I’ve been to a lot of conferences. I’ve moderated sessions, sat on panels and had more hallway conversations about the future of work than I can count. And lately, nearly every single one of them circles back to the same topic: artificial intelligence. What it means, what it can do and what it means for the people whose job it is to develop other people.

Here’s what I’ve noticed, though. We’re very good at convincing each other.

We gather in these rooms L&D professionals, HR practitioners, talent leaders and we nod along as someone makes a compelling case for why AI changes everything. We workshop it. We debate it. We leave energized. And then we walk back into our organizations, sit down across from a CFO or a department head and … stumble. Because the language that lands in a learning conference doesn’t necessarily land in a budget meeting. And that gap between what we know and what we can communicate is one of the most pressing challenges facing L&D right now.

We’re an Insular Bunch. And That’s a Problem.

L&D, by its nature, attracts people who are plugged into the human side of work. We care about empathy, about behavior change, about the whole person. Those instincts are what drew most of us to this field. But they also create a kind of echo chamber. When we talk about AI needing to be deployed with empathy and thoughtful leadership, everyone in the room gets it immediately. Of course. That’s obvious. Why would you even need to explain it?

But put that same conversation in front of a FinTech team, or an engineering department, or a group of operations leaders and you might get blank stares. Or worse, polite nodding that masks complete disengagement. The concepts we treat as self-evident are anything but universal. And if we can’t bridge that gap, we risk losing the very people we need to bring along.

This is something I’ve been thinking about a great deal lately. It’s one thing for us to go to these conferences and strengthen each other’s convictions. But then what? How do we take what we know back to the broader organization and make sure the right things are actually happening?

Empathy Has a Branding Problem. So Does L&D.

In a recent conversation I had with Alexandra Hyland, an experienced L&D practitioner and keynote speaker, she put it in a way that stuck with me: empathy has a branding problem. The word itself can feel soft, abstract, or even irrelevant depending on your audience. And she’s right. If we’re asking business leaders to prioritize human-centered approaches to AI adoption, we need to meet them where they are in language that’s compelling to them, not just comfortable for us.

This isn’t just about word choice. It’s about framing. It’s about understanding what your audience is trying to solve and positioning the conversation accordingly. An elevator pitch that works for a CLO won’t work for a COO. The core message might be the same, but the entry point has to be tailored.

What Alexandra described the desire for practitioners to have language they can actually use with their leaders is something I hear often. And it points to a real opportunity for L&D to play a more strategic role. What if one of our key outputs, as a function, was giving HR and L&D professionals the tools to make the case for human-centered AI adoption in terms their business leaders would actually respond to? Not just the what, but the how specific, actionable language calibrated for different audiences and industries.

The ‘Empty Mandate’ Problem

There’s another dynamic playing out inside organizations right now that we can’t ignore. Leaders issue the decree: we’re going to use AI. It’s part of the workflow. It’ll be in your performance review. AI is the future.

And then … nothing. No guidance on what that means in practice. No clarity on what problems it’s meant to solve. No answer to the very reasonable question: what exactly do you want me to do with this?

I’ve seen this play out repeatedly. The top-down mandate arrives, people wait to see if it sticks and eventually as with many technology initiatives before it some just quietly wait it out. That’s not cynicism, it’s pattern recognition. We’ve all watched initiatives arrive with fanfare and disappear without a trace. Why would AI be any different?

This is precisely where L&D has to step in. Not just to build AI literacy programs, but to help organizations answer the harder questions: What are we actually trying to achieve? What problems are we solving? What does good look like in six months? Without that scaffolding, even the most enthusiastic adopters will flounder and the skeptics will feel vindicated.

Content Is Not Learning. This Distinction Matters More Than Ever.

Here’s where I want to be direct about something, because I think it’s a conversation our industry needs to have more honestly.

AI is extraordinarily good at generating content. It can take a subject matter expert’s 400-page technical document and spin it into a structured course in a fraction of the time it used to take. Platforms are emerging every day that make it easier and faster to produce e-learning at scale. And leaders are excited. Of course they are.

But there’s a distinction that L&D professionals understand and that business leaders often don’t: content is not learning.

Capturing institutional knowledge and presenting it in a digestible format is valuable genuinely valuable. But it is not behavior change. It is not skill development. It is not the thing that moves someone from knowing something to being able to do something differently. And if we let organizations conflate the two if we allow AI-generated content to be called “training” simply because it’s faster and cheaper we are failing in our core responsibility.

I sat in on a session at a recent conference where a speaker from a major organization talked about how AI-generated content was scoring higher on effectiveness metrics than content their team had previously produced. In the same breath, he mentioned they’d let go of roughly 80% of their instructional designers. He put it plainly: I just got rid of a bunch of people who do the job that you do.

That’s a sobering moment for anyone in this room. But here’s the thing the answer isn’t to defend the status quo. It’s to make the case, clearly and compellingly, for what instructional designers actually do at their best. Not capturing content, but translating it. Not organizing information, but engineering behavior change. That’s not something AI does well. Not yet. And that distinction needs to be part of every conversation L&D is having with organizational leadership right now.

This Is Actually an Exciting Moment. Here’s Why.

I want to end on something that I genuinely believe: this is one of the most interesting times to be in learning and development in a long time.

For years, L&D operated as an order-taker. Someone upstairs decided what training was needed and we built it. The function was reactive, often undervalued and rarely seen as strategic. AI has, in a strange and somewhat ironic way, changed that dynamic. Because organizations are looking at learning leaders and asking: what should we be doing? Where should we focus? How do we prepare our people for this?

We don’t have to wait to be told anymore. We get to write the playbook.

That means getting comfortable with a few things. It means being willing to push back when AI is positioned as a time-saving silver bullet because the research doesn’t fully support that framing and because time saved is only valuable if it’s redirected intentionally. It means helping leaders understand the difference between efficiency and effectiveness. And it means showing up to those conversations with language calibrated for the audience in front of you, not the audience you left back at the conference.

We’re in a period of rapid change where nobody not the technology companies, not the consultants, not even the most forward-thinking L&D teams has it all figured out. But that’s precisely the point. The organizations that will navigate this best won’t be the ones with the most sophisticated AI tools. They’ll be the ones with people who can ask the right questions, build the right capabilities and translate between the human and the digital in ways that actually move the needle.

That sounds a lot like what L&D has always done, at its best.

So let’s go do it.

The post Stop Convincing Each Other: How L&D Must Lead the AI Conversation Beyond the Conference Room appeared first on Brandon Hall Group.

The patterns are unmistakable. Organizations rush to deploy AI across departments, convinced that speed equals progress. Marketing launches chatbots. Finance pilots predictive analytics. Operations experiments with automation. Each initiative makes sense in isolation. Each promises transformation. And most deliver fragmentation instead.

Docebo’s analysis of this phenomenon cuts to the heart of why AI adoption feels simultaneously urgent and chaotic. Their framework for consolidating scattered AI initiatives under strategic alignment offers a path forward that our research at Brandon Hall Group consistently validates. When organizations treat AI as infrastructure rather than as isolated experiments, success rates shift dramatically.

The Hidden Cost of Moving Fast Without Direction

The rush to adopt AI creates three distinct failure modes that Docebo identifies as vertical, horizontal, and technical fragmentation. Our research shows these aren’t abstract risks but the daily reality for 46% of organizations operating at Phase 1 and Phase 2 progression levels.

Vertical fragmentation occurs when executive vision never connects with frontline execution. We see this in organizations where leadership mandates AI transformation while teams struggle with basic tool access or training. The disconnect isn’t just communication failure, it’s structural. Without clear governance mechanisms that translate strategy into actionable execution, AI initiatives stall in competing interpretations of what success looks like.

Horizontal fragmentation emerges when departments optimize for local efficiency rather than enterprise value. One company we studied discovered 40 AI-related OKRs across multiple functions with numerous interdependencies that no one had mapped. Each team believed they were driving innovation. No one realized they were duplicating effort and creating incompatible systems that would never integrate.

Technical fragmentation forces employees to navigate contradictory workflows as different tools demand different approaches. Docebo frames this as a capability crisis: without unified competency frameworks, even the best AI tools become sources of confusion rather than productivity gains.

Strategic Alignment Creates Universal Success

Organizations reaching Phase 3, where strategic alignment becomes operational reality, report universal success with AI initiatives. Not improved success. Not better outcomes. Universal success.

This isn’t hyperbole. It’s what happens when AI initiatives connect directly to business objectives through disciplined governance. Our research shows that 25% of organizations operate at this strategic alignment phase, and they’ve fundamentally transformed how AI delivers value.

The difference isn’t better technology. Phase 3 organizations don’t have superior AI models or more advanced platforms. What they have is the governance frameworks, data foundations, and capability development processes that turn AI from experiment into execution engine.

