Most organizations have spent the past two years asking the same question about AI: What can it make? Course content, translated modules, scenario scripts, voiceovers. The results have been real and the time savings genuine.

But AI-generated content is still just content. Someone still has to find it, assign it, track it and figure out whether it actually moved the needle. The administrative burden does not disappear because the authoring got faster. And the learner who needs guidance right now, in the middle of a real workflow, still has to stop what they are doing and go searching.

That is the limit of AI as a content tool. It is also the starting point for AI agents.

What Agents Actually Do

An AI agent is not a content generator. It is an active participant in a workflow. It takes inputs, reasons about them and does something: recommends, answers, triggers, analyzes. The distinction matters because the value proposition is fundamentally different.

Brandon Hall Group research on HCM Excellence Award® winning AI learning programs makes this concrete. In every program studied, 100% of organizations achieved measurable improvements in learning outcomes, operational efficiency and cost savings. But the programs that delivered the strongest results shared a specific characteristic: They did not rely on a single AI capability. They combined natural language processing, machine learning, adaptive algorithms and contextual assistance into integrated systems. Thirty-six percent already employed multi-agent architectures, with specialized agents handling distinct functions across the learning ecosystem.

The payoff was operational. Efficiency gains typically hit within three to six months of implementation and averaged 15 to 25 percent reductions in training staff time on administrative work. That capacity went toward personalized coaching and strategic planning. Those gains do not trace back to faster content creation. They trace back to agents handling work that previously required a person.

The Problem with Generic AI

Enterprise AI tools built for everyone have a structural problem in corporate learning: They do not know your organization.

They do not know that your compliance catalog was reorganized last quarter. They do not know that your onboarding path for field technicians differs from the one for call center staff. They cannot connect to your HR system to check a learner’s development history before recommending a next step. They handle generic queries well and organizational ones poorly.

The imc Agent Builder, built natively into the imc Learning Suite, is designed around the opposite premise. L&D teams create, configure and manage their own agents, ones grounded in the organization’s actual data, processes and learning catalog. As Christian Wachter, Co-CEO of Scheer IMC, puts it: “Our customers build their own agents. They know their processes, their data and the people who work with them. That expertise cannot be replaced by generic solutions. We provide the platform, the tools and the methodological guidance.”

The result is an agent that knows what your organization knows and can act on it.

Agents for Every Role That Touches Learning

The imc Agent Builder is built around three deployment scenarios, each targeting a different pressure point in enterprise L&D.

For learners, the agent evaluates prior experience and available learning time, then recommends relevant content from the organization’s own catalog with direct links. The recommendation reflects where the learner actually is, not a keyword match against a course library.

For platform users, the agent resolves operational questions in real time: how to register colleagues for a course, what the next required compliance training is, where to find a specific resource. It can answer directly, walk through a process, or trigger a service request. It knows the organization’s workflows, not just the platform’s help documentation.

For L&D and administrative teams, the agent handles structured analysis. An administrator asking whether the compliance catalog has gaps gets a usable overview, not a list of search results to sort. That is where the research-documented 91 percent rate of administrative burden reduction among top programs comes from: not from creating content faster, but from cutting the cognitive and operational overhead around it.

Built to Stay Manageable

One of the more consequential decisions in the imc Agent Builder’s architecture is what it avoids: a single all-purpose agent trying to do everything.

Roman Muth, CTO of Scheer IMC, is direct about why: “When a process changes, you adapt individual building blocks rather than the entire system. That significantly improves long-term manageability.” The platform uses a modular design where specialized agents handle specific roles, each accessing only the content and processes relevant to its function. The result is better traceability, better stability and easier adaptation as the organization’s needs evolve.

The headless architecture of the imc Learning Suite means agents are not tied to a single interface. They operate wherever employees already work: the learning portal, the intranet, service systems. AI becomes part of existing workflows rather than a separate application to manage. Organizations can connect agents to HR systems, ERP solutions and custom data sources via MCP servers and built-in governance controls handle PII protection, content filtering and access restrictions.

That last piece matters. Brandon Hall Group research is clear that organizations achieving excellence in AI learning treat governance as a foundational requirement, not a compliance checkbox addressed after deployment.

Getting from Pilot to Scale

Brandon Hall Group research on top-performing programs is consistent on one point: user experience design and change management predict adoption success more reliably than technical sophistication. The organizations that get the most from AI agents invest in collaborative development, real user testing and transparent communication about what the agents can and cannot do.

Scheer IMC, a Brandon Hall Group Eminence Partner, builds this into the imc Agent Builder delivery model through four structured phases: identifying where AI agents create genuine value in the specific organizational context, designing agent behavior and data integration, piloting with real users under monitored conditions and scaling with the explicit goal of enabling organizations to manage and extend their agent ecosystems independently.

That independence is the point. The organizations sustaining AI learning gains are not the ones that deployed an agent and moved on. They are the ones that can adapt as their needs change, add use cases and adjust agent behavior without starting over. Ninety-one percent of top programs in Brandon Hall Group research outlined continued AI investment strategies and expansion plans. The infrastructure has to support that.

The Shift Worth Making

Most enterprise AI spending in L&D is still at the content layer. The organizations moving ahead are at a different layer, one where agents actively participate in workflows, know the organization’s data and do work on behalf of learners, managers and administrators in real time.

The imc Agent Builder is built for that layer. It puts configuration and optimization in the hands of the L&D team. It connects to the systems that make agents genuinely useful. And it scales without requiring a rebuild every time something changes.

AI-generated content was a reasonable first step. Agents are where the real productivity gains are.

The post AI in L&D Has a Problem. Agents Are the Answer. appeared first on Brandon Hall Group.

Let’s be honest about compliance training for a second. When you say those two words to just about anyone in an organization, including learning professionals, you get some version of the same reaction: a groan, an eye roll, maybe a resigned “oh, that.” It’s become the necessary evil of L&D. Something you check off, not something you actually think about.

And yet, no other learning program touches as many people or covers as much ground. Compliance training reaches nearly every employee in nearly every organization and it spans an enormous range, from a fairly benign walkthrough of the employee handbook all the way to life-and-death procedures depending on your industry. The stakes vary, but they’re never really low. Get it wrong and, depending on the situation, you’re looking at anything from a formal warning to fines in the millions to the actual inability to operate your business.

Here’s the paradox we’re stuck with: This is one of the most universal, highest-stakes categories of learning in the entire organization and it’s also the one we’ve been the least willing to touch. While the rest of L&D has spent the last several years getting more learner-centric, more strategic, more tied to real outcomes, compliance training has largely just… stayed put. Check the box. Get it done. Move on.

It Doesn’t Have to Be This Way

Here’s the thing: People don’t learn differently because the content happens to be regulatory in nature. The fact that something is compliance-related doesn’t mean the neuroscience of how people absorb, retain and apply information suddenly works differently. So, there’s no real excuse for treating it differently in terms of design. That means bringing in the same things we already value everywhere else in learning: multiple modes of delivery, interactive formats and a genuine reliance on how people actually think, remember and apply what they’ve learned.

That also means finally letting go of completion as the measure of success. For years we’ve talked about moving away from “did everyone finish it” as the metric for learning broadly and we’ve made real progress there; except in compliance, where completion is often still treated as the finish line. It’s time to shift that mindset. It can’t just be about finishing. It has to be about improving performance. Strategy, content, technology and how we measure all of it need to be realigned around actual business needs, even for content that feels mundane on the surface. Because here’s the truth: even the most boring-seeming compliance topic exists for a real business reason. The framing just can’t be “take this training or else.” That approach has run its course.

To be fair, we’ll never get away from this completely. Some compliance training is dictated wholesale by a government agency or third-party regulator and there’s only so much creative license you have there. But for everything else,  and that’s most of it, there’s no reason we can’t take a fundamentally different approach.

What Good Actually Looks Like

I want to share a few examples pulled from our Excellence Awards library, because they make the case far better than I can in the abstract.

