At Phenom’s Analyst Day in Philadelphia, the conversations did not feel like a typical product update or technology showcase.

They felt like a signal.

A signal that we are moving into a new phase of work, one where the conversation is no longer centered on automation, but on something much more fundamental: how work itself is designed, executed, and experienced.

Throughout the sessions, demos, and discussions, one idea kept surfacing in different forms:

We are not just optimizing work anymore. We are beginning to re-architect it.

A Shift in Thinking: From Tasks to Systems

For years, organizations have approached HR technology as a way to make existing processes faster, post jobs quicker, screen candidates more efficiently, automate workflows.

But what became clear during Analyst Day is that this model is reaching its limits.

Traditional automation works well in stable environments. But in reality, hiring conditions change, candidate behaviors evolve, regulations shift, and business priorities move faster than static systems can keep up with.

What is emerging instead is a more dynamic approach, one that connects data, intelligence, and execution into a coordinated system rather than a series of disconnected tools.

Phenom’s perspective reflects this shift: moving from isolated automation toward an orchestration model, where intelligence can be applied across different workforce scenarios without needing to rebuild processes each time.

The implication is significant.

This is not just about doing the same work faster.

It is about enabling organizations to adapt how work gets done in real time.

Agentic AI and the Role of Human Judgment

One of the most consistent themes across sessions was the rise of agentic AI, AI that doesn’t just analyze or recommend, but actively participates in executing work.

Naturally, this raises the question many leaders are asking:

Where does that leave people?

What stood out to me was not a narrative of replacement, but one of rebalancing.

AI agents are being positioned to handle repeatable, high-volume, and time-sensitive tasks, screening, scheduling, assessments, coordination, while humans remain responsible for judgment, context, and decision-making.

At one point, a simple but powerful idea surfaced:

Intelligence is no longer scarce. Judgment is.

That distinction reframes the conversation entirely.

Because if intelligence can be scaled, then the real differentiator in organizations becomes how leaders and teams apply judgment, how they interpret signals, make decisions, and navigate ambiguity.

This is where the human element does not disappear. It becomes more valuable.

The Reality Check: Technology Is Not the Hard Part

If there was one theme that came through just as strongly as the technology itself, it was this:

The biggest barrier to transformation is not the technology. It is the people.

In discussions with CIOs and business leaders, the tension between systems and adoption was clear. Organizations are not struggling to access AI capabilities — they are struggling to integrate them into how work actually happens.

There is hesitation.

There is fear of getting it wrong.

There is uncertainty about where to start.

And perhaps most importantly, there is a need for alignment, between HR, IT, and the business, on what success actually looks like.

As one conversation reinforced, when organizations introduce new technology without evolving training, behaviors, and expectations alongside it, they create friction instead of progress .

This is where leadership becomes critical.

Because transformation is not just about implementing systems.

It is about guiding people through change.

Trust, Governance and the New Risk Landscape

Another layer that cannot be ignored is trust.

As AI becomes more embedded in hiring and workforce decisions, new risks are emerging, ones that many organizations are not yet fully prepared to handle.

From synthetic candidates to AI-assisted interview responses, the hiring landscape itself is evolving. What used to be edge cases are becoming more common, more sophisticated, and more difficult to detect.

At the same time, expectations around governance are increasing.

Organizations must now think about:

• How decisions are made and validated
• How bias is monitored and mitigated
• How transparency is maintained across AI-driven processes

Phenom’s approach emphasizes explainability, compliance, and auditability as core components of its model.

But stepping back, the broader message is clear:

AI strategy without governance is not innovation. It is exposure.

Closing the Gap Between Strategy and Execution

Perhaps the most strategic takeaway from Analyst Day is how AI is beginning to close a long-standing gap inside organizations, the gap between strategy and execution.

Many leaders today have a vision for where they want their workforce to go:

• More agile
• More skilled
• More aligned to business priorities

But they lack the visibility and infrastructure to operationalize that vision at the task level.

What is emerging now is the ability to connect workforce data, skills, and work activities in a way that allows organizations to make more precise decisions:

• What should be automated
• What should be augmented
• What should remain human

This is not a theoretical exercise. It is becoming a practical requirement.

As AI adoption accelerates, organizations are being forced to answer a deeper question:

What work actually creates value, and who or what should be doing it?

A Leadership Moment, Not Just a Technology Moment

As I reflect on the experience, what stands out most is that this is not just a technology shift. It is a leadership moment.