Docebo’s emphasis on viewing AI as infrastructure rather than application layer resonates with what we observe in high-performing organizations. When companies establish centralized oversight while enabling distributed innovation, they solve the core tension that fragmentation creates: the need for both control and agility.

Building Infrastructure Through Governance

Docebo’s proposed AI Accelerator Group addresses the governance challenge directly. This is about creating clarity. The five-phase gated process they outline is a disciplined approach that balances strategic direction with grassroots innovation.

The intake and strategic review phases ensure alignment with business objectives before resources get committed. But the critical innovation lies in Phase 3: Capability Due Diligence. This is where Docebo’s framework intersects powerfully with our research on capability building as strategic infrastructure.

Making the Chief Learning Officer’s sign-off mandatory for pilot funding acknowledges that  technology readiness means nothing without workforce readiness. The most sophisticated AI deployment fails when people can’t or won’t use it effectively. By requiring formal capability mandates before initiatives proceed, organizations prevent the common pattern where AI tools launch without adequate training, support, or change management.

Our data shows that organizations implementing comprehensive governance frameworks early prevent compliance issues that plague later-stage adoption. Phase 3 organizations establish regular AI risk assessments and bias monitoring as standard practice—not as reactive measures after problems emerge.

The Safe Harbor Model for Innovation

Docebo’s distinction between formal governance for high-stakes initiatives and safe harbor zones for experimentation solves a problem that stymies many organizations: how to maintain control without crushing innovation.

The safe harbor approach works because it establishes clear boundaries within which teams can experiment freely. Pre-approved tools, defined data policies, and prerequisite AI literacy training create the conditions for rapid prototyping without introducing unmanaged risk.

The path from safe harbor to formal governance creates the selection mechanism that prevents promising experiments from dying in obscurity. Grassroots projects that demonstrate value get nominated for strategic backing, ensuring that innovation flows from actual problem-solving rather than top-down mandates.

Cross-Functional Coordination as Competitive Advantage

AI transformation affects multiple functions simultaneously. Docebo recognizes that sophisticated coordination prevents conflicts and creates synergies that siloed approaches miss. This requires governance bodies with representation from all key functions, clear decision-making processes for enterprise-wide initiatives, and shared metrics that create accountability.

Our research shows that organizations successfully navigating this coordination challenge establish AI Strategy Committees that meet monthly for strategic review and quarterly for comprehensive assessment. These are working sessions where conflicts get resolved, resources get allocated, and strategic direction gets adjusted based on results.

The payoff appears in data integration and platform capabilities. Organizations with strong cross-functional coordination implement integrated HRIS systems with analytics that connect all functions. API integrations enable seamless data flow. Organizational capabilities that emerge only when governance creates the conditions for coordination.

Data Quality as Strategic Imperative

The data quality problem is organizational. Data fragmentation reflects governance fragmentation. When multiple functions maintain separate data stores without coordination, no amount of AI sophistication can generate reliable insights. The solution requires enterprise data strategies that establish ownership, enforce standards, and maintain continuous audits. This is foundational for AI success.

Companies implementing data-as-a-service functions, providing centralized data capabilities to the broader enterprise, report smooth AI adoption and consistent analytics capabilities. This centralization creates trustworthy AI solutions and enables the rapid deployment that organizations need to maintain competitive advantage.

 Workforce Capability as Execution Enabler

The connection between AI infrastructure and capability development runs throughout.  Docebo’s AI Capability Academy concept deploys targeted workforce readiness plans that are designed during governance review is what high-performing organizations actually do rather than what they aspire to do.

Our progression model shows clear progression in how organizations approach capability building. Phase 1 organizations struggle with limited skills and siloed systems. Phase 2 organizations begin foundational AI literacy development. Phase 3 organizations achieve organization-wide literacy that enables cross-functional collaboration. Phase 4 and Phase 5 organizations build innovation leadership that drives continuous advancement.

The capability mandate approach ensures that AI investments include explicit plans for building the skills and knowledge needed for success. This prevents the common pattern where technology deployments outpace workforce readiness, creating adoption problems that undermine ROI.

Learning and Development as Strategic Architecture

Docebo correctly positions learning and development leaders as architects of cohesive AI operating systems rather than support functions. The transformation from service function to strategic pillar requires that learning leaders gain seats on AI Accelerator Groups and other governance bodies. This ensures that capability implications get assessed before initiatives launch rather than being addressed reactively when adoption problems emerge.

The Convergence of Governance Requirements

Docebo’s observation about regulatory convergence toward mandatory AI governance aligns with what we see emerging globally. The voluntary ethical guideline phase is ending. By 2027, AI governance will likely become required across sovereign AI laws and regulations worldwide.

This creates strategic windows for early adopters. Organizations implementing comprehensive governance now build competitive advantage while others scramble to meet compliance requirements. The governance frameworks, data standards, and capability development processes that enable successful AI transformation also position organizations to meet evolving regulatory demands.

Making Integration Operational

The practical steps Docebo outlines translate governance principles into executable actions:

1. Establish clear, single-point ownership of the enterprise AI portfolio. If accountability is ambiguous, your first action is to designate a single executive leader. Recognizing this critical need, many organizations are creating a formal Chief AI Officer (CAIO) role, whose first mandate should be to charter and empower the AI Accelerator Group.
2. How many “random acts of AI” are currently draining your budget? Mandate that all new AI initiatives enter through the single front door of centralized governance, while empowering grassroots innovation within a clearly defined safe harbor.
3. Is your workforce readiness an input to your tech strategy, or an afterthought? Codify “Capability Due Diligence” as a mandatory, auditable gate in your project funding workflow. Making the CLO’s sign-off a prerequisite is the single most effective way to guarantee your AI investments will be adopted.
4. Can you see your entire AI landscape on a single screen? If not, build a transparent portfolio dashboard that tracks all initiatives—both those in the formal lifecycle and those emerging from the safe harbor. This builds trust and helps you spot opportunities for cross-functional synergy.
5. Is your L&D function funded to be strategic? Allocate budget for L&D to perform the front-end, strategic work of assessing capability implications and providing the foundational AI Literacy that enables safe, widespread experimentation.

The Path Forward

Organizations can continue pursuing disconnected AI initiatives that promise transformation while delivering fragmentation. Or they can adopt unified approaches that treat AI as strategic infrastructure requiring governance, coordination, and capability building.

Consolidating AI initiatives under strategic alignment and governance provides the architecture that enables this transition. By establishing governance that balances control with innovation, ensuring data quality supports AI applications, building workforce capabilities that enable adoption, and positioning learning leaders as strategic architects, organizations create the conditions for AI to deliver its transformative potential.

The question isn’t whether to adopt AI. That decision is made. The question is whether AI adoption will happen through strategic design or chaotic accumulation. Organizations choosing design, governance, coordination, and capability building over speed alone are achieving universal success.

Read about the complete Docebo framework and detailed implementation guidance at Docebo’s website.

The post How Strategic Governance Changes Everything</br> in AI Initiatives appeared first on Brandon Hall Group.

Artificial intelligence has quickly become one of the most talked-about forces shaping the future of learning and development. Every week, it seems a new tool promises to generate courses, produce videos, or automate instructional design in seconds. The promise is compelling: faster development, lower cost, and learning content at scale.

But there’s a problem with how many organizations are approaching AI in L&D today.

Too often, the conversation begins with automation. Prompt a tool, generate content, edit it quickly, and publish. The assumption is that producing more learning content faster will somehow translate into stronger workforce capability.

In reality, learning has never worked that way.

At Brandon Hall Group, our research consistently shows that effective learning begins with expertise, context, and clear business outcomes. Technology — including AI — should amplify those elements, not replace them. That’s why there needs to be a shift in mindset from AI-first automation to author-first augmentation.

The Problem with an AI-First approach

The earliest wave of AI-driven learning tools focused heavily on content generation. The workflow was simple: enter a prompt, generate a course or module, and then edit the output.

The advantage of this model is speed.

The downside is quality, relevance, and alignment.

AI-first approaches often optimize for volume. When learning is treated primarily as a content production challenge, organizations risk flooding their workforce with generic materials that may look polished but lack the nuance and context required to drive real capability.

Learning doesn’t fail because organizations lack content. It fails when the content doesn’t connect to real work.

Subject-matter expertise, business context, and performance objectives are elements that cannot simply be generated by AI.

This is why organizations should reframe the role of artificial intelligence—not as the primary creator of learning, but as a partner that supports experts and accelerates their ability to share knowledge.

The Author-First Alternative

A more sustainable approach begins with a simple principle:

Learning should start with human expertise.

Within every organization, subject-matter experts (SMEs) hold critical business specific knowledge — how processes work, how customers behave, and how decisions are made. Historically, capturing that knowledge has been difficult because traditional course development requires specialized instructional design skills, external vendors, and long development cycles.