ByteDance (TikTok) partnered with Siyona Tech to build a gamified privacy compliance program called PrivacyFlix — yes, styled after Netflix — and rolled it out in seasons. Season one covered things like data retention and protections for young users; season two moved into data security and AI governance. Instead of dumping everything on employees at once, they released it as an ongoing experience, complete with diverse narrative styles (a Wild West movie trailer among them), badges for progress and a design that worked seamlessly across desktop, laptop and tablet so people could move between devices without losing their place. The results back it up: 97.3% of surveyed employees reported measurable knowledge gain, satisfaction scores hit 4.6 out of 5, genuinely outstanding for compliance training, and the company saw a real cultural shift, with employees treating privacy as part of the culture rather than a box to check, along with reduced exposure to regulatory fines and reputational risk.

FanDuel, working with Labyrinth Training, took a character-driven approach to anti-money laundering training, content that’s often mandated and can feel dry by nature. Rather than static PDFs, they built relatable, retail-sportsbook-style scenarios and created a persistent universe of recurring characters that showed up across their anti-money laundering, anti-bribery and privacy programs, almost like a Marvel Cinematic Universe for compliance. Their Director of Financial Crimes Compliance noted that employees started asking who the characters were based on; a sign people were actually paying attention, not just clicking through. The company’s own framing captures it well: Story beats obligation. Compliance training shifted from something people dreaded to something people actually talked about with each other.

Blue Cross Blue Shield of North Carolina, partnering with Aptara, took a different route. Back in 2023, they realized their attempts to gamify training had become overly complex and were eating up too much seat time, so they stripped things down and leaned into microlearning: a format that, for years, compliance professionals insisted couldn’t work (“people need to sit for 12 hours to be in compliance”). A standout piece was their “test me” pathway: a pre-assessment that diagnosed what employees already knew so they weren’t forced through redundant content. That single move cut down on wasted time significantly and made the experience feel far more respectful of people’s time. They also built hands-on scenarios around AI and data privacy risk, phishing identification and industry-specific issues like data destruction protocols, letting people practice judgment rather than just reading rules.

Nordea, working with InfoPro Learning, focused on Infosec training and reported some of the clearest financial outcomes of the bunch: more than $300,000 saved through design and process streamlining, another $240,000 saved through reduced seat time and nearly $70,000 saved by focusing content more precisely on what people actually needed. On top of that, year-over-year NPS improved and IT tickets related to security issues dropped by more than 50%. The program was highly modular, localized into four Nordic languages and built around real-world decision loops and AI-assisted character storytelling; something that’s become far more feasible now that AI can handle branching narrative logic that used to require building every path by hand.

None of these organizations lost sight of the regulatory requirement. They just refused to stop there.

Five Strategies Worth Stealing

Pulling from these examples and our broader research, a few high-level strategies stand out for anyone looking to rebuild their approach:

1. Provide context. Explain why the training matters to the business and to the individual; not just that it’s required.
2. Use a wider variety of tools and modalities. Click-through PowerPoints alone don’t work anymore, if they ever really did.
3. Lean on neuroscience and cognitive science, the same way you would for any other learning program.
4. Recognize achievement. Completing compliance training shouldn’t just mean avoiding a stern email. Genuinely acknowledge people for getting it done.
5. Measure a variety of outcomes, not just completion rates.

That last point is worth sitting with. In our research, we asked companies a simple question: is your compliance training effective at meeting your organization’s needs? When we looked at how the “yes” group and the “not really” group measure success, the differences were telling. Companies that say their compliance training is *not* effective tend to lean almost entirely on less strategic measures: Did we meet regulatory requirements, do we have a safe work environment, are we mitigating risk? Important, but not exactly ambitious. Companies that say their training *is* effective are far more likely to also track strategic outcomes: business performance, employee development, corporate citizenship, becoming an employer of choice. They’re not just trying to stay out of trouble; they’re using compliance training as a genuine extension of their learning strategy and their culture.

The Bottom Line

It really is possible to reimagine almost all of compliance training; not despite the regulatory pressure, but alongside it. The organizations that are getting this right aren’t ignoring their compliance obligations. They’re just refusing to let “we have to” be the whole story. They’re asking what outcomes actually matter, applying the same design thinking they’d apply to any other learning program and treating the people going through the training like people, not boxes to check.

The regulatory requirement isn’t going away. But the gap between “we’re in compliance” and “our people actually understand and act on this every day” is exactly the space where the real work happens. That’s where the opportunity is.

The post Reimagining Compliance Training: Why “Take This or Else” Doesn’t Work Anymore appeared first on Brandon Hall Group.

Most organizations discover the limits of their learning platform the hard way. The system worked fine for 2,000 employees in a single country. Then came an acquisition, a global expansion or a mandate to extend training to dealer networks and distribution partners. Suddenly the platform that handled the basics is running into walls that no amount of configuration is going to move.

Brandon Hall Group research consistently finds that organizations underestimate how fundamentally different the requirements become once learning needs to operate at genuine enterprise scale. The features that look sufficient during an evaluation tend to be the ones that surface their limitations first during a real implementation. By then, the contract is signed.

The failure modes are predictable. They fall into three categories: governance, data segregation and performance under load. Understanding how they materialize before they become operational problems is how organizations avoid the expensive cycle of implementing, struggling and replacing.

Governance: The First Thing That Fractures

Governance problems in enterprise learning tend to start small and accumulate quietly. An organization launches a global platform and grants administrative access to regional L&D teams because central administration is too slow. Those teams begin customizing workflows, content catalogs and user groups for their local needs. Within 18 months, what was supposed to be one platform is functionally operating as a dozen different ones, with no consistent compliance reporting, no unified view of workforce capability and no way to enforce enterprise-wide standards without overriding the local adaptations that keep the regions engaged.

This is an architecture problem. A platform without genuinely decentralized administration built into its governance model forces organizations into an impossible choice between central control and local relevance. Most platforms resolve this tension poorly because they were not designed for it.

The imc Learning Suite from Scheer IMC, a Brandon Hall Group Eminence Partner, is built around what Scheer IMC calls decentralized administration with centralized governance. Each tenant, department or audience group can be given its own portal environment, its own catalog structure, its own workflow logic and its own administrative access, all operating within a governance layer that allows central administrators to maintain oversight, enforce standards and extract enterprise-wide analytics. The 43 individual government department portals that operate within Ireland’s Civil Service deployment on a single imc Learning Suite instance illustrate this model in practice. Each department manages its own learning environment. Central government retains unified visibility and control.

Brandon Hall Group identifies this capability as one of the clearest differentiators between platforms that are genuinely built for enterprise scale and those that approximate it. The difference shows up not in demonstrations but in deployments where the governance requirement is real.

Data Segregation: More Than a Technical Requirement

Extended enterprise learning introduces a data problem that most L&D leaders do not fully appreciate until they are in the middle of it. When a single platform serves internal employees, external dealers, distribution partners and customers simultaneously, the question of which data belongs to which audience is not just a technical configuration task. It is a legal, regulatory and commercial obligation.

A dealer in one country should not be able to see the training records of a dealer in another country operating under different regulatory requirements. A channel partner should not have access to internal employee development data. A customer training program with commercial pricing logic should not expose the internal cost structures of the L&D function. These are the daily operational realities of extended enterprise learning at scale.

STIHL, the world’s top-selling chainsaw brand with more than 20,000 employees and a presence across 160 countries, came to the imc Learning Suite with exactly this challenge. The company was running six separate learning systems to serve its internal workforce and its 50,000-dealer global network. Each system existed partly because the data segregation requirements were different enough that a single, simpler platform could not accommodate all of them within a unified architecture. The consolidation onto imc Learning Suite required working through the requirements of multiple national operating companies to arrive at an architecture that could serve all audiences appropriately, with each learner population able to access exactly the content they needed and nothing they should not see.

Leandra Deininger, STIHL’s E-Learning and Training Manager, described the requirement plainly: all users, from internal STIHL staff to dealers, had to be able to find exactly the learning content they needed, quickly and easily. The system had to be simple and intuitive to get to grips with. That outcome requires work that happens well below the user interface layer, in the data governance architecture that determines what each user sees and what they cannot access.

For organizations operating under GDPR or similar data sovereignty requirements, the stakes are higher still. Scheer IMC’s European regulatory grounding, its support for client-approved LLMs and its ability to keep data within defined geographic environments address a compliance architecture requirement that organizations in healthcare, financial services and government are finding increasingly material to procurement decisions.

