Automated reporting tools have shortened close cycles and reduced visible errors across many finance functions. Exception alerts surface quickly; reconciliations run with minimal manual input, and dashboards present a steady picture of compliance. From an operational standpoint, the environment appears to be controlled.

What has shifted more quietly is accountability. Fewer people process transactions; more people interpret system outputs and regulatory changes. The work has moved upstream, toward judgment and oversight, even if the systems appear stable.

Capability models, however, often remain anchored in older role definitions. As automation reshapes work, enterprise custom eLearning frameworks have to account for how compliance responsibilities now sit with fewer individuals who carry broader oversight expectations.

The system may be stable. That does not automatically mean the capability behind it is.

How AI and RPA Are Changing Compliance Workflows Inside Finance

AI and RPA are now embedded in core finance operations, particularly in auditing and reporting environments. Transactions are screened continuously; reconciliations run in the background, and irregular entries are flagged before formal review cycles begin. What once required layered manual checks is now compressed into system-driven monitoring.

In practical terms, this has altered daily expectations:

• Automated anomaly alerts are replacing extended manual review cycles.
• Reporting systems update faster than internal review habits have adapted.
• Transaction processing has reduced, while interpretation and sign-off responsibility have increased.

The visible outcome is speed and consistency. Quarterly reporting stabilizes. Error rates decline. Yet the compression of execution time leaves less space for reflection, discussion, and informal knowledge transfer.

When the work changes in this way, the definition of being “skilled” in compliance with work changes as well. If enterprise custom eLearning programs continue to mirror older task structures, they risk reinforcing competencies that automation has already displaced.

Why Finance Roles Now Require Different Skills Than Before

As automated systems absorb routine processing, finance roles are becoming less transactional and more interpretive. The daily workload no longer centers on compiling reports from multiple data sources. It increasingly revolves around assessing whether automated outputs are aligned with policy, regulation, and internal control standards. That shift changes the skill profile of the team in practical ways.

The work now includes responsibilities such as:

• Evaluating system outputs rather than generating reports manually
• Interpreting regulatory updates that may apply differently across regions
• Questioning whether automated classifications still reflect current guidance
• Sitting in cross-functional review calls where interpretations are discussed in real time
• Preparing responses when auditors question how automated decisions were applied
• Clarifying assumptions behind numbers that leadership now sees almost instantly

In one multi-entity structure, reporting cycles became faster after automation was implemented, yet audit discussions grew longer. The issue was not data completeness. It was interpretation. Teams had to justify why certain entries were treated consistently across markets despite local regulatory nuances.

As organizations operate across more jurisdictions, these interpretive demands multiply. What appears in uniform at the system level often varies at the regulatory level. That growing divergence is where complexity begins to accumulate.

Managing Multi-Region Regulatory Complexity in Automated Environments

Multi-region finance structures rarely operate under a single regulatory lens. US GAAP and IFRS may overlap in principle, yet interpretation and timing of updates do not always move in sync. In shared service models, reporting activities are often centralized while regulatory accountability remains local. Automation brings efficiency to consolidation, but it also assumes a level of uniformity that does not fully exist.

Policy updates in one jurisdiction may be implemented within weeks, while another market reviews implications over a longer cycle. Systems, however, tend to apply standardized rules unless configuration changes are made deliberately. In one regional setup, updated revenue recognition guidance was incorporated into internal documentation quickly, yet automated classifications reflected the prior interpretation until the next reporting configuration cycle. Reports looked stable. The underlying assumption had shifted.

These timing gaps are not dramatic failures. They accumulate quietly. Over time, the distance between evolving regulation and embedded system logic widens. That distance is where knowledge begins to age, and where exposure often sits unnoticed.

The Hidden Risk: Knowledge Ages Faster Than Systems Update

Automation creates an impression of stability. Reports are consistent. Controls appear embedded. Yet regulatory interpretation continues to evolve, sometimes incrementally, sometimes in response to external guidance. Systems do not reinterpret policy on their own. People do. When the learning layer does not move at the same pace, knowledge begins to lag behind operational execution.

This misalignment often appears in small ways:

• Internal policy is updated, but supporting learning materials remain unchanged
• New hires are trained using older examples that no longer reflect current interpretation
• Quarterly refresh cycles overlook regulatory adjustments issued mid-year
• Teams rely on prior case discussions that assumed different reporting treatments

The gap is rarely visible in dashboards. It shows up in review conversations and audit clarifications. Compliance automation therefore becomes a learning issue as much as a system one. Enterprise eLearning structures that operate on annual updates struggle to keep pace.

A more continuous compliance learning model starts to look less optional and more structural, which leads naturally to reconsidering how learning architecture is designed in the first place.

Designing Continuous Compliance Learning as Part of Enterprise Digital Learning

If compliance expectations evolve continuously, the learning model cannot remain annual. In many organizations, training still follows a calendar rhythm rather than a regulatory one. That structure may have worked when reporting cycles were slower, and roles were stable. In automated environments, it leaves gaps.