This is where a modern, AI-enabled authoring platform can have meaningful impact.

AI-powered platforms like Easygenerator help organizations empower SMEs to transform their expertise into structured learning experiences. Rather than replacing authors, these platforms embed AI directly within the authoring workflow to support tasks like drafting content, generating assessments, refining tone, and structuring courses, making them didactically stronger.

The philosophy behind this approach is explored further in How L&D Teams Use AI: Lessons from Real Conversations, which highlights how organizations are using AI to remove friction from course creation while keeping subject-matter expertise at the center of the process.

The result is a fundamentally different model.

Experts remain the source of knowledge.
AI removes friction from the creation process.

That balance — human insight supported by intelligent technology — is the essence of augmentation.

Automation vs. Augmentation

One of the most important distinctions organizations must make in the AI era is the difference between automation and augmentation.

Automation replaces human activity.
Augmentation enhances human capability.

In industries like manufacturing or transportation, automation may remove people entirely from a process. But in learning, that approach rarely works. Training requires judgment, context, and alignment with performance outcomes.

AI excels at repetitive, time-consuming tasks. It can summarize text, generate quiz questions, translate content into multiple languages, or structure a course outline in seconds.

Humans bring something very different: understanding of the business environment, awareness of learners’ needs, and the ability to connect learning objectives to organizational goals.

When these strengths are combined, organizations unlock the real potential of AI in L&D.

Instead of replacing learning professionals or SMEs, AI becomes the engine that accelerates knowledge capture and course creation.

Scaling Expertise Through Employee-Generated Learning

Another major shift accompanying this author-first model is the rise of Employee-Generated Learning (EGL).

Traditional learning models rely heavily on centralized development teams. Every training request—from compliance modules to product training—flows through the same bottleneck. As organizations grow, this model becomes unsustainable.

Employee-generated Learning flips that dynamic.

With intuitive authoring tools and embedded AI assistance, employees across the organization can contribute their expertise directly to the learning ecosystem. SMEs can create training aligned with their day-to-day work, keeping knowledge accurate, relevant, and continuously updated.

This democratization of knowledge creation is powerful.

It allows organizations to:

• Capture expertise at scale
• Reduce development bottlenecks
• Keep learning aligned with evolving business realities

At the same time, L&D’s role becomes even more strategic.

Rather than acting primarily as content producers, learning leaders evolve into architects of knowledge ecosystems—setting standards, guiding learning design, and ensuring quality while enabling experts throughout the organization to contribute.

The Strategic Role of L&D in the AI Era

All of these developments point to an important truth:

AI does not diminish the role of learning leaders—it elevates it.

Experts will continue to provide the knowledge and context that organizations depend on. AI will reduce the effort required to transform that expertise into structured, accessible learning experiences.

As technology removes production barriers, L&D professionals are freed to focus on higher-value work: aligning learning with strategy, orchestrating knowledge ecosystems, and ensuring that learning experiences truly drive performance.

In this environment, partnerships between technology providers and research organizations become increasingly important.

Through initiatives like the Brandon Hall Group Institute and our Preferred Provider Program, organizations gain access to trusted partners, emerging technology insights, and practical guidance on how to integrate innovations like AI into their learning strategies responsibly and effectively.

These collaborations help learning leaders move beyond experimentation and toward scalable, measurable impact.

To learn more about Brandon Hall Group, click here.

About Easygenerator

Easygenerator is an AI-powered e-learning suite that helps organizations create company-tailored training at scale. Built for internal experts and L&D teams alike, Easygenerator is used by over 50,000 people across 2,000+ companies—including Danone, Electrolux, and Sodexo.

The post Authoring First, AI Second:</br> Why the Future of L&D Is Augmentation, Not Automation appeared first on Brandon Hall Group.

Let me set the scene. It’s my first month on the job. My manager sends me a link to a learning management system and tells me to complete my onboarding training. I click in and I’m greeted by a 47-slide PowerPoint that someone clearly built in 2016, a few grainy videos with robotic narration and a quiz at the end that I could pass without actually watching any of it.

I finished it in under an hour. I retained almost none of it.

Sound familiar?

If you work in L&D or HR, I want you to hear this, not as a complaint, but as honest feedback from the generation you’re now trying to train, develop and retain. Gen Z isn’t disengaged because we don’t care about learning. We’re disengaged because the way most organizations deliver learning doesn’t match the way we actually learn.

We Grew Up Learning Differently

Here’s something worth understanding about Gen Z: We didn’t discover the internet. We were born into it. By the time we entered the workforce, we had already spent years learning how to do things through YouTube tutorials, TikTok breakdowns, Reddit threads and Discord communities. We learned to edit videos, code apps, start businesses and develop skills, entirely self-directed, entirely on our own time and almost always in short, digestible formats.

So when we show up to work and get handed a three-day instructor-led training, it doesn’t just feel boring. It feels inefficient.

That’s not arrogance. That’s just the reality of how our learning instincts were shaped.

What We Actually Want

Let me be specific, because “Gen Z learns differently” is a vague statement that doesn’t help anyone build a better training program.

1. Bite-sized and on-demand. We want to learn in the moment we need it, not three weeks before we need it in a scheduled session. Microlearning works for us. A 5-minute video, a quick how-to guide, a short interactive module we can pull up on our phone between meetings. That’s the format that fits our workflow and our attention spans. This isn’t laziness; it’s efficiency.
2. Relevant and immediately applicable. If I can’t connect what I’m learning to something I’ll use this week, I’m going to struggle to stay engaged. Gen Z responds to learning that feels practical and tied to real outcomes. Tell us why we’re learning something and what we’ll be able to do after. Context matters more than content volume.
3. Social and collaborative. We don’t just want to learn at something, we want to learn with people. Peer learning, group discussions, mentorship and even social learning features inside platforms (think comments, reactions, shared notes) make the experience feel alive. We grew up learning in communities online and that instinct doesn’t disappear at work.
4. Personalized to our path. Not everyone on a team has the same skills gaps or career goals. Cookie-cutter learning paths feel tone-deaf to us. We want development that feels tailored, with learning recommendations based on our role, our goals and where we actually want to grow. AI-powered learning platforms are starting to make this possible, and Gen Z notices and appreciates when a company invests in that kind of experience.
5. Continuous, not episodic Learning shouldn’t feel like a once-a-year event tied to performance review season. We want it woven into our day-to-day work. Small opportunities to grow, consistent feedback, stretch assignments. This is what keeps us engaged and feeling like we’re moving forward.

What L&D Teams Should Do About It

I’m not here to just point out the problem. Here’s what I’d actually recommend if you’re building or rethinking your learning programs with Gen Z in mind:

• Audit your content for relevance and format. If it’s longer than 15 minutes and can’t be broken up, ask yourself if it needs to be. Could this be a shorter, more focused live session rather than an all-day training event? Could this PDF become an interactive module?
• Build in social learning touchpoints. Cohort-based programs, peer mentorship pairings and even Slack channels dedicated to sharing resources go a long way. Don’t underestimate informal learning.
• Use technology that feels modern. Gen Z can tell the difference between a platform that was built for us and one that was built in 2010. Investing in a modern LXP (Learning Experience Platform) signals that the company takes development seriously.
• Ask us what we want. Run a survey. Do focus groups. Include Gen Z employees in the design of learning programs. We’ll give you better insights than any generational report will.
• Tie learning to career growth explicitly. Show us the path. If completing this learning track means I’m on track for a promotion or a new role, say that. Connect development to opportunity clearly and often.

The Bottom Line

Gen Z isn’t a hard generation to develop. We actually want to grow. We’re ambitious, curious and used to teaching ourselves things. The opportunity for L&D leaders is to meet us where we are, rather than asking us to adapt to systems built for a workforce that no longer exists.

Get the format right, make it relevant and give us community around it. Do that and you won’t just train Gen Z. You’ll build some of the most self-directed, engaged learners in your organization.

And maybe retire that 47-slide PowerPoint. I’m begging you.

The post What Gen Z Actually Wants from Workplace Learning</br> Spoiler: It’s not a 4-hour training module appeared first on Brandon Hall Group.

The world of talent and learning development is at an inflection point. Organizations are navigating tighter budgets, evolving skill demands and a rapidly shifting technology landscape — all at the same time. Based on Brandon Hall Group research and conversations with companies throughout 2025, here’s a comprehensive look at where things stand today and what to expect in the year ahead.

The Pressures Organizations Are Facing Right Now

If there’s one theme that cuts across virtually every talent challenge today, it’s time. Budget constraints consistently rank among the top organizational challenges and while financial pressures ebb and flow with economic conditions, the scarcity of time is a constant. As one analyst put it, “Money can’t buy you time in most places.” This reality shapes nearly every decision organizations make, especially when it comes to technology adoption.