Because the organizations that will succeed in this next phase will not be the ones that adopt AI the fastest. They will be the ones that integrate it the most thoughtfully.

They will:

• Balance efficiency with humanity
• Pair intelligence with judgment
• Build trust alongside capability

And they will recognize that while AI may reshape how work is done, it does not replace the need for leadership.

If anything, it raises the bar.

Final Reflection

Analyst Day made one thing very clear: We are no longer asking, “How do we use AI?”

We are now asking, “How do we redesign work in a way that allows both humans and AI to perform at their best?”

That is a far more complex question. But it is also a far more important one.

And for HR leaders in particular, it represents an opportunity to step into a new role, not just enabling the workforce, but actively shaping how it evolves.

The post From Automation to Orchestration — What Phenom’s Analyst Day Reveals About the Future of Work appeared first on Brandon Hall Group.

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.

TL;DR: Talent agility isn’t about moving fast. Instead, it’s about building integrated systems that allow organizations to assess, develop and redeploy people in response to changing business needs. Brandon Hall Group research shows that award-winning organizations treat talent management as a connected business system, not an HR program, combining strategic alignment, experiential development, personalized learning paths and multi-level measurement to build workforces that can adapt at the speed the business requires.

Defining Talent Agility Beyond the Buzzword

Every organization wants an agile workforce. Few can define what that actually means, or even measure whether they have one.

Talent agility is the organizational capacity to identify, develop and deploy the right capabilities at the right time. It requires knowing what skills exist today, anticipating what skills will be needed tomorrow and closing the gap before it becomes a crisis.

That sounds straightforward. In practice, it requires a level of integration across talent systems that most organizations haven’t achieved.

Why Traditional Approaches Fall Short

Most organizations manage talent in silos. Learning sits in one system, performance management in another, succession planning in a third. Career development happens informally, if at all.

The result is predictable: When business priorities shift, organizations scramble. They can’t quickly identify who has the capabilities they need. They don’t know which skills gaps are most urgent. And they lack the infrastructure to develop people fast enough to keep pace.

Brandon Hall Group research on award-winning talent management programs reveals the scale of the problem. Only 42% of organizations report above-average to excellent alignment between learning and business goals. When learning and talent systems operate independently, alignment becomes nearly impossible to sustain.

What Agile Talent Systems Look Like

Organizations that demonstrate real talent agility share a common trait: They treat talent management as an integrated capability-building system rather than a collection of separate programs.

The most effective organizations connect learning management, goal-setting, career progression and succession planning into a unified infrastructure. These aren’t standalone tools. Instead, they function as components of a single system. When goal setting connects to career pathways, career pathways connect to succession planning and succession planning connects to learning investments, organizations gain the visibility and responsiveness that talent agility demands.

Building Agility Through Assessment

You can’t develop what you haven’t measured. Brandon Hall Group research on award-winning team development programs found that a majority conduct pre-program self-assessments and skills gap analyses, while many layer in competency mapping, 360-degree feedback and team effectiveness assessments.

The most sophisticated organizations combine these approaches, pairing self-assessment with stakeholder feedback, competency evaluation with performance data and individual capability measurement with team diagnostics. This multi-source approach creates the accurate, actionable baseline that agile talent decisions require.

Experiential Learning as an Agility Accelerator

Classroom training alone doesn’t build agility. Skills must be practiced, applied and refined in real work contexts to transfer into actual capability.

Brandon Hall Group research confirms this. Among award-winning team development programs, 71% use interactive and experiential workshops as their primary development approach. Scenario-based exercises, action learning projects and one-on-one coaching are also common features.

Organizations that rely primarily on formal instruction will always lag behind those that embed development in the flow of work. When employees learn by solving real business problems, capability development and business value creation happen simultaneously.

Manager Involvement: The Multiplier Effect

Talent agility depends heavily on managers. They control application opportunities, feedback quality and accountability for skill development. Without active manager involvement, even well-designed talent systems lose their impact at the point of execution.

Yet Brandon Hall Group research shows that time is a significant or heavy constraint on manager involvement in learning for more than half of survey respondents. Organizations building talent agility must address this tension directly, not by adding responsibilities but by simplifying how managers support development through structured conversation guides, performance support tools and clear accountability metrics tied to existing goals.

Measuring Talent Agility

This is where most organizations struggle. They can measure training completion rates. They can track engagement survey scores. But measuring talent agility requires connecting inputs to outcomes across multiple dimensions.