A more resilient approach treats compliance capability as part of enterprise digital learning rather than a standalone course series. Within a broader digital learning transformation, the focus shifts toward alignment with real work. That often includes:

• Role-mapped learning pathways that reflect current decision responsibilities
• Short refresh modules triggered by policy updates or audit feedback
• Scenario-based practice built around recent regulatory interpretations
• Teams rely on prior case discussions that assumed different reporting treatments

This is not an expansion of content volume. It is a shift in learning transformation strategy, where compliance becomes an operating discipline supported by structured eLearning architecture. Learning content evolves alongside policy changes and workflow adjustments instead of waiting for annual review cycles.

Once compliance learning is positioned this way, the question becomes less about delivering courses and more about selecting the right custom eLearning provider to design capability systems that remain current.

How Upside Learning Supports Organizational Learning Transformation in Finance

Upside Learning works with finance teams in regulated industries. It builds custom eLearning based on how compliance work is actually carried out inside the organization. That includes how reports are reviewed, how interpretations are discussed, and how decisions are recorded during audit cycles. The learning is structured around existing workflows instead of generic course templates.

In finance environments, this often means connecting learning design directly to reporting cycles and review practices instead of separating training from operational reality.

The approach generally includes elements such as:

• Capability maps tied to specific compliance responsibilities
• Learning modules that can be revised when policies change
• Scenario exercises built around recent audit observations
• Review structures that link policy updates with learning updates

Programs are structured around real workflow pressures and regional variation, so learning remains connected to the work being performed. Within broader digital learning transformation efforts, this supports organizational learning transformation by keeping capability aligned with evolving expectations rather than static course calendars.

Compliance Capability Must Evolve with Automation

Automation can stabilize reporting cycles, but stable systems do not guarantee capable teams. When compliance is treated mainly as a technology initiative, attention often centers on controls and configuration while learning evolves more slowly. In regulated environments, that gap creates risk. Regulations shift, roles expand toward interpretation, and accountability becomes more distributed. Governance therefore has to include how knowledge is refreshed and how teams are prepared for decisions; automated tools cannot be resolved.

Upside Learning supports organizations aligning compliance capability with enterprise digital learning priorities. Through custom eLearning grounded in real workflows, finance teams can strengthen consistency as regulations and responsibilities change.

Organizations reassessing their compliance learning architecture, reach out to Upside Learning to build a more durable capability foundation.

FAQs About Embedding Skills Assessment into Performance Workflows

1. What is workflow-embedded skills assessment in enterprise learning?

Workflow-embedded skills assessment measures how employees apply skills during real work tasks instead of only testing knowledge at the end of a course. Traditional LMS-based assessment validates recall or simulated responses. Embedded performance-based assessment captures live signals such as CRM entries, quality metrics, safety adherence, or manager-validated observations. This approach allows enterprise learning teams to measure actual capability rather than course completion.

2. Why do traditional skills assessments fail to reflect job performance?

Traditional skills assessment measures knowledge at a single point in time inside the LMS. It does not track whether skills are consistently applied in real workflows. When learning data is disconnected from operational metrics, organizations measure participation instead of capability.

3. How can performance-based assessment improve ROI in training and development?

Performance-based assessment improves ROI training analysis by linking skill application to measurable business outcomes. Instead of reporting course completions, organizations track changes in metrics such as defect rates, deal conversion, resolution time, or safety incidents. When applied skill signals align with operational improvements, ROI training conversations shift from assumptions to evidence. This strengthens executive confidence in enterprise learning investments.

4. How can an enterprise start embedding skills assessment into workflows?

Start with one high-impact role. Define three to five observable behaviors aligned with skills mapping. Use existing CRM, ERP, or operational data to capture performance signals. Combine system data with manager validation and compare baseline and post-training results over 60 to 90 days.

5. What role does skills mapping play in embedded assessment?

Skills mapping defines the required capabilities for each role. Embedded skills assessment validates whether those capabilities appear in real work. When operational evidence aligns with mapped skills, organizations gain accurate visibility into workforce capability.

6. How does embedded skills assessment support workforce skilling decisions?

Embedded assessment shows which skills improve productivity, quality, and revenue. It helps learning and development focus investment on measurable outcomes and strengthens executive confidence in employee training and development.

Ready to Move from Assessment to Measurable Capability?

Understanding the gap is one thing. Redesigning how capability is measured across systems is another.

If you are evaluating how to evolve your skills assessment approach, start with a structured diagnostic conversation. Identify where assessment currently sits, how performance-based assessment signals are defined, and what data already exists inside your operational platforms.

At Upside Learning, we work with enterprise teams to translate strategic intent into practical implementation plans. That includes pilot scoping, stakeholder alignment, skills mapping refinement, governance structuring, and ROI training measurement design.

If you want a practical roadmap tailored to your skills-based learning environment, let’s begin with a focused discussion around your current architecture and business priorities

The post Upskilling Finance Teams for Automated Compliance Systems first appeared on The Upside Learning Blog.