From a broader talent management perspective, three challenges rise to the top:

1. Financial constraints. Budget limitations remain the single biggest barrier to how organizations manage, develop and deploy their people. This is unlikely to ease in the near term, meaning L&D leaders must become even more skilled at doing more with less.
2. Voluntary turnover. While the job market has shifted somewhat, retaining top talent — especially high performers — remains a strategic priority. The focus now is less on a red-hot labor market and more on keeping key contributors engaged and committed for the long haul.
3. Upskilling and reskilling at scale. Skills have been a headline challenge for years and that hasn’t changed. The pace of change is accelerating and organizations are under pressure to continuously identify the skills they need, develop them in their workforce and do it fast enough to stay relevant.

The Hidden Skills Challenge

Here’s an interesting contradiction in the data: while skills are a top concern, two foundational activities (defining skills and competencies for roles and tying those to individual development plans) rank at the bottom of the list of perceived challenges. In other words, companies don’t feel particularly challenged by those things.

But that may not tell the full story.

In practice, building and maintaining a skills ontology is extraordinarily labor-intensive. Once created, there’s a natural temptation to “put it on a shelf” but skills frameworks are living documents that require continuous maintenance. More importantly, many organizations are still struggling to connect skills data to the actual work being done, the people doing it and the development opportunities available to them. That alignment of skills to people to work to development remains elusive for most.

It’s possible that organizations are underreporting how hard this really is.

Measuring Learning: Still a Work in Progress

Despite years of conversation about learning impact and ROI, most organizations are still measuring learning at the most basic levels, including completion rates, smile sheets and simple assessments. Very few have made meaningful progress toward measuring behavioral change (Kirkpatrick Level 3) or actual business results (Level 4), let alone calculating a true ROI.

This is a perennial challenge and it hasn’t gone away. Organizations continue to struggle with connecting what happens in a learning program to outcomes that matter to the business. Until that link is made more clearly, L&D will continue to fight for its seat at the strategic table.

The Technology Landscape: What Companies Are Adding

When it comes to learning technology, a few trends stand out:

• AR/VR and simulations are gaining traction as companies look to immersive tools that go beyond traditional eLearning.
• LearnOps platforms are growing in interest; organizations want tools to manage the business of learning, not just deliver content.
• Analytics and video remain high priorities as companies look to make more data-informed decisions and leverage richer media.
• LXPs still have a place, with about 30% of companies considering adding one, though the line between LXP and LMS continues to blur.
• The LMS sits at the bottom of the “adding” list, not because it’s irrelevant but because most organizations already have one (or several).

Notably absent from the list? A single line item for “artificial intelligence.” That’s not because AI isn’t important; it’s because AI isn’t one technology. It’s the engine powering all of the above.

How AI Is Actually Being Used in Talent and Learning

As of mid-to-late 2025, only about 11% of organizations said they weren’t using AI in any meaningful way. For everyone else, AI is showing up in a variety of forms:

• Content creation (61%) is the clear leader. AI is helping teams dramatically reduce the time it takes to develop learning content: not by replacing human judgment but by generating frameworks, outlines and drafts that people can then refine and polish.
• Support tools and chatbots are widely deployed, particularly for just-in-time performance support.
• Combining AI-powered tools to build custom platforms and workflows is how about 30% of companies are operating.
• Personalized learning is an active and growing use case, with AI helping surface the right content to the right learner at the right moment.

From a broader talent perspective, organizations are also exploring AI for:

• Improving employee engagement — Using AI-driven interactions to maintain connection and motivation.
• Automating processes — Stripping out time-consuming manual workflows so teams can focus on higher-value work.
• Personalizing development plans — Using AI to synthesize a wide range of data points into a more complete picture of each employee’s needs and growth opportunities.
• Optimizing talent allocation — Getting smarter about where to deploy people and when to invest in development.
• Predictive attrition analysis — Perhaps the most forward-looking use case, using AI to identify patterns across the organization that might signal flight risk, well before a manager would notice on their own.

On that last point, it’s worth noting: performance reviews alone are not sufficient predictors of future potential or attrition. The power of AI in this context lies in its ability to pull together data from across the organization, things humans wouldn’t think to correlate and surface patterns that would otherwise be invisible.

Predictions for 2026: A Sneak Peek

As highlighted in Brandon Hall Group’s HR Outlook 2026 book, several significant shifts are on the horizon:

1. The Flexible Learning Ecosystem

Organizations will move away from centralized, monolithic learning platforms toward a more interconnected ecosystem of tools: specialized solutions for content creation, skills tracking and delivery, all working together. Blockchain-based credentialing may start to gain traction as a way to build a verifiable, portable digital record of an individual’s skills growth. AI agents will play a growing role in delivering learning directly within the flow of work.

2. Cognitive Offload Curriculum

The idea of embedding learning directly into the workflow, via co-pilots and agents, will become more mainstream. Rather than pulling employees away from their work to “go learn something,” tools will identify learning moments in real time and deliver targeted support right where people are working. This makes “learning in the flow of work” less of an aspiration and more of an operational reality.

3. Neural Learning Integration

Organizations will pay greater attention to how the brain actually learns and use those insights to inform the design and delivery of learning programs. Expect to see more science-backed approaches influence everything from content structure to pacing and reinforcement strategies.

4. Learning as Personal Brand Currency

Learning won’t just be something the organization does to employees; it will increasingly become something employees actively build and own as part of their professional identity and career trajectory.

5. Hyper-Personalized, Just-in-Time Learning

This is one of the most significant shifts on the near-term horizon. AI tools are finally making true personalization achievable: surfacing the right learning opportunity for the right person at the right moment, whether they’re in their email, their CRM or a project management tool. Proactive, micro-interventions will help employees address skill gaps in real time before small problems become larger ones.

6. Mastery Guild Models

Organizations will experiment with community-based expertise models that formalize how knowledge is shared, developed and recognized across teams and departments.

7. Predictive Learning for Employee Retention

Building on predictive attrition capabilities, organizations will start using AI-powered insights to proactively design and deploy development opportunities that address retention risk, turning learning into a retention strategy, not just a development one.

The Thread That Connects It All

Across every one of these predictions runs a common theme: seamlessness. The goal isn’t just more learning, or faster learning, or cheaper learning. It’s learning that feels like a natural, invisible part of how work gets done, as intuitive and integrated as the tools people already use every day.

We’ve been talking about “learning in the flow of work” for years. The difference now is that the tools to actually make it happen are finally here. The organizations that figure out how to put them together — intelligently, intentionally and with a relentless focus on outcomes — will be the ones that win the talent game in 2026 and beyond.

The post The Intelligent Learning Organization:</br> Trends, Challenges and Predictions for the Year Ahead appeared first on Brandon Hall Group.

TL;DR: Most learning programs fail not because of poor content, but because managers aren’t equipped or engaged to support skill application. Most learning programs fail not because of poor content, but because managers aren’t equipped or engaged to support skill application. Research from Brandon Hall Group shows that mentoring/coaching and on-the-job training — both requiring active manager involvement — rank among the most highly effective learning modalities. When managers reinforce, coach, and create opportunities for practice, training investment translates into measurable business results.

The Learning Transfer Problem

Organizations invest heavily in learning and development programs. Employees complete courses, attend workshops, and participate in training initiatives.

Then they return to work. And very little changes.

The content was relevant. The design was sound. But without active manager involvement, most learning never gets applied.

This is where managers become critical.

Why Managers Determine Learning Success

Managers control the environment where skills are practiced, reinforced, and integrated into daily work.

They influence:

• Application opportunities. Do employees get chances to use new skills immediately?
• Feedback quality. Are employees receiving coaching on what works and what doesn’t?
• Accountability. Are skill improvements expected, measured, and recognized?
• Psychological safety. Can employees practice, make mistakes, and improve without fear?

When managers actively support learning, capability development accelerates. When they don’t, training becomes an isolated event with minimal impact.

Where Learning Strategies Fall Short

Despite managers’ influence on skill application, most learning strategies treat them as passive participants.

Common disconnects include:

• Managers Are Not Involved in Design: Learning programs are often built without manager input. Managers don’t understand the learning objectives, can’t connect training to business priorities, and struggle to reinforce content they haven’t seen.
• No Preparation or Support Tools: Managers receive no guidance on how to support skill development. They lack conversation guides, coaching frameworks or performance support resources.
• Conflicting Priorities: Managers are measured on operational results, not learning outcomes. When workload pressures increase, development conversations disappear. Brandon Hall Group research reveals that 57% of survey respondents rate time as a “significant” or “heavy” constraint, making it critical to simplify how managers support learning.
• Limited Communication: Managers often don’t know when their team members have completed training or what skills they’re expected to apply.
• Lack of Manager Capability: Many managers have never been trained to coach, provide developmental feedback, or create learning opportunities in the flow of work.