Based on Brandon Hall Group research, effective measurement frameworks address four levels:

• Participation and Engagement Quality — Not just completion, but depth of involvement, including individual development plans and talent pool creation.
• Capability Development — Measuring actual skill gains through competency-based assessments, 360-degree feedback and assessment centers.
• Behavior Change and Application — Tracking whether new capabilities transfer to workplace performance through real-world application that can be observed and measured.
• Business Impact — Connecting talent activities to operational outcomes. 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.

The organizations that measure across all four levels gain something the others don’t: evidence of whether their talent systems are actually producing the agility the business needs.

Technology as an Enabler, Not a Solution

Technology infrastructure matters for talent agility, but it’s an enabler, not a substitute for strategy. The key is integration. When learning platforms connect to performance systems, performance data informs development recommendations and dashboards give both employees and managers visibility into progress, technology creates the information flow that agile talent decisions require. Without integration, even sophisticated tools produce fragmented data that hinders rather than helps.

Making Talent Agility Operational

Organizations ready to build genuine talent agility should focus on three priorities.

First, connect the systems. Learning, performance management, succession planning and career development must function as integrated components, not independent programs. Brandon Hall Group research consistently shows that organizations achieving exceptional results operate these as a unified system with shared data and aligned objectives.

Second, invest in the application layer. Experiential learning, coaching, manager involvement and action learning projects are what convert knowledge into capability. Without them, training investment doesn’t translate into organizational agility.

Third, measure what matters. Move beyond activity metrics to capability and impact metrics. Track skill application rates, time to proficiency, succession pipeline readiness and the business outcomes that talent investments are designed to produce.

EI Powered by MPS designs learning ecosystems that connect formal development with on-the-job application, manager enablement and sustained performance support. These are the integrated approach that talent agility requires.

As a Brandon Hall Group Platinum Smartchoice® Provider, EI Powered by MPS partners with organizations to build adaptive talent systems that respond to shifting business priorities and deliver measurable capability growth.

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

To design integrated learning and talent solutions with built-in agility, connect with EI Powered by MPS at www.eidesign.net.

Talent agility is more than a program. It’s a capability built through integrated systems, experiential development and measurement that connects people growth to business results.

The post What Talent Agility Really Means (And How to Measure It) 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.

We are excited to announce the release of the 2026 Women in Leadership Magazine, Shaping Tomorrow.

This new edition captures many of the insights, conversations, and leadership perspectives shared during the Brandon Hall Group Women in Leadership Summit. The magazine brings together executives, HR leaders and learning professionals who are working to advance leadership development and create more inclusive workplaces.

The Women in Leadership Summit has always been about more than just discussion. It is a place where leaders share real experiences, research insights and practical strategies that organizations can use to strengthen leadership pipelines and support the next generation of women leaders.

The magazine reflects those conversations.

Why This Conversation Matters

Despite progress, organizations still face challenges when it comes to advancing women in leadership.

Our research shows that only 28 percent of organizations report being highly effective at developing women for senior leadership roles and fewer than 14 percent have reached more than 50 percent representation of women in senior leadership positions.

These numbers highlight why continued dialogue, research and leadership development are so important.

Throughout the magazine, you will hear from leaders across organizations, including Pfizer, Bank of America, Verizon, Johnson & Johnson and Weatherby Healthcare, sharing their perspectives on topics such as:

• Leading through disruption and change
• Building workplace cultures rooted in empathy and innovation
• The role women must play in shaping the future of AI leadership
• Sustainable leadership and avoiding burnout
• Practical approaches to inclusive leadership

These stories and insights are designed to help leaders translate ideas into action.

Looking Ahead to the 2026 Summit

The conversations that inspired this magazine will continue later this year.

On November 17 and 18, 2026, Brandon Hall Group will host an expanded leadership experience at the Boca Raton Innovation Campus.

The event will introduce a new format designed to deepen leadership conversations.

The program will begin with the Leadership Development Summit on November 17, a full-day event focused on leading through complexity, disruption, and digital transformation.

On November 18, the Women in Leadership Summit will bring together executives for a focused half-day discussion on advancing women at the highest levels of leadership. Topics will include executive presence, sponsorship, influence and navigating the path to the C-suite.

Together, these events create a unique opportunity for leaders to connect research with real-world leadership challenges.

Explore the Magazine

If you are passionate about leadership development, advancing women in leadership or building stronger and more inclusive organizations, we invite you to explore the magazine.

