It’s a toolset, and your strategy is what gives it purpose. But when we use these zones to make sense of the changes and chart our course through them, we position ourselves not just to keep up, but to lead.

The opportunity isn’t just to work faster—it’s to work smarter, with greater intention and impact. These zones are already in motion. The question is: how will you show up in them?

Image creadit: Sansert Sangsakawrat

The post AI Impact Zones for L&D appeared first on Learning Guild.

Back in the summer of 2023, the AI buzz was loud and growing louder by the day. In nearly every conversation, learning leaders were asking versions of the same question: How do we get our people ready for this? Not just ready to click through a webinar or try out a chatbot, but really ready—to use it, adapt with it, and lead with it.

Spoiler: It’s not just about training. It’s about transformation. And not the kind that happens overnight. In fact, it’s ongoing.

Lately, I’ve been in a lot of conversations—some casual, some strategy-deep—about what it takes to skill up teams for AI. One pattern keeps emerging: The organizations getting the most out of generative AI are the ones doing the most to support their people. They’re not just training on a single tool. They’re building the capacity to work with AI as a class of technology.

So let’s talk about that. Not the hype, but the real work of helping humans thrive in an AI-enabled workplace.

Three layers, not a one-and-done

There’s a temptation, especially when technology is moving fast, to jump straight into tools. “Let’s train everyone on [insert shiny new AI app here]!” And sure, some exposure helps. But that approach usually skips over the foundation people need to actually use the technology with confidence and clarity.

Instead, we’re thinking in layers: AI Literacy, AI Proficiency, and AI Fluency. Each one builds on the last, and each serves a different (but connected) purpose in your transformation strategy.

We’re building on smart thinking from leaders like Markus Bernhardt and Brandon Carson, who frame the conversation around AI literacy and fluency. Their work emphasizes the importance of strategic understanding and dynamic governance across the enterprise.

We agree—and we recognize an additional, practical step that many teams need in between: Proficiency. That’s the hands-on, role-specific skill building that helps people move from knowing what AI is to actually using it well in their day-to-day work.

It’s not about choosing one framework over another. It’s about scaffolding a learning journey that supports both individual growth and organizational change.

1. AI literacy: Start with the basics

Think of this like digital literacy a decade ago—it’s table stakes now. AI literacy is about demystifying technology: What is AI? What can it do (and not do)? Where is it showing up in our tools and processes? What are the ethical risks and guardrails we need to understand?

This isn’t about teaching folks to write code or design neural nets. It’s about awareness, process, and policies. And yes, it often feels like compliance training—that’s because it is a kind of workplace safety. I’ve now heard this from two sources, Markus Bernhardt and a military  cyber-intelligence SME: AI literacy is the new cybersecurity.

It’s that important.

Done well, AI literacy builds not just knowledge, but curiosity. It gives people enough grounding to move from fear or skepticism into experimentation.

2. AI proficiency: Practice, not theory

Once you know what AI is, the next step is learning how to use it—on the job, in your context, for your goals.

Proficiency is where things start to feel tactical. It’s how you bring AI into your workflow and how your workflow changes as a result. That could mean summarizing meeting notes, writing performance reviews, analyzing survey data, or creating microlearning content in half the time.

At this layer, the questions shift from what is possible and permissible to how to do these things well.

This is also where risk increases. Without strong literacy under it, proficiency can lead to misuse—or just meh results. The two have to go hand in hand: know the tool, know the rules, then learn to wield it well.

3. AI fluency: From “I” to “we”

Here’s where it gets powerful. Fluency is what happens when AI use moves from individual experimentation to collective capability.

It’s the shift from:

• “What can I do with AI?” to “What can we do together?”
• Trying things out to building shared practices
• Using AI to designing how AI is used across the team

In fluent organizations, people collaborate on prompts. They refine workflows. They co-create guidelines. And they share what’s working—and what’s not—openly.

Fluency is less about technical skill than it is about culture. It’s where experimentation gets normalized and where learning loops (and trust) take root.

How this shows up in practice

When I talk to L&D teams, I ask: How are you scaffolding this journey?

Because it is a journey. And it doesn’t happen in one workshop or one lunch-and-learn. It’s about designing learning that’s iterative, role-relevant, and community-driven.

Some starting points:

• Map use cases before you build the program. What are people trying to do? Where does AI help? Start there.
• Don’t treat all learners the same. Literacy, proficiency, and fluency are different stages that require support. Each department has different needs.
• Build in refresh points. AI changes fast. Yesterday’s best practice might be obsolete tomorrow. Keep the learning loop open.
• Make it social. Fluency grows when teams experiment together. Host AI jams. Share prompts. Build a repository of “stuff that works.”
• Measure what matters. Not just attendance or completion—but confidence, usage, and impact. You may measure impact in terms of time savings, reduction in burnout, more effective output, etc. Be sure your measurements align with the business goals for using AI in the first place.

One more thing: How you roll it out matters

This part often gets overlooked, but it’s crucial.