The result is a significant gap between learning investment and business impact.

What High-Performing Organizations Do Differently

Organizations that achieve measurable results from learning treat managers as essential partners, not bystanders.

According to Brandon Hall Group research, only 42% of organizations report above-average to excellent alignment between learning and business goals. Those with excellent alignment share several common characteristics, including learning initiatives structured around business problem-solving and dedicated transformation teams with clear governance frameworks.

1. Engage Managers Before, During, and After Training

Effective learning strategies include managers at every stage:

Before Training

• Share learning objectives and business context.
• Discuss how new skills align to team and organizational priorities.
• Prepare managers to set expectations with employees.

During Training

• Include managers in parallel sessions that explain program content and reinforcement strategies.
• Provide structured tools managers can use immediately.

After Training

• Equip managers with coaching guides, performance support materials, and feedback templates.
• Create accountability for skill application through manager check-ins.

Brandon Hall Group research shows that mentoring/coaching and on-the-job training—both requiring manager involvement—rank among the most highly effective learning modalities.

2. Simplify Manager Responsibilities

Managers are overloaded. Learning reinforcement must be practical and time-efficient.

High-performing organizations provide:

• Structured conversation guides with specific questions and discussion points.
• Microlearning reinforcement assets that take minutes, not hours.
• Performance support tools managers can share during team meetings or one-on-ones.
• Clear accountability metrics that connect to existing performance goals.

This approach removes friction and makes development conversations manageable.

EI Powered by MPS designs learning solutions that include comprehensive manager enablement tools, ensuring that skill development extends beyond formal training into sustained performance improvement.

As a Brandon Hall Group Platinum Smartchoice® Provider, EI Powered by MPS works with organizations to build manager-supported learning ecosystems that drive measurable business outcomes and create lasting capability growth.

3. Build Manager Capability to Coach and Develop Talent

Managers cannot support learning effectively if they lack coaching skills themselves.

Brandon Hall Group research on learning team competencies reveals that coaching and mentoring, business acumen, and consulting skills rank among the most highly valued capabilities across learning organizations. These same capabilities are essential for frontline and mid-level managers.

Organizations must invest in developing:

• Coaching fundamentals. How to ask powerful questions, listen actively and guide problem-solving.
• Feedback delivery. How to provide specific, actionable and timely input on performance.
• Development planning. How to identify skill gaps and create focused improvement plans.
• Recognition strategies. How to reinforce progress and celebrate skill application.

These capabilities strengthen manager effectiveness across all responsibilities, not just learning support.

4. Connect Learning Reinforcement to Performance Goals

When skill application is measured and tied to business outcomes, managers prioritize development conversations.

Effective approaches include:

• Including learning reinforcement responsibilities in manager performance expectations.
• Tracking skill application metrics alongside operational KPIs.
• Recognizing managers who successfully develop team capability.
• Making development impact visible in performance reviews and talent discussions.

Brandon Hall Group research shows that organizations with excellent business alignment maintain robust measurement frameworks connecting learning to performance metrics.

5. Create Manager Communities of Practice

Managers need support from each other. Peer learning communities help managers:

• Share reinforcement strategies that work.
• Troubleshoot challenges in real time.
• Access additional resources and tools.
• Build confidence in their role as development partners.

These communities create momentum and normalize development-focused management. Brandon Hall Group research confirms that leveraging internal expertise through communities of practice is a common approach among organizations achieving excellent business alignment.

6. Measure Manager Impact on Learning Outcomes

To sustain manager engagement, organizations must demonstrate the connection between manager support and business results.

Key metrics include:

• Skill application rates for teams with high versus low manager engagement.
• Time to proficiency for employees with active manager coaching.
• Performance improvement velocity across different teams.
• Employee engagement and retention linked to development opportunities.

When managers see evidence of their impact, engagement increases.

The Business Case for Manager-Enabled Learning

Brandon Hall Group research on learning modalities reveals that on-the-job training, mentoring/coaching, and experiential learning consistently rank among the most highly effective approaches. These modalities share a common thread: they all require active manager involvement.

Organizations that successfully activate managers as learning partners achieve measurable advantages:

• Faster capability development. Skills are applied immediately, significantly reducing time to proficiency.
• Higher training ROI. Learning investments produce stronger performance outcomes.
• Improved retention. Employees who receive manager coaching are more engaged and less likely to leave.
• Stronger bench strength. Continuous skill development builds internal talent pipelines.
• Better business agility. Organizations can upskill and reskill quickly as priorities shift.

Without manager involvement, even the best learning programs underperform.

Moving from Awareness to Action

Brandon Hall Group research shows that award-winning programs delivered financial impacts ranging from $75,000 to over $1.9 million through improved operational efficiency, reduced time-to-proficiency and enhanced customer experiences. Those outcomes are directly influenced by how well managers support skill application in daily work.

Recognizing that managers are the missing link is the first step. Equipping them to succeed is what drives results.

Organizations ready to:

• Engage managers as active learning partners?
• Provide practical tools and coaching frameworks?
• Measure manager impact on skill development and business outcomes?

Explore research and advisory insights from Brandon Hall Group at www.brandonhall.com.

To design learning solutions with integrated manager enablement and performance support, connect with EI Powered by MPS at www.eidesign.net.

When managers are equipped and engaged, learning becomes performance improvement. And performance improvement becomes competitive advantage.

The post Why Managers Are the Missing Link in Your Learning Strategy appeared first on Brandon Hall Group.

Here’s a problem most organizations won’t admit they have: They’re running 17 different AI training programs at once. Different teams, different frameworks, different terminology, all supposedly working toward the same goal. And the result? Confusion instead of competence.

This isn’t a hypothetical scenario. Brandon Hall Group Smartchoice® Preferred Provider Docebo recently highlighted a global financial services firm where multiple business units independently created training programs for generative AI, each with its own approach, language, and governance frameworks. Employees working across these units found themselves navigating contradictory mental models rather than building actual capability. If this sounds familiar, you’re not alone.

The fragmentation problem runs deeper than redundant training programs. What Docebo identifies in their latest analysis resonates strongly with the patterns we’ve observed at Brandon Hall Group: Organizations are experiencing a three-dimensional fragmentation crisis that threatens their ability to capitalize on AI investments.

The Three Faces of Training Fragmentation

These three distinct sources of fragmentation each create their own set of business problems.

Vertical fragmentation occurs when different organizational levels receive disconnected training. Executives develop one understanding of AI capabilities while frontline employees get a completely different picture. This misalignment between strategic vision and operational execution creates a gap that widens with each implementation decision.

Horizontal fragmentation emerges when functional departments develop incompatible training approaches. Sales learns one set of AI tools and terminology, customer service learns another, and operations learns a third. The result isn’t just inefficiency — it actively prevents the cross-functional collaboration that AI-enabled work requires.

Technical fragmentation happens when multiple AI tools demand different training approaches without a unified competency framework. Employees find themselves learning contradictory patterns, trying to reconcile different mental models for similar tasks across various tools.

Our research at Brandon Hall Group confirms that organizations consistently struggle with siloed systems that limit effectiveness. We’ve documented how companies face data quality issues, inconsistent data formats, and legacy systems that can’t support modern applications. When training efforts mirror these technical silos, the problem compounds itself.

Why Traditional Change Management Won’t Work

AI implementation isn’t like previous technological shifts. Historical change management assumed a linear progression — introduce the innovation, integrate it, stabilize it, move on. Training could follow along methodically, one phase at a time.

AI adoption doesn’t work that way. It unfolds rapidly and simultaneously across organizations, creating a constant state of transformation rather than discrete events. This demands what Docebo calls “continuous change management” — an approach where human capability development must lead technological implementation, not follow it.

This shift from episodic to continuous change aligns with our findings about skills-based organizational structures. Traditional job-based structures give way to capability-focused models that organize work around skills rather than fixed roles. Brandon Hall Group research shows organizations moving toward comprehensive skills inventories, project-based assignments, and performance management focused on skill development rather than task completion.

But here’s the challenge: you can’t build that kind of organizational agility with fragmented training. You need integration at scale.

From Scalable Efficiency to Scalable Learning

For decades, competitive advantage came from optimizing production and minimizing costs — the manufacturing-era model. AI fundamentally changes this equation. In Docebo’s Scalable Learning model, business combines human innovation with artificial intelligence to transform how work gets done. AI takes over routine tasks. Humans focus on continuous learning, adaptation, and creative problem-solving. Speed of learning becomes the primary competitive differentiator.

The Brandon Hall Group AI progression model reveals that organizations evolve through distinct phases — from manual processes to automated processes to intelligent processes. From traditional hierarchical structures to network structures to ecosystem structures. From experience-based decisions to data-driven decisions to AI-augmented decisions.