You can download the Women in Leadership Magazine at https://web.brandonhall.com/women-in-leadership-magazine and learn more about the upcoming summit here: https://web.cvent.com/event/91deb7fe-3746-4f5a-b1b5-376aa30d2196/summary/

We hope the stories and insights in these pages inspire new ideas, meaningful conversations and continued progress in shaping the future of leadership.

The post Brandon Hall Group™ Releases the Women in Leadership Magazine: Shaping Tomorrow appeared first on Brandon Hall Group.

There’s a hard truth that most organizations discover too late: a brilliant strategy means nothing if your workforce can’t execute it.

Docebo’s latest thinking on aligning workforce capability with business strategy hits on something we’ve been documenting in our research. The organizations that succeed with AI aren’t the ones with the best technology plans. They’re the ones that understand capability building is execution infrastructure, not an afterthought.

The Execution Gap Nobody Talks About

Our research shows that only 25% of organizations have reached what we call Phase 3 progression — where strategic alignment actually exists and talent functions directly support business goals. Organizations achieving this alignment report universal success with their AI initiatives.

The correlation isn’t accidental. Organizations that crack the code on strategy-to-capability translation have built a systematic way to turn business ambition into workforce readiness.

Why L&D Needs a Seat at the Strategy Table

Docebo’s framework expands the role of the Chief Learning Officer to program owner and execution partner. This is about recognizing that in an AI-transformed business landscape, workforce capability has become a critical dependency for every strategic initiative. When the legendary CEO Jack Welch appointed the first CLO at GE in 1994 (Steve Kerr), he indicated learning wasn’t a downstream consequence but a key mechanism for executing strategy. Too many CLO’s today sit too far away from strategic leadership.

We’ve tracked HR organizations through five progression phases when it comes to their progression with AI. The Brandon Hall Group AI Progression Model for Empowering Excellence outlines five phases of HR progression in adopting AI technologies.

• Phase 1: Reactive/Ad Hoc – HR focuses on essential administration with limited AI readiness; no formal governance exists.
• Phase 2: Standardized – HR establishes consistent policies and begins small-scale AI pilots; basic governance structure starts to form.
• Phase 3: Defined/Strategic – HR aligns policies with business goals, utilizing data-driven decision-making and predictive analytics.
• Phase 4: Managed/Transformational – HR operates as a strategic partner with advanced analytics and a mature governance framework.
• Phase 5: Optimized HR Excellence – Continuous innovation defines HR operations, with fully autonomous AI systems and self-correcting governance.

In phases 1 and 2 of AI Progression, HR and talent functions operate as service providers with process-driven decision-making. Organizations stuck at these levels struggle with AI adoption because they’re treating capability development as reactive training rather than proactive execution readiness.

The shift happens at Phase 3, where the role begins to transform from service provider to strategic partner. Decision-making becomes data-driven. Talent management moves from reactive hiring to proactive planning. Most importantly, the talent function earns a seat at the leadership table because it’s demonstrably contributing to value creation, not just supporting operations.

From Strategy to Capability Mandates

The concept of Capability Mandates provides the translation layer most organizations lack. These aren’t training plans. They’re structured definitions of the specific skills, behaviors, knowledge, and systems required to deliver on a strategic priority.

When a business commits to expanding digital services or integrating AI automation, the CLO should be asking: What decision-making processes will change? Which roles will evolve? What technical proficiencies and behavioral shifts are required? What measurable performance improvements need to happen in 90 or 180 days to signal success?

Organizations that succeed don’t just deploy technology. They systematically map how that technology changes work, then build capabilities in parallel. We call this “redesigning work for human-AI collaboration,” and it requires analyzing current roles, identifying automation potential, and designing future workflows that leverage both human and AI capabilities.

We’ve found that successful organizations assess time allocation across key tasks, determine AI automation potential for each, and then identify the human-unique value that remains. A customer service role might have 40% of tasks automated by AI, but the remaining 60% shifts toward complex problem-solving and relationship building, capabilities that need deliberate development not ad hoc training.

Building Integration Infrastructure That Actually Works

Docebo’s three integration models serve as your execution infrastructure: unified knowledge architecture, federated governance, and cross-functional learning pathways. This matters because fragmentation kills execution. AI tools get rolled out across departments without shared language or governance. Each function interprets the strategy differently. Training remains tool-specific with no coherent competency framework. The result? Slowed execution, uneven adoption, and rising cognitive load on employees.