Performance Under Load: Where Scale Becomes Concrete

Performance is the most visible failure mode and, in some ways, the most avoidable. Organizations that properly load-test their platforms before major events rarely have problems. The ones that discover their platform’s limits during a global product launch training rollout or a mandatory compliance deadline do not quickly forget the experience.

At genuine enterprise scale, performance is not simply a matter of server capacity. It is a function of architectural decisions made years earlier about how the platform handles simultaneous user sessions, how content is delivered across geographies with different network characteristics and how administrative operations interact with learner-facing functionality under peak load. A platform that functions well at 500 simultaneous users may degrade significantly at 5,000, and the behavior at 25,000 simultaneous users, as one global retailer requires during hardware product launch training events, is something most platforms have never been tested against.

Scheer IMC’s support for up to 25,000 simultaneous users without service degradation is not a theoretical benchmark. It reflects an architectural investment in the infrastructure required to serve large, geographically distributed learner populations during the peak demand scenarios that large enterprises actually experience. For organizations planning a global consolidation onto a single platform, this is the kind of operational evidence that matters more than feature comparison matrices.

The Consolidation Variable

Underlying all three failure modes is a consolidation challenge that Brandon Hall Group research identifies as one of the most pressing strategic issues in enterprise learning today. Many large organizations entered the current decade managing anywhere from three to 30 separate LMS installations across business units, geographies and acquired companies. The pressure to consolidate is real: cost, data governance requirements and the need for a single source of truth for learning data all point in the same direction. But consolidation is harder than it looks.

The reason organizations ended up with multiple systems is usually that each system was solving a problem the others could not. A compliance-focused system for regulated training. A separate system for dealer certification. Another for customer-facing learning. A fourth inherited from an acquisition. Consolidating them onto a single platform requires that platform to accommodate the requirements that drove each separate system in the first place, all within a unified architecture, without creating new technical debt in the process.

Mercedes-Benz consolidated more than 22 LMS systems onto the imc Learning Suite, serving 250,000 users across a multi-tenant, multi-lingual, multi-brand environment. The outcome was a unified learning data environment that made enterprise-wide analytics possible for the first time and a single compliance architecture governing all training delivery globally. A global hardware and software retailer is currently consolidating four separate Saba Learning installations serving 700,000 users across retail store employees, retail partners and external audiences. These are architectural transformations that require a platform built from the ground up for that level of complexity.

What to Look for Before You Sign

Brandon Hall Group has consistently recommended that organizations evaluate learning platforms against the full weight of their operational requirements rather than a feature checklist. For organizations operating at enterprise scale, that means asking questions that go beyond the demonstration environment.

Governance

Can the platform support genuinely decentralized administration without sacrificing centralized oversight? Can different audience groups operate in separate portal environments within a single platform instance, each with its own catalog and workflow logic, while a central administrator maintains unified reporting and compliance visibility? Ask for reference clients who are running this model in production, not just in theory.

Data Segregation

How does the platform handle multi-audience environments where learner populations should not see each other’s data? What are the mechanisms for enforcing data residency requirements across different geographies? How does the platform manage GDPR compliance for extended enterprise audiences who are not employees?

Performance

What is the documented simultaneous user capacity, and what happens at the edges of that capacity? What is the delivery architecture for learners in geographies with constrained network infrastructure? Has the platform been tested against the peak load scenarios your organization actually faces?

These are not comfortable questions to ask in a sales process. They tend to reveal gaps that solution providers would rather address after contract signature than before. But the organizations that ask them upfront are the ones that avoid the expensive discovery process that comes from finding the answers in production.

Scheer IMC supports more than 12 million learners across 1,300 organizations worldwide and maintains a 98 percent customer retention rate across its active enterprise client base. For organizations facing the governance, data segregation and performance challenges that come with scaling a learning platform to genuine enterprise complexity, that combination of architectural depth and reference client evidence is a concrete starting point for the evaluation process.

The post When Enterprise Learning Scales: What Breaks and How to Design Against It appeared first on Brandon Hall Group.

AI is advancing more rapidly than most organizations can govern, which reflects the current reality in 2026. Learning functions are no exception.

There is significant pressure to adopt AI in L&D. Solution providers are integrating it across their platforms, executives are requesting updates and employees are using AI outside approved channels regardless of organizational approval. L&D leaders now must learn how to implement it in a defensible, trustworthy manner that supports learning outcomes.

This requires competent governance, which most organizations have yet to establish.

What Governance Means in a Learning Context

Governance is commonly viewed as a compliance issue, but it should not be framed solely in that context.

In a learning context, AI governance is the set of policies, structures and practices that determine how AI is used to design, deliver, personalize and measure learning; who is accountable for those decisions; and how the organization guarantees that AI advances learning outcomes rather than merely automating activities.

The scope of governance is broader than many organizations realize. It includes AI embedded in LMS systems, content generation tools, recommendation algorithms, skill data analysis systems and AI used for summarizing or assessing learner work. Each use case poses distinct risks, data considerations and accountability requirements.

Without a framework, organizations are not ungoverned but inconsistently governed, which can be more problematic.

Why This Matters Now

Brandon Hall Group research has documented the rapid spread of AI-enabled learning tools. Dashboard analytics that generate AI-built widgets and actionable insights prove already embedded in leading platforms.

Brandon Hall Group’s analysis of HCM Excellence Award® winners in the Best Use of AI in Learning category shows how far and fast this has moved. AI role-play scenarios and simulations appeared in 54 percent of winning programs. AI content generation and creation in 46 percent. AI personalized learning and recommendations in 42 percent. These are now mainstream practices among organizations recognized for learning excellence and they are generating decisions about employee development, skill recognition and career paths at scale.

AI improves this capability as well as raises questions about data interpretation, generation and reliability. An AI system recommending learning paths based on flawed models actively shapes employee development. Deploying such systems without governance frameworks results in high-stakes decisions lacking clear accountability.

This stresses the need to establish governance now, before AI becomes so integrated into learning operations that implementing accountability retroactively is impractical.

The Components of a Workable Framework

A governance framework for AI-powered learning does not require extensive documentation. It ought to address a few essential questions clearly and assign responsibility for each answer.

• Identify which AI systems are in use and their purposes. Most organizations lack a comprehensive inventory of AI within their learning ecosystems. Platform-embedded AI, content generation tools, analytics layers and third-party integrations must be documented before successful governance can begin.
• Determine what data the AI uses and who owns it. Skills taxonomies, learner profiles, performance data and behavioral patterns may all serve as inputs. Governance requires understanding data flows, usage for model building or refinement and existing protections. This is particularly important when AI is vendor-embedded rather than internally developed.
• Ensure AI decisions are explainable and intentional. Employees directed to specific learning paths, flagged for skill gaps, or assessed on competencies should expect clear explanations. Governance frameworks should  set transparency standards appropriate to the significance of each decision.
• Define accountability for AI errors. No AI system is error-free; skills may be miscategorized, recommendations may be biased and assessments may misunderstand capabilities. Governance frameworks should  form clear reporting channels and accountability systems to ensure errors are identified, investigated and corrected.
• Protect learner consent and privacy. AI systems that adapt learning, extract skills, or analyze behavior process sensitive information. Governance frameworks should  specify how learners are informed about AI use, their opt-out options and data protection measures.

What Good Looks Like in Practice

Instructure, a Brandon Hall Group Eminence Partner, has integrated thoughtful AI governance principles into the design of Canvas Career. The platform offers opt-in controls for AI features, enabling learners and organizations to engage with AI capabilities by choice rather than by default. Instructure refers to this as “AI nutrition facts,” a transparency mechanism that documents AI operations and the guardrails in place.

The company’s policy of not using customer content to train external models represents a significant governance commitment, especially for organizations in regulated industries or those managing sensitive workforce data. This principled approach is what administrative frameworks should require from any AI-enabled vendor in the learning stack.

AI capabilities within Canvas Career include course generation, skill extraction, customized recommendations and an insights feed for administrators. These features operate within a privacy and security framework built on the Canvas foundation in higher education, drawing on decades of experience supporting institutions with rigorous privacy and learner data requirements.

This foundation is critical. Organizations evaluating AI-powered learning platforms should consider not only the AI’s capabilities, but also the vendor’s governance model, transparency regarding AI operations and available recourse if problems occur.