Each phase requires different capabilities, different skills, different training approaches. But — and this is the critical point — these evolutions happen simultaneously across different parts of the organization. You can’t manage this with traditional, sequential training programs. You need a unified approach that can flex and adapt across all these dimensions at once.

The Capability Academy Solution

Capability Academies — structured learning ecosystems centered around business-critical capabilities rather than traditional training initiatives offer the flexibility and adaptability necessary for this type of transformation. Unlike conventional programs, these academies deeply align with organizational goals and are often organized around functional areas of the business. What makes Capability Academies different? They create dynamic partnerships between L&D and core business functions, transcending fragmented approaches. They establish cohesive learning ecosystems that align with strategic imperatives while fostering continuous adaptation.

This approach addresses a reality we see repeatedly in our research: executives often view capability building through the lens of isolated initiatives—training programs, pilot projects, knowledge-sharing activities. They fail to recognize the interconnected nature of these elements. Adaptive organizations cultivate these components as an integrated system of continuous growth.

Brandon Hall Group data from winners of our  HCM Excellence Awards® shows that organizations achieving strategic alignment with AI initiatives report universal success. But getting there requires more than good intentions. It requires infrastructure that supports three essential skill clusters that Docebo identifies:

Human skills enable effective interpersonal interaction, including communication, conflict resolution, and emotional intelligence. As AI handles more routine tasks, these uniquely human capabilities become more valuable, not less.

Conceptual skills allow employees to see entire concepts, analyze problems, and find creative solutions. Strategic thinking, systems thinking, and creative problem-solving enable people to navigate complexity that AI can’t easily replicate.

Technical skills provide the knowledge to work with specific tools and systems. While AI changes which technical skills matter, the need for technical proficiency doesn’t disappear — it evolves.

Our competency frameworks at Brandon Hall Group define four levels of AI capability: AI Aware, AI Enabled, AI Proficient, and AI Expert. Each level builds on the previous one, creating a progression that moves from basic understanding to strategic innovation. But you can’t build this progression effectively in silos. Different departments creating their own versions of these levels just recreates the fragmentation problem at a higher level.

Three Models for Integration Success

Docebo identifies three key integration models that leading organizations use in their Capability Academies:

Unified knowledge architecture creates a single source of truth for AI competencies and use cases that spans organizational boundaries while allowing functional specialization. This resonates with our findings about integrated HRIS systems with analytics that connect all functions through API integrations and cloud-based infrastructure.

Federated governance establishes clear decision rights for AI training that balance central coordination with functional autonomy. Our research shows that comprehensive AI governance frameworks with defined roles, responsibilities, and decision-making processes are essential at the strategic phase of maturity.

Cross-functional learning pathways develop learning journeys that connect AI literacy foundations with function-specific applications and cross-functional collaboration scenarios. This addresses what we document as the shift from hierarchical structures to networked work models where AI enables coordination across traditional boundaries.

The Implementation Reality

Moving from fragmented training to integrated capability academies isn’t just a technical challenge—it’s an organizational transformation. There are four key actions that align with the most successful implementations:

Establish clear integration ownership at the executive level. This isn’t something that can be delegated down the chain. It requires formal accountability, ideally through an expanded CLO role.

Conduct an integration audit. You can’t fix fragmentation you haven’t identified. Organizations need comprehensive evaluation of current training approaches to find critical gaps.

Develop a phased roadmap. Address immediate fragmentation risks while building toward sustainable integration. Our implementation frameworks at Brandon Hall Group show that successful transformations move through assessment, foundation, and scale phases — typically 9 to 18 months for meaningful progress.

Model integration behavior. The executive team needs to demonstrate the cross-functional collaboration they’re asking the organization to embrace.

The Continuous Learning Imperative

What Docebo knows and what Brandon Hall Group research consistently validates is that traditional training approaches are insufficient for the AI era. Organizations need learning systems that adapt quickly, provide ongoing support, and integrate seamlessly with daily work.

This means just-in-time learning resources embedded in workflows. Communities of practice for sharing knowledge. Mentorship programs connecting experienced and new practitioners. Continuous skill assessment and certification.

Our data shows the evolution clearly: from manual record-keeping to LMS management to automated workflows to autonomous administration. From manual content creation to templates to GenAI at scale to AI content factories. From classroom delivery to e-learning to adaptive learning to ambient learning that happens invisibly in the flow of work.

But none of these phases work effectively in isolation. Integration isn’t optional — it’s fundamental to capturing value from AI investments.

What This Means for Your Organization

If you’re experiencing any form of training fragmentation — vertical, horizontal, or technical — you’re not alone. But you also can’t afford to wait. The gap between organizations that integrate their capability development and those that remain fragmented widens every quarter.

Docebo‘s framework for Capability Academies provides a practical path forward. It acknowledges the reality of continuous change while offering structured approaches to manage it. The shift from Scalable Efficiency to Scalable Learning isn’t just conceptual — it’s operational, measurable, and essential for maintaining competitive advantage.

The post From Fragmented Training to Enterprise Capability:</br> Why Integration Matters Now appeared first on Brandon Hall Group.

By Michael Rochelle, Chief Strategy Officer and Principal Analyst, Brandon Hall Group; David Wentworth, Managing Director, Learning and Talent, Brandon Hall Group; and Dr. Marline Duroseau, Managing Director, HR and Leadership, Brandon Hall Group

We recently participated in a briefing with Adobe’s Learning Manager team to evaluate their platform capabilities, market positioning and strategic direction. This assessment examines their technology stack, customer deployments, AI roadmap and support approach based on the information presented.

Platform Architecture and Core Capabilities

Adobe Learning Manager has been in the market for over 10 years, originally launched as Adobe Captivate Prime before being rebranded in 2022. The platform is built on a multi-tenant database architecture with services deployed inside a virtual private cloud, providing tenant isolation at both database and business logic layers.

The technical infrastructure includes:

• Relational database management system for core business logic optimized for bulk operations.
• Elasticsearch for course metadata and Vector DB for semantic search.
• CDN storage for content repository.
• A robust Agentic AI roadmap based on Google ADK.

The platform supports comprehensive APIs for headless learning deployment, webhooks for real-time event export, and native connectors for Salesforce, Workday, ADFS, Zoom, Microsoft Teams, Adobe Connect and content providers including LinkedIn Learning, getAbstract and GO1.

Customer Deployments and Use Cases

Adobe has an impressive list of more than 800 customers worldwide, including some of the top names across industries.

Just for perspective, the customer list of Adobe Learning Manager includes:

1. Five out of the top 15 medical device manufacturers.
2. Four out of the top 15 tech companies.
3. Four out of the top 15 pharma companies.
4. Three out of the top 10 QSR chains.
5. Two out of the top 5 advertising agencies.
6. Two out of the top 5 retailers.

Strategic Value for HR and Leadership Development

From an HR leadership and financial management perspective, Adobe Learning Manager delivers several high-impact business outcomes:

Workforce Capability Visibility: The AI-ready data lake and analytics capabilities provide HR leaders with unprecedented visibility into workforce capabilities, skill gaps, and development velocity. This enables data-driven workforce planning and more accurate succession planning by identifying high-potential talent based on learning agility and skill acquisition rates.

Leadership Pipeline Development: The platform’s ability to create personalized learning paths and track cohort progress makes it particularly valuable for leadership development programs. HR teams can design and monitor differentiated development tracks for emerging leaders, mid-level managers, and senior executives while measuring program effectiveness across cohorts.

Total Cost of Ownership Optimization: The customer examples demonstrate 60-85% platform consolidation (e.g., one of the top tech companies replaced 16 platforms with Adobe Learning Manager; a leading healthcare chain replaced 3; and a top tech manufacturer consolidated three different use-cases into one single platform). From a CFO perspective, this represents substantial savings in licensing costs, implementation expenses, integration maintenance and administrative overhead. The standardization also reduces vendor management complexity and strengthens negotiating position. In a recent economic impact study conducted on Adobe Learning Manager, it was found that the platform provides a 337% Return on Investment and starts delivering within a 6-month timeframe.

Skills-Based Talent Strategy Execution: With native skills management capabilities, HR leaders can operationalize skills-based talent strategies — moving beyond job titles to competency-based development, deployment, and compensation decisions. This aligns with the broader shift toward skills-based organizations that Brandon Hall Group research shows improves talent mobility by 40% and reduces time-to-productivity by 30%.

Measurable Business Impact: The AI roadmap for Adobe Learning Manager has an Impact Evaluator agent  that will come up in the course of time whose focus will be on post-training behavior analysis, addressing the perennial HR challenge of demonstrating training ROI. By analyzing actual work products and role play performance, HR teams will be able to quantify learning impact on business outcomes rather than relying solely on completion rates and satisfaction scores.