Organizations reaching Phase 4 progression where transformation becomes sustainable have solved this coordination problem. They’ve moved beyond pilot projects to enterprise-wide deployment by building sophisticated integration capabilities. They establish governance that balances consistency with autonomy. They create innovation processes for identifying emerging AI applications. They develop internal expertise while maintaining strategic vendor partnerships.

The critical success factors for an execution infrastructure include leadership commitment backed by visible resource allocation, employee engagement through comprehensive support systems, and systematic approaches with clear project discipline. Without this connective tissue, capability-building remains fragmented, well-intentioned but out of sync with enterprise priorities.

The Real Challenge: Enabling Adaptive Execution

Organizations now operate in constant flux, where steady-state execution models no longer apply. The CLO must enable performance readiness (delivering results with current tools and workflows), change resilience (absorbing disruption and adapting to the unexpected), and business agility (reorienting priorities as conditions evolve). These organizations demonstrate what Docebo calls “performance in motion”. They are supporting real-time learning and enablement through tools that integrate into the flow of work. They build adaptive systems that respond to shifting tools, roles, and conditions. They measure agility, readiness, and speed-to-proficiency, not just completion rates.

What This Means for Investment Priorities

Both Docebo’s framework and our research point to the same conclusion: Organizations need to fundamentally rethink how they structure and fund capability development.

Our investment framework recommends allocating 40-50% of budgets to quick wins (3-6 month timeframe), 30-40% to strategic bets (1-2 years), and 10-20% to transformational initiatives (2-3 years). This portfolio approach recognizes that capability building must balance immediate performance needs with long-term transformation.

These investments only deliver returns when capability building is structurally connected to business strategy. We’ve found that organizations must demonstrate strategic alignment between AI initiatives and business objectives, with leadership actively participating in strategy development. This isn’t about better communication but integrated planning where workforce readiness is treated as a strategic input, not an output.

The Path Forward

Elevate the CLO role in strategic planning. Organizations with talent leaders at the executive table consistently outperform those where the function reports several levels down. The correlation with AI success rates is too strong to ignore. Brandon Hall Group HCM Excellence Award winners see a 58% average improvement in efficiency and a 58% reduction in time spent on manual tasks. All of this contributes to an average cost reduction of $200,000 by program area.

Build Capability Mandates into operating rhythms. Make capability definition part of how initiatives are scoped, resourced, and reviewed. Connect these mandates directly to business outcomes. Create systematic processes for this translation that are intentional, not ad hoc or reactive.

Operationalize integration infrastructure. Unified knowledge architecture, federated governance, and cross-functional pathways aren’t optional nice-to-haves. They’re core execution infrastructure. Organizations that build these systems early avoid the fragmentation that plagues later-stage adoption efforts.

Design for performance in motion. Support real-time learning through tools integrated into work itself. Organizations reaching higher maturity phases have shifted from static training models to adaptive systems that respond to evolving needs.

Rescope the entire L&D operating model. It’s time to ask the tough questions. Where does it sit organizationally? Who does it report to? How is it funded? What priorities drive it? These are foundational questions that create the framework for addressing every need.

Making It Real

The research is clear. Organizations that align capability building with business strategy don’t just perform better with AI, they fundamentally transform how work gets done. They move from reactive training functions to proactive performance systems. They replace siloed programs with integrated infrastructure. They build workforces that can execute strategy, not just understand it.

Docebo’s framework provides a practical roadmap for this transformation, grounded in real implementation challenges. Their emphasis on the CLO as execution partner, Capability Mandates as translation mechanisms, and integration models as infrastructure all align with what we’ve documented in organizations that successfully navigate AI transformation.

So will you make this shift deliberately or be forced into it by competitive pressure? The window for deliberate action is narrowing. The organizations pulling ahead right now are the ones treating capability development as strategic infrastructure, not operational support.

If your workforce can’t execute your AI strategy, you don’t have an AI strategy. You have a plan that depends on execution readiness you haven’t built yet.

The post When Strategy Meets Capability:</br> Why Execution Readiness Matters More Than You Think 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.

Artificial intelligence is rapidly transforming how organizations manage people, develop skills and make workforce decisions.

For HR, learning and talent leaders, the conversation has shifted. The question is no longer whether AI will impact work. The focus is now on how organizations can adopt AI responsibly and apply it in ways that create measurable business impact.