Building Internal Governance Alongside Platform Governance

Governance around solution providers is necessary but not sufficient. Organizations also require internal frameworks to govern how L&D teams use AI, independent of specific platforms.

Brandon Hall Group’s research on award-winning programs consistently points to stakeholder engagement and organizational alignment as critical success factors. Stakeholder engagement appeared in 69 percent of winning programs. CEO and leadership endorsements in 43 percent. Progress communications in 33 percent.

These principles also apply to AI governance. A framework developed solely by L&D and IT will have gaps. Effective AI governance requires input from HR, legal, privacy officers, business leaders and employees. Organizations most likely to deploy AI-powered learning responsibly treat governance as a cross-functional effort, not just a technical checklist.

Feedback mechanisms are also essential. Continuous feedback loops were present in 59 percent of award-winning programs. In AI governance, this means regularly evaluating whether AI systems perform as intended, whether learners trust the tools and whether outcomes match organizational goals. Governance is not a one-time task; it requires ongoing, iterative improvement.

The Opportunity Inside the Obligation

Governance may appear to constrain AI adoption, but this perspective is misleading.

Organizations with clear AI governance frameworks can move more quickly because they have already addressed the questions that typically slow adoption. They understand permissible data use, required transparency for learners and established accountability structures. This clarity reduces friction rather than creating it.

Clear governance also builds the organizational trust necessary for AI-powered learning to succeed. Employees who understand how AI is used in their development, have meaningful opt-in controls and can review the evidence behind their skill assessments are more likely to engage with AI-powered tools than those who perceive the systems as opaque and unaccountable.

The skills-based learning programs that Brandon Hall Group research consistently identifies as high-performing are not just technically sophisticated. They are trusted by the people using them. Governance is what makes that trust possible at scale.

AI adoption in learning will continue to accelerate. Organizations that establish management systems now will deploy AI with confidence in the future, while those that do not will spend that time addressing the consequences of ungoverned systems.

The post Building Governance Frameworks for AI-Powered Learning appeared first on Brandon Hall Group.

Most learning and development leaders can tell you exactly how many courses were completed last quarter. They can tell you average completion rates, time spent in training, even satisfaction scores from post-course surveys. What they usually cannot tell you is whether any of it made a difference.

That gap is rooted in measurement itself. This distinction is more consequential than most organizations realize, setting the stage for deeper issues in L&D effectiveness.

Brandon Hall Group research found that 97% of HCM Excellence Award® winning organizations track progress and completion data, a near-universal foundation. But the organizations pulling ahead are doing more. They are connecting that activity data to skills outcomes, business performance and long-term behavior change, building a measurement picture that goes well beyond the dashboard. Dashboards are full. The opportunity is in what comes next.

This is the measurement paradox at the heart of modern L&D: Organizations are generating more learning data than ever before, but remain largely unable to answer the questions that matter most to business leaders. Did this training close the skill gap we identified? Are the people who completed this program performing differently? Are we building the workforce capabilities the business actually needs?

Activity Is Not Accountability

The traditional L&D reporting model was built around programs, not outcomes. A program launched, employees completed it and a report was filed. That was the accountability loop.

Program-based accountability shows what happened inside the learning system but tells you little about its external impact. Completion rates measure access and compliance, not capability. Satisfaction scores capture experience, not transfer.

Brandon Hall Group research drawing on award-winning organizations recognized for learning excellence shows where best practices are heading. Leading organizations track usage and engagement metrics, conduct ROI analysis and use behavioral analytics to understand how learning transfers to the job. The best are also beginning to deploy predictive modeling to anticipate future skill needs before gaps become visible. This momentum is growing as business leaders increasingly ask for clear evidence of return on investment from talent investments.

Skills gaps now have real operational consequences. Workforce capability is increasingly treated as a strategic asset, not just an HR metric. When L&D cannot demonstrate its contribution to either, it loses influence precisely when it should be gaining it.

What Real Measurement Looks Like

The shift that needs to happen is from reporting activity to tracking outcomes. That means measuring not just what learners did inside a platform but what changed as a result.

This requires a different data infrastructure that connects learning records to skills, performance, and operations, so the line between training and capability gain is clear. It also means tracking skills over time, not just at course completion, and surfacing data to those making workforce decisions.

Brandon Hall Group research on skills-based strategies finds that high-performing organizations measure outcomes at multiple levels, link those outcomes to skill targets, and use the data to adjust what they offer and how they deliver learning. They measure, rather than hope.

That distinction matters more than it sounds. A measurement system tells you when something is working, when it is not and what to do next. A compliance report just tells you what happened.

The Data Organizations Are Missing

Three categories of measurement are consistently underutilized in corporate L&D.

• Skills progression data. Most organizations can tell you a learner completed a course on communication skills. Far fewer can tell you whether that learner’s demonstrated communication capability improved as a result, or how that individual’s skill profile compares to what their role actually requires. Brandon Hall Group research found that leading organizations are building this capability: 67% conduct skills assessments and competency evaluations, and 58% use pre- and post-program assessments to measure learning impact. Closing that gap requires structured skills assessments tied to learning pathways, not just completion certificates.
• Time-to-competency data. How long does it take a new hire to reach full productivity in a given role? Which learning paths accelerate that timeline and which do not? Brandon Hall Group research found that organizations with mature skills strategies have faster onboarding and time to contribution, but most organizations cannot measure this connection due to a lack of integrated data systems.
• Business impact correlation. When sales performance improves after a product training rollout, can you demonstrate the connection? When customer satisfaction scores rise following a service skills initiative, is there evidence of attribution? Brandon Hall Group research found that among award-winning organizations, 53% track performance metrics and business impact, and the most sophisticated are conducting long-term follow-up studies to measure whether behavior change actually sticks. Building that infrastructure is how L&D makes the business case clearly and keeps it.

Why the Platform Architecture Matters

Getting to this kind of measurement is not just a matter of wanting it. It requires a platform architecture designed to capture, surface and connect the right data from the beginning.

This is a structural argument for Canvas Career by Instructure, a skills-first learning platform, built on the pedagogical foundation Canvas established in higher education. Unlike retrofitted legacy LMS platforms, Canvas Career organizes learning around skills rather than courses or content. Skills data is native, not just an add-on.

The practical difference is in measurement. Canvas Career tracks progress against a skills taxonomy, not just a completion checkbox. Managers see the distribution of skills for their team, not just module completions. L&D leaders get data on capability gains relative to role requirements, not training hours.

Instructure is a Brandon Hall Group Eminence Partner, and this measurement architecture is a significant part of why that relationship exists. The ability to tie learning activity to skills outcomes and surface that data in useful formats is exactly what organizations need to shift from compliance reporting to genuine accountability.

Changing the Conversation

The measurement problem in L&D is ultimately a credibility problem. When business leaders ask what the learning function is delivering and the answer is completion rates, the conversation tends to end there. When the answer is skills-progression data tied to workforce capability gaps, the conversation goes in a different direction.

Brandon Hall Group research shows that organizations with mature measurement practices are more likely to receive executive support for learning investments, be involved in strategic workforce planning and demonstrate measurable business outcomes from their programs. Measurement is not a reporting exercise. It is how L&D earns a seat at the table and keeps it.

The dashboards organizations have today are not useless. They are just incomplete. They tell part of the story while leaving out the part that matters most. The organizations pulling ahead are the ones building measurement systems that close that gap, connecting what learners do to what capabilities they build and connecting those capabilities to what the business actually needs.

This requires deliberate decisions about what to measure, how to structure the data and how to make that data visible to the people who need it. It also requires a platform that treats skills as the unit of accountability rather than an afterthought layered onto completion records. Canvas Career was built around exactly that principle. Because skills are the organizing unit of the platform rather than a reporting layer added on top, the measurement Instructure enables is structural, not cosmetic. Managers see capability data. L&D leaders see skills progression against defined targets. Business stakeholders see workforce readiness tied to actual role requirements. And finally, it requires L&D leaders willing to hold their function to a higher standard of evidence than the field has historically demanded of itself.