Extended Enterprise

The platform supports easy extensibility for multiple use-cases and has been extensively used to train up organizational customers and partners at scale. The platform is also headless in nature and comes with an Experience Builder feature that allows admins to create custom pages for each audience segment. These features align with findings from Brandon Hall Group research, which points out that organizations that execute a targeted personalized program achieved as much as a 94% increase in partner enrollment and a 137% increase in program logins.

AI Strategy and Roadmap

Product Roadmap Across Three Themes

The platform has rich features that can be bucketed around three themes:

AI-Powered enterprise learning:

• Semantic search using AI and Vector databases
• Admin and learner AI assistants for operational tasks and Q&A based on conversational interfaces
• An AI-powered virtual coach that allows learners to practice sales pitches/brush up on their knowledge with a virtual AI coach in a controlled environment
• AI-driven recommendations based on products, roles, levels, skills and peer behavior

Experiences That Engage and Drive Adoption:

• Extensibility APIs using webhooks to export enrollment, completion, pass/fail events in real-time
• Highly scalable non-logged-in experiences for customer and partner academies
• Experience Builder for creating custom and branded learning experiences
•  Deep integration with Adobe Experience Manager Sites for a headless learning experience

Productivity Tools for Learning at Scale:

• LTI integration supporting Learning Tools Interoperability standard
• Structured feedback templates for learner feedback
• Ability to import external skills
• Cohort Progress Tracker for monitoring user cohorts across common course/path sets. This feature is particularly valuable for HR leaders managing leadership development cohorts and high-potential programs where tracking comparative progress is essential.
• Custom certificates with graphical WYSIWYG editor
• Granular role management allowing multiple custom roles per user with user group scoping

Support Structure

Adobe provides tiered support offerings:

Standard Support includes a dedicated Customer Success Manager (ongoing account management), Product Consultant (30/60/90 day limited engagement for initial launch advisory), Technical Consultant (technical setup assistance, including SSO configuration and data connectors during onboarding) and Expert Support (ongoing product support for administrators with P1 24×5 support and P2-P4 business hours support).

Paid Add-On Services include LMS Administrator (contract co-term for ongoing setup and administration), TAM Support (technical support relationship management) and Named Support (end-user technical support).

System integrators provide additional configuration and implementation services on a paid basis.

For HR leaders evaluating total cost of ownership, the tiered support model allows organizations to right-size support investments based on internal capabilities. Organizations with strong internal L&D teams can leverage Standard Support, while those requiring more hands-on assistance can add services incrementally. This flexibility enables better alignment between support costs and organizational maturity with the platform.

Market Position Assessment

Adobe Learning Manager serves three primary target personas:

1. Marketing, Customer Success, and CX Leaders addressing product adoption and customer journey improvement.
2. Partnership and Alliance Leaders enabling partners, franchisees and sales personnel.
3. Learning and Development Heads managing upskilling, reskilling and compliance.

Importantly, this tri-modal positioning creates strategic value for CXOs, CHROs and HR leaders who increasingly own customer experience, partner enablement and employee development outcomes. The platform’s ability to serve all three personas with a single solution enables HR to expand its strategic influence beyond traditional boundaries while demonstrating enterprise-wide impact.

The platform competes in both traditional L&D and extended enterprise (customer education and partner training) segments.

Integration Ecosystem

Adobe Learning Manager integrates natively with the Adobe product suite, including Adobe Captivate (one-click publishing to ALM), Adobe Connect (virtual training with consolidated VILT data), Adobe Experience Manager Sites (headless learning deployment), Adobe Commerce (monetization of training programs) and Marketo Engage (marketing automation).

The platform also  integrates with   Microsoft Teams and Salesforce applications allowing learners to access learning programs directly from their Teams and Salesforce interfaces, white-labeled mobile apps and support for SAML SSO, Adobe ID, ALM ID and social login authentication methods.

For HR leaders managing enterprise technology ecosystems, these native integrations reduce implementation risk and time-to-value. The Workday connector is particularly strategic, enabling seamless data flow between HR systems of record and learning platforms to support skills-based talent strategies. The Salesforce integration and Adobe Commerce integration support revenue-generating customer education programs that help HR demonstrate P&L impact.

Strategic Outlook

Adobe Learning Manager presents a technically sophisticated platform with proven large-scale deployment capability. The customer roster includes complex, multi-use case implementations at global enterprises. The AI roadmap is comprehensive and architecturally sound, built on a modern data platform foundation.

The dual positioning across L&D and customer education markets provides strategic diversification with clear value propositions for each buyer persona. The platform’s content creation and delivery capabilities, combined with native Adobe ecosystem integration, represent strong differentiators in the LMS market.

The consolidation trend in learning technology favors platforms capable of replacing multiple point solutions and Adobe’s customer wins demonstrate this capability effectively. The company is well-positioned to capitalize on this trend through clear messaging around operational efficiency, proven ROI in customer education deployments and robust skills management capabilities that help organizations execute skills-based talent strategies successfully.

Bottom Line for HR and Leadership Development Leaders

Adobe Learning Manager warrants serious consideration for HR leaders addressing three converging imperatives:

First, organizations implementing skills-based talent strategies need integrated platforms that connect skills assessment, development and deployment.

Second, CFOs and boards increasingly expect HR to demonstrate measurable business impact. The platform’s  proposed Impact Evaluator, monetization capabilities and consolidation potential provide concrete mechanisms for quantifying learning ROI and transforming L&D from cost center to value driver.

Third, the future of work requires HR to enable learning at the speed of business change. Adobe’s AI  tools , particularly the  AI Assistnt for learners (currently in Beta)  and Content Builder, address the scalability challenge of providing personalized, just-in-time learning across large, distributed, multi-generational workforces.

For HR leaders seeking a strategic learning platform that supports workforce capability building, leadership development and business value creation, Adobe Learning Manager merits inclusion in formal evaluation processes.

The post Adobe Learning Manager: A Strategic Assessment</br> of Platform Capabilities and Market Position appeared first on Brandon Hall Group.

More than half of Gen Z workers already use AI regularly to solve problems at work. According to Brandon Hall Group research,75% are using it to upskill, more than any other generation. And yet, in boardrooms and L&D strategy sessions across the country, the conversation is still largely “should we adopt AI?” For Gen Z, that question was settled years ago. The real question, the one that matters, is why so many organizations are still asking it.

That generational gap isn’t just a curiosity. It’s a competitive liability. The workforce is changing not just in who is showing up, but in what they expect, how they learn and what it takes to keep them. The organizations that recognize this are pulling ahead. The ones still deliberating are falling behind, quietly, and faster than they realize.

AI isn’t a threat to learning. Outdated learning is.

The long-form, one-size-fits-all training model is losing its effectiveness and the data supports it. When employees click through courses just to hit a completion metric, organizations aren’t building capability; they’re just checking a box. There’s a better way, and the technology to do it already exists.

AI enables personalized content, real-time skill gap identification, and learning experiences that meet people where they actually are. 57% of Gen Z workers are already using generative AI in their day-to-day work; they didn’t wait for a corporate rollout. The expectation for tech-enabled, personalized experiences isn’t a differentiator anymore. It’s the baseline.

As for what AI means for the future of L&D professionals, Brandon Hall Group’s Chief Strategy Officer, Michael Rochelle, put it best: “I was cutting down a tree with an axe, and someone handed me a chainsaw.” AI isn’t replacing the people doing the work; it’s upgrading the tools they have to do it. That frees learning leaders to focus on what genuinely requires human judgment: strategy, culture, and the work of truly developing people. That’s not a disruption; that’s an opportunity.

Skills are the new currency, and Gen Z isn’t waiting to cash in.

Here’s what the data says about Gen Z at work: 88% value on-the-job learning and practical experience for skill development. Learning and development consistently rank in their top three reasons for choosing an employer. This generation didn’t show up to collect a paycheck and wait for a promotion. They showed up to grow, and if the current job isn’t delivering that, 1 in 3 Gen Z workers plans to change jobs within the next year.

The writing is on the wall. Loyalty isn’t bought with perks or titles anymore. It’s earned through genuine investment in people’s growth. Skills-based learning does exactly that; it ties development to tangible, stackable capabilities instead of vague “growth opportunities” that never quite materialize. As Rachel Cooke, Brandon Hall Group’s Chief Operating Officer, framed it at this year’s HCM Excellence Conference, “retention is a system outcome: learning + career growth + inclusion + culture + wellbeing + flexibility.” Perks are a band-aid. That formula is the real solution.