The way you implement your AI skilling efforts communicates everything about your organization’s approach. Are you empowering people to experiment? Are you modeling trust and transparency? Are you treating this like a checkbox or a capability?

Every course, every communication, every pilot project is a chance to show your people: We’re in this together. We’re figuring it out. And we’re doing it in a way that aligns with our values.

Because, at the end of the day, skilling up for AI isn’t just about keeping up. It’s about showing up—together, curiously, and with a commitment to do this well.

What will you do with this?

So here’s the nudge: Where is your organization on this literacy–proficiency–fluency path?

What’s one step you could take to move your team forward? It could be a conversation. It could be a pilot. It could be a question in your next staff meeting: “What’s changed?”

Because change is here. AI is here. The question is — are we building the skills, the systems, and the shared understanding to meet it?

Let’s make it more than just adoption. Let’s make it a transformation.

Go all in on transformation!

If you’re eager to learn how to use AI and emerging technologies to transform learning at your organization, don’t miss our Pillars of Learning: Technology full-day seminar, a pre-conference event ahead of the Learning Leadership Conference (Learning 2025). Led by Megan Torrance, this seminar explores generative, predictive, and analytical AI, data analytics technologies, and next-generation learning platforms.

Come to the Learning 2025 conference a day early and launch your learning transformation!  Pillars of Learning: Technology is on September 30, 2025; the Learning Leadership Conference is October 1–3, 2025. Registration for both events is open now!

Image credit: ipopba

The post Skilling Up for AI Transformation appeared first on Learning Guild.

You’ve been there before.

You mapped out the perfect route. The plan was solid, every step carefully laid out. The team was prepared, and the destination was clear. Then, the landscape shifted. A key stakeholder left, priorities changed, a new SME joined the team, or new requirements emerged. Suddenly, your well-planned path no longer works, and you’re forced to reroute.

Your project has been waylaid by an onslaught of change.

What if, instead of resisting change, you designed for it from the start? That begins with anticipating the shifts ahead—before they disrupt your project. When you train yourself to recognize early signals of change, you can adjust long before they become roadblocks.

It’s not just about project management tools or Agile frameworks —it’s about designing work that moves with change, rather than fighting against it. An agile mindset and techniques can shape not only how we manage projects but also how we design the work itself. Whether you’re creating learning experiences, leading a business initiative, or launching a product, the way you prepare today determines how smoothly you can adapt when things inevitably shift.

We call it “Design for Change” and it has three components:

1. Seek out the Change
2. Build for the Change
3. Brace for the Change

Spot the terrain: Seek out the change

Most of us only react to change when it forces itself upon us. But what if you had a way to spot the signals of change early, before they disrupted your plans? Successful teams make detecting change part of their regular process, not just a reaction to sudden surprises.

How to approach this

Get the right people on board. Identify key stakeholders, decision-makers, and end users early. This includes the project sponsor, subject matter experts, project manager, and learners. Build strong relationships and frequent communication so that you’re more likely to hear about change and when it happens, you have a built-in supporter.

Solve for business and learner goals. Clearly define why you’re taking on this project. When changes happen, it’s easier to realign when you know the purpose.

Work, release, and evaluate in iterations. Test and refine as you go instead of assuming everything can be planned up front. When you release and test a product or program, you have a built-in opportunity to see what’s changed and use that insight to fuel your next iteration.

Include a contingency. Build in extra time, effort, and resources expecting that something will change. Keep that contingency time at the end of the project—when changes are most difficult to incorporate.

Regularly ask what’s changed. Schedule brief check-ins, even when everything seems stable. Add “What’s changed?” as a regular part of your meeting agendas. Then become a broken record: Every meeting starts with “What’s changed?”

In action

You’re preparing to launch a new product, and your sales team needs training on features, pricing, and positioning. You map out the training plan by collaborating with product managers and marketing. Everything seems on track—until leadership announces last-minute changes. They refine product features, adjust pricing structures, and shift the sales strategy.

Without a plan to handle mid-development changes, you risk delivering outdated content, confusing sales reps, or revising materials at the last minute.

Rather than waiting until training is fully developed to address the shifts, consider these proactive steps:

• Build in extra time, effort, and resources, anticipating that something will change.
• Check in with product teams early to ask, “Are any updates expected before launch?”
• Pilot a minimum viable version to gather early feedback and refine the content before full rollout.

By identifying change early and building flexibility into the development process, you ensure training materials remain accurate, up-to-date, and effective before development even begins. This prevents rework, confusion, and delays.

Lighten the load: Build for the change

Even when we anticipate change, we often overcomplicate our learning designs, making it harder to adjust. Instead of creating rigid structures that break, we need to build flexibility into our projects from the start.

How to approach this

Build lightweight and flexible solutions with modularity in mind. Use flexible learning assets that are easy to update, such as text-based slides, still images, modular content, and job aids. Save the fancy treatment for things that don’t change as often. Design components so updates can happen independently. This allows one part to change without disrupting the entire project.

Delay development on likely-to-change components. If something is uncertain, don’t finalize it too soon. Start your work on the parts that are least likely to change.