The organizations that make that investment will find something unexpected on the other side: not just better reporting, but better learning. When you measure what actually matters, you design differently. You prioritize differently. You can see what is working and stop doing what is not. Instructure built Canvas Career to support exactly that cycle, where measurement informs design, design improves outcomes and outcomes justify continued investment. The measurement system does not just prove impact. It creates the conditions for more of it.

That is what it looks like when an organization stops counting completions and starts measuring what training actually builds. And that is the conversation worth having.

The post See What Training Builds: How Real-Time Skills Data Is Changing the Conversation appeared first on Brandon Hall Group.

When organizations evaluate learning management systems, they often begin by asking about cost, integrations and reporting. While these are valid considerations, they should not be the entire focus.

The first question should be: What is this platform designed to achieve?

The distinction between compliance-focused and skills-focused systems has a greater impact on L&D outcomes than any feature list. However, many organizations overlook this question until they are committed to a contract.

Brandon Hall Group research makes the case plainly: Effective skill development programs operate as integrated learning ecosystems, not isolated training events. The organizations producing the best results don’t just use technology to track completions. They use it to build capability and then prove it.

This approach often requires a fundamentally different platform architecture.

As workforce skill requirements accelerate and employees increasingly evaluate employers based on development opportunities, the cost of the wrong platform choice compounds year over year. Organizations locked into compliance-first systems don’t just miss a feature. They may miss the compounding returns that come from years of data on skill progression, personalized learning paths and demonstrated capability growth.

Two Different Design Philosophies

For years, the corporate LMS market has been dominated by systems designed to ensure employees complete required training and that this can be documented. These systems perform that function effectively, offering compliance dashboards, auto-enrollment, completion certificates and audit-ready records. Their design prioritizes administrative needs.

However, most organizations now seek to demonstrate that learning leads to measurable changes in employee performance, skill levels and business results. This objective requires a different type of platform.

Skills-based LMS platforms are designed to develop capabilities rather than simply log activities. They prioritize the learner, organize content around skills and measure progress by demonstrated proficiency. These platforms aim to make learning engaging and meaningful, rather than a compliance obligation.

This distinction significantly influences what the platform tracks, how it presents content, what it highlights for learners and how it reports outcomes to the organization.

What the Data Shows

Brandon Hall Group research indicates that 84 percent of award-winning competency development programs use blended learning paths with multiple delivery methods. Virtual instructor-led training is present in 79 percent of programs, self-paced e-learning in 75 percent and microlearning modules in 55 percent. These elements are central to effective program design.

Assessment data further supports this approach. Skills assessment and competency evaluation are included in 67 percent of award-winning programs, pre- and post-program assessments in 58 percent and certification programs in 39 percent. The goal is to determine whether capability has actually developed, not merely to generate documentation.

Measurement practices are also evolving. Progress and completion tracking is nearly universal at 97 percent, serving as a baseline. Business performance metrics are used in 53 percent of programs, while skills application data, proficiency tracking, ROI measurement and behavioral observation each appear in 42 percent.

These advanced measurement practices set leading organizations apart. Those that link learning to demonstrated skill changes and business outcomes are more likely to receive recognition and achieve advantages in retention, performance and competitiveness.

A compliance-oriented LMS tracks completion, while a skills-oriented platform identifies who improved and whether that improvement led to measurable results.

What makes this data particularly significant is what it says about learner motivation. The award-winning programs that measure skill application and behavioral change are not just better at reporting. They are built around an experience learners want to return to. When a platform shows someone their own capability growth in concrete terms, it creates a reason to keep learning. Completion rates are a byproduct of that engagement, not a goal in themselves.

Canvas Career: Built for This Problem

Instructure, a Brandon Hall Group Eminence Partner, developed the Canvas Career platform around a specific design philosophy: The learner comes first, skills are the organizing principle and demonstrated outcomes are the measure of success. Where many legacy corporate LMSs treat the administrator as the primary user and completion as the primary metric, Canvas Career was built from the learner outward, drawing on the long Canvas track record in higher education and grounding feature decisions on whether it advances actual capability development.

The platform structures learning around skills rather than solely content. Its content-led skills taxonomy automatically extracts skills from each course and page, providing organizations with a real-time view of skill development. Assignments can be mapped to Bloom’s taxonomy levels, ensuring skill recognition is based on demonstrated performance.

Canvas Career provides learners with a mobile-first experience focused on career advancement. The Skill Space feature consolidates earned and demonstrated skills across programs, offering learners a clear overview of their capabilities. The resume-upload function extracts skills and personalizes recommendations, encouraging ongoing development rather than simple completion.

The platform also supports just-in-time, microlearning experiences validated as critical by Brandon Hall Group data. Collections enable concise learning delivery, while AI-powered recommendations suggest next steps. Study tools such as AI summaries, flashcards and quizzes facilitate self-directed development. The overall design positions learning as a career investment.

For organizations, Canvas Career delivers analytics that extend beyond enrollment and completion. Skill reporting provides L&D and business leaders with actionable data on learning’s value. The platform’s open LTI and API architecture enables practical integration with HRIS, CRM and talent management systems.

That last point matters more than it might appear. Learning data that flows into talent and performance systems gives HR and business leaders a shared language for workforce conversations. Instead of L&D reporting completion numbers in isolation, the organization can track how skill development connects to hiring decisions, internal mobility and individual development plans. That integration is increasingly what separates learning functions with a seat at the table from those defending their budget.

The Integration Question

A common concern when considering a skills-based platform is its impact on organizations already using existing compliance training systems. Replacing these systems entirely is often impractical.

Canvas Career addresses this reality by offering an open API and LTI ecosystem, allowing it to operate alongside existing infrastructure. The platform serves as a complementary skills development layer, extending organizational capability without necessitating a complete technology overhaul.

However, Brandon Hall Group data highlights the limitations of a compliance-only approach. With 53 percent of award-winning programs measuring business performance impact and 42 percent tracking behavioral change, organizations relying solely on compliance platforms miss critical evidence of effectiveness. They can document training completion but cannot demonstrate its impact.

The Question Underneath the Question

The issue extends beyond platform selection. Choosing a compliance-based LMS is not only a technology decision but also a statement about learning expectations. It reflects a preference for coverage over development and values completion rates over capability gains.

This model was appropriate when L&D’s primary role was legal defensibility. Today, with rapid skill changes, increasing employee expectations for meaningful development and greater demand for evidence of learning’s impact on performance, it is less effective.

Brandon Hall Group data from award-winning organizations demonstrates that those achieving measurable results design learning around skill development from the outset, aligning platforms, processes and measurement systems accordingly. Many Brandon Hall Group award-winning programs share a stronger emphasis on skills development than compliance alone.

Start With the Right Design Principle

No platform is without limitations. Canvas Career is relatively new to the corporate market and Instructure acknowledges areas for future development, including enhanced compliance reporting, expanded analytics and social learning features. These are transparent acknowledgments rather than shortcomings to conceal.

For long-term investment, design philosophy is critical. A platform focused on skills, learner experience and measurable outcomes will evolve to meet future needs, while a compliance-oriented platform will remain limited in scope.

Organizations that make the right architectural decision will build on a foundation that supports the future direction of L&D. Those that do not will spend years attempting to retrofit skills-based outcomes onto systems not designed for that purpose.

That timeline is shorter than most organizations expect. Skills management is evolving rapidly and the vendors, tools and expectations shaping it will look different within two or three years. Organizations that start now with the right design philosophy will have real data, real skill profiles and real evidence of impact to build on. Those starting later will be catching up from behind, on a platform that may not have been designed to help them get there.

The key consideration is not only the platform’s current capabilities, but also its intended future development.

The post Skills-Based or Compliance-Based? The LMS Decision That Shapes Everything Else appeared first on Brandon Hall Group.

For years, the default strategy in enterprise learning was to stack solutions. Compliance training ran in one system. Partner and distributor training ran in another. Workforce skill-building ran in a third. Each tool had a point of contact, a support contract, a reporting dashboard and an integration that needed babysitting.

That approach worked, more or less, when enterprise L&D was simpler. But that’s no longer the case.

The pressure to train employees, partners, customers, distributors and resellers under one coherent strategy, while keeping compliance documentation airtight and workforce development moving, has turned what was once a feature conversation into an architectural one. L&D leaders increasingly find themselves asking whether one platform can support fundamentally different learner populations, training purposes and administrative structures without fracturing into the same mess they already have.