Mike Cooke, CEO of Brandon Hall Group, captured the stakes clearly: “traditional talent strategies, anchored in rigid job descriptions and antiquated competency frameworks, have become obsolete. Skills-based organizations outperform traditional competitors in agility, innovation, and talent attraction.” That’s not a prediction. That’s already the reality for the organizations leading the pack.

Traditional learning was built around roles. Train someone to do their job, check the box, repeat. But that model assumes the job stays the same, and nothing about the current landscape supports that assumption. Industries are shifting, roles are evolving, and the skills that matter today might look completely different in three years. Organizations that build around skills rather than static job descriptions aren’t just keeping up. They’re building something that actually lasts.

The best organizations stopped waiting for permission.

Working at Brandon Hall Group makes it clear just how much the best organizations are already getting this right and how much runway still exists for everyone else. The research is consistent: high-performing organizations invest more in learning, leverage technology more strategically, and tie development directly to business outcomes. They treat L&D not as a compliance checkbox but as a competitive advantage.

The research backs it up consistently. High-performing organizations invest more in learning, use technology more strategically, and connect development directly to business outcomes. As one conference session put it, award-winning L&D teams have shifted from “order-takers to strategic partners, embedding learning into the daily workflow and proving their impact in terms executives care about.” L&D isn’t a line item to these organizations. It’s a competitive advantage.

The ones falling behind share a common trait: they’re waiting. Waiting for the budget, waiting for leadership alignment, waiting to see if the technology “matures.” Meanwhile, the workforce keeps moving; with or without them.

The future of L&D isn’t a vision statement or a strategic priority for next fiscal year. It’s a decision that gets made today. Build learning that reflects how people learn, tie it to skills that matter, and use the technology that’s already here. From a Gen Z perspective, the time to act was yesterday, but today will do.

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In our research and advisory work at Brandon Hall Group, one theme keeps surfacing across organizations at every maturity level: the greatest risks in AI adoption aren’t malicious actors, but well-intentioned early adopters operating without adequate knowledge or guardrails.

Data consistently reinforces this reality. Roughly 70 percent of AI implementation challenges stem from people and process issues rather than technology itself, a clear signal that organizations are attempting to solve workforce problems with technical solutions alone.

And it’s not working.

Only 4 percent of companies have developed AI capabilities generating real business value. That gap isn’t about better algorithms or more powerful models. It’s about the foundation that Docebo, a Brandon Hall Group Smartchoice Preferred Provider®, identifies as non-negotiable: strategic AI literacy and governance working in tandem.

The Literacy-to-Fluency Journey Is Real

Docebo’s distinction between AI literacy and AI fluency captures something critical that we’ve observed across 600 organizations: competency development isn’t binary. You don’t simply “know AI” or not. There’s a progression that organizations must intentionally design and support.

AI literacy is the baseline. It means understanding fundamentals, recognizing risks, and applying policies. Every person in the organization needs this foundation. Our research shows that organizations in the earliest maturity phases (what we call Reactive/Ad Hoc) struggle precisely because this universal baseline doesn’t exist. People are experimenting with consumer-grade AI tools without enterprise security, unable to evaluate vendor claims, introducing bias without awareness.

But literacy alone isn’t enough. The concept of AI fluency where employees move from basic usage toward creative application and strategic innovation maps directly to what happens when organizations build genuine capability. Fluent employees don’t just use AI tools. They initiate process improvements, articulate AI’s role clearly, challenge and adapt AI outputs, and develop novel applications aligned with strategic goals.

We see this progression play out in our competency data. Organizations that invest in moving people from awareness to enabled to proficient capabilities report fundamentally different outcomes. They’re not just deploying AI. They’re innovating with it.

Governance Evolves With Capability

Here’s where Docebo’s framework becomes particularly valuable: the recognition that governance must evolve as organizational capability matures. Too many organizations treat governance as a static compliance checklist. Really, there are three distinct phases in the journey to true AI capability: centralized governance with clear rules, controlled expansion with domain-specific guidelines, and distributed intelligence with principles-based guidance.

Our research across organizations at different maturity phases validates this progression. At the beginning, most organizations have no formal AI governance framework at all. The result? Uncontrolled risk exposure. These organizations face the challenge of using consumer AI tools without enterprise security, lacking AI literacy, and having no budget allocated for AI initiatives.

By the time basic governance structures emerge with initial policies and guidelines, risk assessment processes begin including AI considerations. But here’s the challenge Docebo identifies well: inconsistent governance application across pilot projects. Those newly established frameworks get applied unevenly or incompletely across different initiatives. The opportunity centers on developing standardized AI governance processes and decision criteria that work consistently across all initiatives.

Once organizations achieve what Docebo describes as comprehensive governance with defined roles and responsibilities. Our governance structure model shows exactly what this looks like: Board oversight, executive leadership engagement, specialized committees for AI ethics, technical standards, and risk management. When an AI Strategy Committee meets monthly for strategic review and quarterly for comprehensive assessment, with clear decision authority for investments, that’s mature governance enabling innovation within appropriate boundaries.

The most mature organizations, representing just 29 percent of organizations in our research, have automated monitoring, real-time risk management, and predictive compliance systems. Some are approaching what Docebo describes as “adaptive frameworks that empower employees to make responsible decisions within established parameters.” They’ve shifted from prescriptive rules to principles-based guidance because their workforce has the literacy and fluency to operate responsibly.

The Timeline Matters

Docebo’s five concrete steps for executives are actionable precisely because they acknowledge something many organizations miss: this takes time and sustained commitment. You can’t build enterprise-wide AI capability in a quarter. This isn’t about checking boxes. It’s about building the foundation that makes everything else possible.

Learning and Development (L&D) professionals have a unique chance to simultaneously create AI programs and participate in governance structures. This dual role ensures that training aligns with governance requirements. It also provides governance teams with practical insights from the learning environment.

Measuring What Matters

We must move beyond simple training completion rates toward evaluating actual capability development. It’s important to track employee development from current AI levels to target levels and identify key skill gaps with assigned development priorities. But the real value comes from what Docebo describes: skills assessments that challenge employees with realistic scenarios, measuring how they navigate AI ethical dilemmas and apply appropriate judgment.

This assessment approach reveals something important: competency develops along a continuum, not through binary achievement. You can’t simply declare someone “AI competent” after completing a course. Capability builds through application, experimentation, feedback, and refinement.

The Integration Imperative

What makes Docebo’s approach right for this moment is the recognition that AI literacy, fluency, and governance must develop simultaneously, not sequentially. You can’t wait to build governance frameworks until after literacy programs complete. The frameworks inform what literacy programs must teach. Literacy programs prepare employees to operate within governance boundaries.

Our research confirms this integration imperative across all maturity phases. Organizations attempting sequential implementation consistently struggle. The successful transformations we document are those that build both capabilities and guardrails together from the start.

This extends to the cross-functional coordination that Docebo emphasizes. AI transformation affects multiple functions simultaneously, requiring sophisticated coordination to avoid conflicts and ensure synergies. Our enterprise AI governance framework shows that effective AI Strategy Committees include the Chief Executive Officer, Chief Human Resources Officer, Chief Information Officer, Chief Financial Officer, Business Unit Leaders, and AI Subject Matter Experts. This isn’t bureaucracy. It’s recognition that AI transformation is an enterprise challenge requiring enterprise-level coordination.

The Challenges Are Predictable

Governance challenges across maturity phases consistently line up. Organizations in early maturity struggle with lack of AI literacy and foundational understanding, fear and resistance among staff, absence of AI strategy or governance frameworks, and inability to evaluate AI vendor claims. While organizations which are slightly more mature face inconsistent governance application across pilot projects. As maturity increases, the challenges shift. Organizations must manage comprehensive framework complexity while maintaining innovation agility or encounter challenges managing interconnected AI systems, ensuring meaningful human oversight in automated processes, and adapting to rapidly evolving capabilities.

The good news? These challenges are predictable. Organizations can prepare for them. The governance opportunities Docebo outlines for each phase provide clear direction: establish foundational AI ethics and risk management frameworks early, develop standardized governance processes that scale, implement predictive governance models for proactive risk mitigation, and ultimately lead industry transformation in AI governance standards and practices.

Why This Matters

Some analysts and advisors forecast that AI will add close to $16 trillion to global economic output by 2030. That value won’t materialize automatically. It requires exactly what Docebo describes: organizations that build strategic AI literacy and governance as the foundation for responsible innovation.

The competitive advantage isn’t going to organizations with the most advanced AI technology. It’s going to organizations with workforces that can effectively collaborate with AI, innovate using AI, and operate responsibly within appropriate governance frameworks.

Docebo’s framework provides the roadmap. Our research validates it works. The question is how quickly can your organization build these foundational capabilities before the competitive gap becomes too wide to close.

The post Why Docebo Gets AI Literacy and Governance Right</br> — And Why It Matters Now appeared first on Brandon Hall Group.