To help leaders navigate this transition, Brandon Hall Group released a new executive magazine titled The Transformational Impact of AI on HR, Learning and Talent.

The magazine captures research insights, expert perspectives and real-world examples shared at the 2025 Brandon Hall Group AI Summit. It explores how organizations are applying artificial intelligence across HR, learning and talent management and what it takes to scale AI effectively.

Download the magazine:
https://web.brandonhall.com/ai-transformation-in-hr-magazine

AI Adoption in HR Is Accelerating

Artificial intelligence is already embedded in many workforce systems. HR teams are using AI to improve talent acquisition, personalize learning, support employee development and enhance performance management.

As adoption grows, leaders are recognizing that successful AI transformation requires more than new technology. Organizations must address governance, data readiness, workforce skills and strategic alignment.

“AI has moved from experimentation to execution,” said Mike Cooke, CEO of Brandon Hall Group. “We’re calling this the Year of Progression because organizations are no longer asking whether to adopt AI, they’re advancing along a clear maturity path. Our research shows AI is already embedded in systems like performance management and employee development. The real advantage now belongs to leaders who intentionally design human-AI collaboration and treat progression as their operating model.”

The next phase of AI adoption will depend on how well organizations prepare their workforce, align leadership and build responsible frameworks for using AI at scale.

The AI Progression Model for AI Readiness

A key concept explored in the magazine is the Brandon Hall Group AI Progression Model, developed by Chief Strategy Officer Michael Rochelle.

The model is informed by research from more than 800 organizations and provides a structured framework for understanding AI readiness and maturity.

Organizations typically move through several stages as they adopt AI. These stages range from early experimentation to enterprise-wide integration and optimization.

“Progression is about readiness, not hype,” Rochelle said. “Organizations do not stall because they lack tools. They stall because they lack alignment. The AI Progression Model helps leaders evaluate governance, people, process, technology and organizational adaptability. When AI becomes a strategic capability rather than a collection of pilots, meaningful transformation becomes achievable.”

This framework helps HR and business leaders evaluate their current level of AI maturity and identify the next steps for advancing their AI strategy.

How AI Is Transforming HR and Talent Management

Artificial intelligence is reshaping nearly every aspect of human capital management. Organizations are exploring new ways to apply AI across HR, learning and talent development.

The magazine highlights key areas where AI is already making an impact.

• Talent acquisition and candidate screening
• Personalized learning and skill development
• Workforce analytics and decision support
• Employee development and performance management
• HR operations and service automation

Successful organizations are combining these applications with strong governance practices and responsible AI policies. They are also investing in workforce readiness to ensure employees and leaders understand how to work effectively alongside AI systems.

Lessons from Organizations Leading AI Transformation

The magazine also includes examples from organizations that are successfully applying artificial intelligence across the enterprise.

These organizations share several common characteristics:

• They focus on data quality and integration before scaling AI solutions.
• They establish clear governance structures for AI use.
• They prepare employees and leaders for changes in how work is performed.
• They align AI initiatives with measurable business goals.

By taking a structured approach to AI adoption, these organizations are able to move from experimentation to sustainable transformation.

Join the AI in HR Summit to Continue the Conversation

Many of the insights featured in the magazine were first discussed at the 2025 Brandon Hall Group AI Summit.

The conversation continues at the upcoming AI in HR Summit, October 15, 2026, Boca Raton Innovation Campus

The AI in HR Summit is a one day event focused on practical strategies for implementing artificial intelligence across HR, learning, and talent functions.

Attendees will explore topics including:

• AI strategy and workforce transformation
• Responsible and ethical AI governance
• AI use cases across HR and talent management
• Workforce readiness and skill development
• Emerging AI technologies shaping the future of work

The event brings together HR, business, learning, and technology leaders who are responsible for guiding AI adoption in their organizations.

Register for the AI in HR Summit:
https://web.cvent.com/event/cf2f625b-0b2a-4d4a-97b0-64a54e77e8d4/summary

Download the AI Transformation Magazine

AI adoption in HR is accelerating. Leaders who understand how to implement AI responsibly and strategically will be best positioned to create lasting impact.

The Transformational Impact of AI on HR, Learning and Talent magazine provides research insights, practical guidance and real-world examples to help organizations move forward with confidence.

Download the magazine:
https://web.brandonhall.com/ai-transformation-in-hr-magazine

The post The Transformational Impact of AI</br> on HR, Learning and Talent 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.