That is a harder question to answer. But it is the right one.

The Compliance Problem Is Not Going Away

Compliance training has always been the non-negotiable layer of enterprise learning. Regulatory requirements, safety certifications, data privacy obligations, cybersecurity awareness: None of these are optional and the documentation requirements that accompany them are substantial. When something goes wrong and an audit follows, organizations need records that are complete, accurate and traceable.

The challenge is that compliance training often ends up in a system that was optimized for recordkeeping, not learning. Employees click through content they have seen before. Completion rates look fine. Retention and behavior change are another matter.

Brandon Hall Group research on the LMS market found that 46% of buyers rated “demonstrates how the LMS meets our use cases” as highly important and 42% rated “operates as a true business partner” at the same level. Those two priorities are connected. Buyers want a system they can actually use and they want a vendor who understands their context well enough to help them use it well. That expectation shows up most clearly in compliance, where the cost of failure is real. Organizations want a system that can handle the administrative rigor compliance requires while still supporting content that people actually engage with.

For example, Scheer IMC’s Cyber Crime Time program offers an instructive example of what happens when you apply that thinking to cybersecurity awareness. Rather than treating compliance as a box-ticking exercise, the program drops employees into the role of a hacker in a 3D serious game, putting them on the attacker’s side of a phishing or social-engineering scenario before asking them to defend against it. Verbund AG’s Information Security Specialist described the effect as making learning unforgettable and taking understanding to a new level. That is what compliance training looks like when it is built to change behavior rather than generate completion records.

Extended Enterprise Is Where Complexity Multiplies

Internal workforce training is complicated. Extended enterprise training, reaching partners, distributors, franchisees, customers and resellers with tailored learning experiences, is a different level of challenge entirely.

The learner populations are structurally different. A partner organization has its own brand, its own technical environment, its own onboarding timeline and its own reason for caring (or not caring) about the training you are trying to deliver. A distributor in Germany has different regulatory context than one in Singapore. A reseller network may need product certification training that looks nothing like what your internal sales team receives.

Historically, organizations have handled this by standing up separate systems for each external audience, or by dumping external learners into the same environment as internal employees and hoping the experience felt relevant. Neither works at scale. Separate systems multiply administrative overhead and create reporting blind spots. Shared environments without proper segmentation mean external learners see content that has nothing to do with them and lose confidence in the platform fast.

Multi-tenant architecture solves this at the structural level. The imc Learning Suite from Scheer IMC, a Brandon Hall Group Eminence Partner, is built around exactly this model. It’s a single platform instance that can serve employees, partners, distributors, customers and resellers, each in a configurable environment that reflects their brand, their learning paths and their specific training requirements, without duplicating the underlying infrastructure or fragmenting the administrative view. A partner’s employees can log into a portal that looks like their employer’s platform, access only the content relevant to their certification or role and have their progress tracked in a way that feeds reporting back to the organization that owns the learning strategy.

For extended enterprise deployments, the partnership dimension is a hard requirement. The organizations rolling out partner training across dozens of external entities need a vendor with real implementation experience and clear escalation paths, not just a feature checklist.

Workforce Development Cannot Be an Afterthought

Ongoing workforce learning and skill development tends to be where the real cultural work happens. Compliance says, “here is what you must know.” Extended enterprise says, “here is what our partners must be able to do.” Workforce development says, “here is how we grow our people over time.”

Brandon Hall Group research on collaborative learning finds that 89 percent of organizations expected cohort-based learning to remain the same or increase in use, a figure that reflects how organizations think about workforce development now. The focus has shifted to building the conditions for people to learn from each other, apply skills in real contexts and carry that learning back into their day-to-day work.

The same research noted that 83 percent of organizations plan moderate or heavy investment in improving the learner experience. That investment succeeds or fails on instructional design, not technology selection. Organizations that use an LMS with the right collaborative features, such as learner directories, group discussions, cohort-based paths and embedded communication tools, can build that culture without layering in additional platforms, as long as the LMS was built with that use case in mind.

That last phrase matters. Many compliance-first systems were not. They were built to manage records and adding collaborative features on top feels like an afterthought because it was.

The Architecture Argument

This is where the conversation shifts from L&D to IT and finance and where L&D leaders often need a different kind of language.

CIOs and CFOs evaluating an enterprise LMS are not thinking about instructional design philosophy. They are thinking about integration surface area, vendor risk, total cost of ownership and administrative overhead. Stacking three or four point solutions looks cheaper in a line-item budget until someone accounts for the integrations that need maintenance, the data that does not flow cleanly between systems, the reporting that requires manual reconciliation across platforms and the administrative staff time that gets consumed keeping everything running.

A consolidated platform with multi-tenant architecture changes that math. One set of integrations with your HRIS, SSO provider and content libraries. One administrative interface. One solution provider relationship to manage. One reporting structure that can surface compliance status, partner certification progress and workforce development metrics in the same place.

Brandon Hall Group research on the LMS market found that 44% of organizations said the rapid evolution of new functionality makes it difficult to stay current and 42% said all platform types are incorporating each other’s capabilities to the point where they all start to blend together. That confusion is expensive. It leads organizations to buy overlapping tools, then struggle to rationalize them later. Starting from architectural clarity, asking what you need to support, who all of your learners are and what your reporting strategy requires, is what saves organizations from that cycle.

Thinking About This Practically

The organizations that get this right tend to start with a question that Brandon Hall Group has consistently recommended as the foundation of any LMS selection process: what does this system need to do for us? Not in the abstract, but specifically: which learner populations, which use cases, which compliance requirements, which integrations, which reporting obligations.

When that inventory is complete, the case for a platform that can handle all of it architecturally tends to make itself. The alternative is not simpler. It is just distributed differently, across multiple systems, multiple vendors and multiple administrative burdens that add up faster than anyone budgeted for.

Scheer IMC built the imc Learning Suite to address this problem at the enterprise level. With more than 25 years of corporate learning experience and clients that include Mercedes, Lufthansa, Vodafone and the European Commission, Scheer IMC has the implementation depth that organizations running complex, multi-audience learning environments require. For organizations managing compliance mandates, partner networks and workforce development simultaneously, that combination of platform architecture and proven expertise is the baseline for making the whole thing work.

The post When One LMS Has to Do Everything: Managing Compliance, Extended Enterprise and Workforce Learning at Scale appeared first on Brandon Hall Group.

The rebranding of Courseau to CreateUpon is more than a name change. It reflects a larger transformation taking place across the learning technology market.

Following LearnUpon’s acquisition of Courseau in late 2025, the company has officially repositioned the platform as CreateUpon. The move highlights LearnUpon’s continued investment in AI-powered learning creation and its focus on helping organizations accelerate how they build and deliver training.

For enterprise learning leaders, the timing is important.

Organizations today face mounting pressure to upskill employees faster, support continuous learning, and keep pace with constant business change. At the same time, learning teams are being asked to do more with fewer resources. One of the biggest challenges remains turning internal expertise into effective learning content quickly enough to meet business demands.

CreateUpon is designed to address that challenge directly.

The platform allows users to transform existing materials such as webinars, notes, PDFs, and links into structured course drafts within minutes. The platform also emphasizes “strict source anchoring,” enabling organizations to ground generated content in approved source materials to reduce inaccuracies and maintain the integrity of proprietary knowledge.

That capability matters because many organizations are still struggling with what Brandon Hall Group research consistently identifies as a major learning bottleneck: the gap between subject matter expertise and scalable content creation.

Too often, valuable organizational knowledge remains trapped in documents, meetings, presentations, or inside the minds of busy subject matter experts. Traditional course development processes can take weeks or months, creating delays that make learning less responsive to business needs.

CreateUpon represents a growing category of platforms attempting to solve that issue through AI-native content creation.

What stands out is not simply the use of generative AI, but the effort to combine speed with instructional structure and governance. The platform’s emphasis on learning science and instructional design frameworks suggests a recognition that effective learning requires more than automated content generation alone.

As organizations adopt AI across learning ecosystems, the most successful solutions will be those that help companies scale expertise while still maintaining quality, relevance, and learner engagement.