Release a minimum viable product (MVP). Start with a simple and functional version of your project. Gather feedback and refine over time instead of aiming for perfection up front.

Create a “wormhole” to live content. Instead of embedding static resources, link to real-time data or web-based updates.

Keep stakeholders informed. A clear project plan ensures that when updates happen, everyone knows what to expect.

In action

As you design your sales enablement program, it’s tempting to create polished product videos and static pitch decks. But if the product team updates a feature, will your materials keep up?

Rather than scrambling to revise content after changes are announced, consider these proactive steps:

• Link to live product documentation so reps always access the latest details.
• Use short and modular training sections to ensure only affected parts need updates.
• Embed real-time dashboards, linking to dynamic reports instead of creating static versions that quickly become outdated.
• Host live expert discussions rather than pre-recording content that may need frequent rework.
• Keep feature overviews high-level, directing sales reps to real-time product resources.

By designing training with built-in flexibility, you ensure training materials stay current and require minimal updates. This eliminates the need for a full redesign whenever changes occur.

Brace for the change: Carry the load with confidence

Managing change isn’t just about adjusting the plan. It’s about preparing yourself and your team to move forward with confidence. A well-prepared team expects shifts and adapts with focus and resilience.

How to approach this

Take care of your team. A strong leader anticipates challenges, plans for the unexpected, and creates an environment where adaptation feels possible. In the face of change, lead with empathy and courage.

Celebrate small wins. Recognize progress even when things are shifting.

Rework schedules to create capacity. When priorities change, adjust timelines instead of adding more work.

De-scope and streamline work. Simplify deliverables by focusing on what truly matters instead of holding onto unnecessary tasks. Eliminate nonessential work so your team can stay focused on priority tasks.

Delegate and outsource. Share the workload. Don’t try to do it all alone.

Make work visible. Transparency in tasks and progress keeps everyone on the same page.

Communicate change as an engaged partner. Clearly explain why changes are happening and what comes next.

In action

Six months after launch, the product team announces a major feature overhaul based on customer feedback. Sales reps need to pivot their messaging—fast. Instead of reacting under pressure, how will you ensure they adjust smoothly? Here’s what you can do:

• Reduce burnout by scheduling “no meeting” blocks. This gives you and your team uninterrupted time to focus on key updates.
• Pause lower-priority training initiatives to focus on critical product updates.
• Update only the most impactful product changes to minimize disruption.
• Host brief sales huddles to ensure reps understand the updates and how to position the product effectively in customer conversations.

By focusing on strategic prioritization, streamlined updates, and clear communication, you and your team adapt seamlessly without disrupting sales performance.

Keep trekking: Look back, move forward

This isn’t about predicting the future—it’s about making it easy to adjust when things don’t go as planned.

At every stage of your next project, ask yourself:

• Am I identifying potential changes by engaging with stakeholders, monitoring shifting priorities, and staying informed about external factors like budget changes or technology updates?
• Have I designed my work to be lightweight, modular, and easy to adjust?
• Is my team equipped with the mindset, tools, and support to navigate change with confidence? Are they ready to pivot when (not if) things shift?

If the answer to any of these is “no,” it’s time to rethink your approach. Take a step back, simplify, and design for change. The best learning solutions are designed for change and built to evolve.

Want to trek farther?

Explore LLAMA resources on our website.

Or, search “LLAMA” in our “All The Things” resource library for more tools and insights.

Image credit: Darkdiamond67

The post Design for Change: Because You Know It’s Coming appeared first on Learning Guild.

No one has the time, budget, or change capacity toeffectively direct an entire ecosystem all at once. Many leaders are reactive,adopting new technologies in isolation without a strategic view of how eachpiece fits within the broader ecosystem. In this session, Megan will guideparticipants through the process of building a strategy and business case forbuilding out the learning technology ecosystem, emphasizing the synergy betweenAI, xAPI, data analytics, and other key technologies. By focusing on the ecosystemas a whole rather than individual technologies in isolation, we aim to empowerlearning leaders to make more informed decisions, leverage technology moreeffectively, and drive meaningful learning experiences in their organizations.We’ll also acknowledge that the learning ecosystem encompasses more than justlearning technologies: it includes business systems, people, skills,relationships, and suppliers.

In this session, you will learn:

• What components make for a well-rounded learningecosystem strategy,
• How the template used for the map of theecosystem influences focus and strategy
• How to map the ecosystem and identify keycapabilities,
• How to plan a path forward given limited time,budget, resources, and change capacity

The post AI, xAPI, Data … oh my! Your Evolving Learning Technology Ecosystem appeared first on Learning Guild.

The post Introduction to xAPI and Cohort Success appeared first on Learning Guild.

The post Implementing xAPI in Your Organization: Tips for Successful Adoption and Implementation appeared first on Learning Guild.

Executive Summary

Download now for a detailed exploration of the evolving learning analytics in the eLearning industry.

The post The State of Data & Analytics in L&D: 2023 appeared first on Learning Guild.