“Organizations are no longer asking whether AI belongs in learning creation workflows,” said Michael Rochelle, Chief Strategy Officer and Principal Analyst at Brandon Hall Group. “The real question is which platforms can help learning teams move faster without sacrificing instructional quality, governance, or the learner experience. CreateUpon reflects how quickly the market is evolving toward AI-native content development.”

The broader market trend is becoming increasingly clear.

Learning platforms are evolving beyond traditional LMS functionality and moving toward integrated ecosystems that combine content creation, personalization, analytics, and automation into a more unified learning experience.

Organizations are no longer evaluating learning systems solely on administration and compliance management. Increasingly, they are looking at how quickly a platform can help transform institutional knowledge into measurable workforce capability.

In that context, CreateUpon represents more than a rebrand. It reflects the accelerating shift toward AI-native learning creation as a core enterprise capability.

It is also worth noting that LearnUpon is a Brandon Hall Group Smartchoice® Preferred Provider, recognized for its commitment to innovation and impact in the learning technology market.

For learning leaders, the implications are significant.

The organizations that succeed in the next generation of workplace learning will be the ones that can capture expertise quickly, create meaningful learning experiences efficiently, and continuously adapt training to meet changing business needs.

CreateUpon appears well-positioned to support that shift.

The post CreateUpon Signals a Shift in How Organizations Build Learning at Scale appeared first on Brandon Hall Group.

For all the thinking, strategizing and reimagining that happens across corporate learning and development, not much has really changed. Organizations still spend billions annually on training employees complete, forget and rarely apply. Completion rates move up the chain. Dashboards get updated. But behavior in the workplace often stays the same.

That gap matters more than ever. When budgets tighten, L&D investments are among the first to face scrutiny because leadership cannot always connect training activity to measurable performance outcomes. The market is finally forcing a reckoning.

A recent conversation with Steve Dineen, Founder and President of Fuse Universal, showed how deliberately the company is returning to a core idea it has pursued since its inception: Learning should help people perform better in the real world. The discussion centered on Fuse’s platform repositioning, the launch of Fuse 4.2 and Lyra, its new AI coaching solution. Over my years as an industry analyst, I have heard plenty of vendor pitches about “learning in the flow of work.” It has often felt like a pipe dream, especially for frontline workers. What I saw from Fuse gave me reason to pay closer attention.

The Market Reality: Too Much Learning Technology, Not Enough Performance Support

Brandon Hall Group research consistently surfaces technology rationalization as a top priority for HR and L&D leaders. The average enterprise learning tech stack has grown unwieldy: compliance platforms, LXPs, content libraries, coaching tools, performance systems and knowledge repositories all competing for attention. Each has its own login, data model and logic.

This creates a familiar problem. Employees do not care how the technology stack is organized. They want quick answers, practical guidance and support that helps them do the job in front of them. Leaders want to know whether the investment is improving capability, productivity and performance.

The competitive landscape reflects this challenge:

• Cornerstone OnDemand offers comprehensive talent management and compliance capabilities, but complex implementations and suite-level administration can make it harder to deliver simple, immediate performance support.
• SAP SuccessFactors Learning remains deeply embedded in many large enterprises, particularly global organizations already standardized on SAP infrastructure, but companies often look for more modern user experiences and stronger frontline enablement capabilities.
• Axonify has built a strong reputation around frontline learning and microlearning reinforcement, particularly in retail and operational environments, though its focus remains narrower than Fuse’s broader LMS and AI-performance-support vision.
• Sana Labs is gaining attention for its AI-first learning approach and strong positioning inside Workday-oriented environments, but many organizations are still evaluating how these newer AI-native platforms support long-term operational learning and frontline execution at scale.

What none of these approaches has fully cracked is the performance-support gap — the space between “employee completed the training” and “employee can actually do the job.”

What Makes Fuse Different

Fuse has been serving enterprises since its founding in the UK and now counts more than 150 organizations worldwide among its clients, including Hilti, Vodafone, Panasonic, Scandic and Avon. The company operates at the intersection of LMS, LXP and performance support. With Fuse 4.2 and Lyra, the company is making an explicit play to replace the traditional learning management system with something more ambitious.

The architecture behind that strategy is what Fuse calls the LEAP Framework: Learning, Exercise, Application and Performance. It is a useful lens for understanding how the platform approaches the full capability-building journey rather than stopping at content completion.

Four innovations stand out:

Adaptive learning and gap-filling identify what someone does not know and target those gaps with personalized instruction. Lyra uses an adaptive tutor approach that can deliver text, video, visuals and other formats based on need. This is especially relevant for certification, compliance and foundational knowledge.

Safe practice and simulation move employees from understanding to application. Role plays and simulations allow people to practice skills safely, receive feedback and build confidence before real-world execution.

An AI “credible colleague” provides in-the-flow support. Instead of searching for content, employees can ask questions, get advice and solve problems through a knowledgeable assistant available at the point of need.

Performance coaching supports ongoing improvement and behavior change. By analyzing conversations, tasks or other performance signals, Lyra can coach employees over time, functioning more like a virtual manager or development guide than a traditional learning tool.

Who Benefits Most

Mid-market enterprises with 2,000 to 20,000 employees may find Fuse especially compelling. These organizations often outgrow legacy LMS platforms but do not want enterprise systems that are over-engineered for their needs. Fuse offers platform consolidation without sacrificing capability.

Organizations with significant frontline or deskless workforces should pay close attention. Retail, hospitality, manufacturing, logistics and field services all face the challenge of reaching employees who are not sitting at a desk. Fuse’s mobile-first design and in-the-flow knowledge access were clearly built with this population in mind.

Sales-driven organizations focused on revenue performance have one of the clearest use cases. When learning connects directly to sales readiness, practice quality and coaching effectiveness, the ROI story becomes much easier to measure. Fuse can help reduce time-to-competency for new sellers while continuously upskilling existing ones.

Organizations running technology rationalization initiatives may also see value. Fuse’s combined LMS/LXP functionality and embedded performance support can reduce the need for a multi-vendor learning stack while simplifying administration and connecting learning data across the employee experience.

Compliance-heavy industries should not overlook the platform. Financial services, healthcare and manufacturing all have non-negotiable regulatory training requirements. Fuse handles compliance tracking and certification without making compliance the only thing the platform does well. That creates an opportunity to turn mandatory training into an engaging, searchable resource rather than a checkbox exercise.

What This Means for the Market

Fuse occupies an interesting position in the learning technology market. It is not the largest player, but it may be one of the more willing to make strong product bets on AI and genuine workflow integration.

That matters because the next phase of this market will not be won by providers that simply add AI features to an existing architecture. It will be won by companies that rethink what a learning platform is supposed to do. Fuse’s bet is that the platform should start with job performance and work backward to learning, not the other way around.

The LEAP Framework is more than a marketing concept. It reflects a product philosophy centered on capability building over course completion. That is a meaningful distinction in a market still too often organized around content access, administration and completion reporting.

The practical question for L&D leaders evaluating Fuse is whether their organizations are ready to operationalize performance-linked learning. The platform’s full value proposition requires connecting learning activity to performance data and that challenge is organizational as much as it is technical. Fuse can help get organizations there, but they have to want to go.

For organizations serious about moving from activity metrics to performance outcomes, Fuse 4.2 and Lyra represent one of the more credible attempts I have seen to close that gap. That is not nothing in a market full of noise.

Brandon Hall Group helps organizations navigate the rapidly evolving learning technology landscape. The Brandon Hall Group Institute provides corporate HR and L&D professionals with research, frameworks and peer insights to make better technology decisions.

For solution providers, our Preferred Provider Program, TotalTech advisory services and Marketing Services help position innovations effectively in this competitive market.

David Wentworth, Managing Director, Learning and Talent, Brandon Hall Group, contributed to this blog.

The post Fuse Universal Focuses on the Future</br> by Embracing Its Roots appeared first on Brandon Hall Group.

The turf war in corporate learning can be real. It happens for a variety of reasons from lack of awareness to gaps in support to outright operational rebellion. Whatever the reason, learning teams need to come to the place where distributing “control” is strategy and not chaos. 

The team at Docebo has articulated something that our research at Brandon Hall Group validates with data: the organizations achieving universal success with their AI initiatives are those that have solved the tension between centralized governance and distributed autonomy. Docebo’s federated learning model doesn’t just describe a better organizational structure — it maps directly to what we observe in organizations reaching Phase 3 strategic progression, where capability building becomes infrastructure rather than programming.  

The Data Behind Distributed Success 

Our AI Progression Model research reveals a pattern that should matter to every learning leader: organizations achieving strategic alignment at Phase 3 and above (54%) report universal success with their AI initiatives. Not improved success rates. Not better outcomes. Universal success. 

That finding led us to ask: what creates the conditions for universal success? The answer centers on how organizations treat capability development. Those stuck in Phases 1 and 2 approach learning as reactive support and something that responds to business needs. Phase 3 organizations flip this relationship. They build capability as strategic infrastructure that enables business objectives. 

This is where Docebo’s federated model demonstrates its real value. The hub-and-spoke architecture they describe isn’t just an organizational chart exercise. It’s a recognition that capability building requires both centralized coherence and distributed responsiveness — and that tension between the two creates the conditions for breakthrough rather than compromise. 

Why Centralization Fails and Decentralization Fragments 

Docebo traces the familiar pendulum swing that learning organizations have experienced for decades. Centralized models promise alignment but deliver bottlenecks. Decentralized approaches enable speed but create fragmentation. The standard response has been to split the difference with federated structures that satisfy neither requirement particularly well. 

What makes their current argument different is the forcing function of AI itself. As they point out, AI acts as both centralizing and decentralizing pressure simultaneously. Organizations need enterprise-wide governance to manage systemic risks, ethical frameworks, and strategic coherence. At the same time, AI enables functional teams to build and deploy solutions at speeds that centralized models can’t match. 

Our research data supports this dual reality. Organizations in Phase 3 implement comprehensive AI governance frameworks with clearly defined roles and responsibilities. But they also achieve data-driven decision making at all organizational levels. The critical insight: these aren’t competing priorities. They’re complementary capabilities that reinforce each other when properly structured. 

The Hub-and-Spoke Charter as Capability Constitution 

The strength of Docebo’s approach lies in how they’ve thought through decision rights and resource allocation. Their charter model creates what they call a “constitution” for the performance operating model — a clear social contract between the center and the functions. 

This maps to a pattern we observe in Phase 3 organizations: they achieve strategic alignment by making explicit what most organizations leave implicit. Who owns what. Who decides what. How resources flow. How conflicts resolve. 

Docebo’s framework splits ownership with precision: 

The hub owns the infrastructure. The core technology platform, the governance frameworks, the universal user experience. This centralization prevents the fragmentation that kills enterprise-wide initiatives. It ensures stability, security, and the economies of scale that come from world-class shared systems. 

The spokes own the domain expertise. The specific knowledge, the business-driven priorities, the contained experimentation. This distribution enables the speed and relevance that centralized models sacrifice. 

The brilliance here is recognizing that both sides need real ownership, not just assigned responsibilities. The hub can’t be a service provider that functions dictate to. The spokes can’t be consumers who passively receive what the center builds. Both must invest, both must deliver, both must own outcomes. 

Making Autonomy Real Through Resource Commitment 

Most federated models fail at the resource question. They assign distributed responsibilities without providing distributed authority or funding. Docebo’s framework addresses this head-on with three pillars of empowerment that deserve attention. 

Their co-investment model creates shared ownership through mutual financial commitment. Functions collectively fund the shared infrastructure, transforming them from passive consumers into active investors. The center then co-invests back into functional priorities, with the CLO acting as internal venture capitalist for high-potential initiatives. 

This funding flow matters because it solves the accountability problem that plagues most matrix organizations. When the center provides services that functions consume, functions can always claim the center didn’t deliver. When functions invest in shared infrastructure, they own its success. When the center invests in functional priorities, it validates their strategic importance. 

The support model prevents isolated struggle. Performance Architects from the central hub serve as internal consultants to the spokes, providing expertise in learning design, analytics, and change management. This ensures that distributed authority doesn’t mean distributed struggle. 

The incentive alignment makes capability building a leadership responsibility rather than an add-on activity. When building team capability becomes a formal component of leadership scorecards, the time-for-learning paradox that Docebo identifies starts to dissolve. Leaders create space for development when their success depends on it. 

The CLO as Ecosystem Orchestrator 

Docebo positions the Chief Learning Officer role at the center of this transformation, and our research on Phase 3 organizations validates why. In organizations achieving strategic alignment, learning leadership has a seat at the leadership table. Decision making becomes data-driven as standard practice. The learning function shifts from service provider to strategic partner. 

The ecosystem orchestrator role that Docebo describes takes this evolution further. The CLO becomes portfolio strategist for capability, making investment decisions about which functions to onboard and when. They manage the efficiency dividend as internal venture capitalist, seeding experiments and ensuring continuous improvement. They arbitrate conflicts between hub and spokes. They develop the talent pipeline for Performance Architects and spoke leaders. 

This is leadership at the system level rather than the function level. It requires seeing the entire federated network as the product rather than individual programs or platforms. 

Our data shows why this matters: HCM Excellence Award winning organizations leverage learning leadership to provide crucial governance and quality assurance support and alignment. Including 87% of winners directing QA processes, content validation procedures and stakeholder alignment mechanisms as well as 73% providing continuous improvement protocols (Source: 2025 Best Learning Strategy HCM Excellence Award winners). 

Beyond Learning and Development 

Here’s where Docebo’s framing helps us see the bigger pattern: this isn’t just about learning and development. Their argument is that AI transformation requires complete rearchitecting of workforce capability systems. That capability building must embed in business operations rather than remain an isolated function. 

Our research on organizational progression bears this out. The transition to Phase 3 requires scaling pilot projects across multiple functions and demonstrating that AI applications deliver consistent value at organizational scale. Analytics capabilities must advance from basic reporting to predictive insights and strategic decision support. Organizations must demonstrate strategic alignment between AI initiatives and business objectives. 

None of that happens if capability building stays in the learning silo. It requires the distributed-yet-aligned model that federated autonomy enables. 

The hub-and-spoke architecture provides the structure for this broader transformation. The central hub ensures coherence, governance, and scale. The functional spokes ensure relevance, speed, and business integration. The charter defines how they work together. The resource model ensures both sides can execute. The incentive structure ensures leadership attention. 

The Implementation Path 

Docebo’s next steps framework provides a practical bridge from concept to execution. They recommend starting with a single pilot spoke to establish the model before scaling — a pattern our Phase 3 transition research strongly supports. 

The sequence matters: codify the strategic mandate first, working with the pilot function to define specific business outcomes and required capabilities. Launch the pilot spoke by empowering a capability owner and co-creating their charter of autonomy. Deploy Performance Architects to provide expert support. Provide core infrastructure access. Capture the playbook for scaling. 

This disciplined approach prevents the chaos that occurs when organizations try to transform everything simultaneously. It creates proof points that build confidence. It develops the talent and systems needed for broader rollout. 

Organizations making the Phase 2 to Phase 3 transition typically take 12-24 months. That timeline aligns with building a federated model — fast enough to maintain momentum, slow enough to build real capability. 

The Strategic Imperative 

The broader pattern here extends beyond learning and development. Organizations are discovering that capability building can’t remain reactive support. It must become strategic infrastructure that enables business execution. 

The federated model that Docebo articulates—with clear decision rights, mutual investment, aligned incentives, and ecosystem orchestration — provides a framework for making that shift. Not as organizational theory, but as operational architecture. 

Our research shows that organizations reaching Phase 3 strategic alignment achieve universal success with their AI initiatives. They build comprehensive governance while enabling distributed decision making. They align capability development with business objectives. They transform the learning function from service provider to strategic partner. 

That transformation doesn’t happen by accident. It requires the kind of intentional architecture that Docebo’s hub-and-spoke model provides. It requires treating capability building as infrastructure rather than programming. It requires distributing control while maintaining coherence. 

The organizations that figure this out don’t just improve their learning effectiveness. They create the conditions for universal success with strategic initiatives. That’s worth building toward. 

The post Distributed Control as Strategic Infrastructure:</br> Why Federated Learning Autonomy Drives Universal Success appeared first on Brandon Hall Group.