As 2025 closes, 68% of enterprise learning platforms report active use of AI-led authoring or adaptive design tools, which is a sharp rise from 41% in early 2024, according to the CLO Digital Learning Index 2025. 

Audit coverage, however, has not expanded in parallel; only 17% of large organizations maintain validation protocols for AI-generated logic or content updates. The imbalance reflects where resources are moving. Spending on AI-enabled instructional systems has risen 52% year over year, while investment in human design review has fallen by nearly one-third, reducing the ability to evaluate what these systems now produce. 

The outcome is structural. AI has moved beyond supporting production to defining how learning is organized and paced. What began as efficient automation is now an embedded design layer, where systems shape course logic and assembly-deciding how content connects and evolves.  

The way learning is built is already changing- AI is moving upstream, from creating assets to determining how those assets connect. 

How AI Is Rewriting Learning Design

Through 2024 and most of 2025, AI in enterprise learning focused on surface tasks- content writing, quiz generation, and translation, saving time but leaving the course structure unchanged. 

The shift began when generative systems started interpreting learning data, allowing them to influence the sequence and logic of course design.

Models now predict sequence, pacing, and branching from behavioral traces instead of relying on fixed instructional maps. AI no longer produces material for designers to arrange; it determines the arrangement itself.

One financial firm’s compliance program already rebuilds monthly, using AI to scan new regulations and reconstruct scenario paths automatically, with designers now focusing on validation rather than authorship. Over time, content shifts from written to computed- updated and reordered as data changes. 

When AI defines structure, personalization follows. Sequence adapts in real-time, guided by interaction data, turning the course into a system that reshapes itself with every use. 

From Design Choice to System Function

Once AI begins determining course structure, personalization stops being a separate design decision. It becomes a built-in function of how learning systems operate– driven entirely by the volume and precision of learner data flowing through them. Feedback speed matters. When data returns quickly, systems adjust with some accuracy. Slow loops make the model wander. 

• About 72% of enterprise L&D leaders say they plan to bring in adaptive learning frameworks by 2026 (CLO Survey, Q3, 2025).

• In trials where content shifts within a day of learner input, recall is roughly 35% higher. Engagement holds longer, around 28% on average.

• AI engines now interpret behavioral data- click paths, dwell time, revisit frequency- rather than static assessment scores to define learning routes.

• Learner profiles evolve continuously, updated after each interaction rather than scheduled evaluations.

• Content refresh cycles in mature AI ecosystems run nearly ten times faster than traditional redesign schedules.

Personalization is no longer an instructional design goal. It is an operational behavior of data-driven systems- an automatic consequence of continuous model feedback. 

But once learning depends on constant data interpretation, governance becomes unavoidable. Managing how AI reads, weights, and applies that data defines the next phase of enterprise learning design. 

Data Governance and Model Drift

As AI becomes the foundation of the learning ecosystem, the structure of the L&D function itself must be adjusted. The traditional production model- teams creating, reviewing, and distributing static courses- cannot sustain systems that update and adapt continuously. Learning operations are moving toward integrated design networks where data management, instructional oversight, and model governance function as a single workflow. 

Operational changes observed:

• Traditional content teams are replaced by cross-functional systems combining analytics, governance, and instructional design.

• Roles shift from creation to supervision- designers validate algorithmic decisions instead of authoring every component.

• Learning technologists align with data teams to manage audit cycles, content versioning, and drift control.

• Workflow becomes continuous: AI regenerates and reconfigures content automatically; human oversight verifies alignment.

• Metrics evolve from completion rates to model accuracy, bias detection, and compliance reliability.

Organizational implications:

• L&D transitions from a production function to an operational data system.

• Skill priorities move toward data literacy, metadata management, and ethical AI supervision.

• Oversight roles focus on maintaining alignment between automated design behavior and organizational learning standards.

As this operational realignment stabilizes, AI-driven learning stops being a pilot or trend. It becomes the infrastructure through which corporate training operates heading into 2026. 

The Operational Redefinition of L&D

AI is reshaping how L&D teams actually work. The usual cycle of design, build, launch, and update no longer holds up. Systems keep generating and rearranging content, breaking the old linear flow. 

What’s taking its place looks more like network-smaller groups working faster, with design, data, and review running side by side instead of one after another. 

Inside those teams, roles overlap. Designers spend more time reviewing AI outputs for accuracy and intent, making sure the learning still makes sense, instead of building everything by hand. 

Developers and data specialists handle model behavior, ensuring that algorithms interpret learning logic correctly and stay aligned with compliance requirements. The process remains creative, but it happens through coordination and calibration rather than authorship alone. 

Performance indicators are shifting in the same direction. Instead of tracking course completions or satisfaction scores, teams now measure stability, accuracy, and alignment of outputs to business and learning objectives.

As this rhythm settles in, the distinction between data and design becomes less clear, and AI moves from being a supportive tool to the foundation on which learning operations are built. 

What 2026 Will Look Like for Enterprise Learning

As AI becomes the operational base of enterprise learning, the coming year will focus less on experimentation and more on standardization. The early wave of pilots has matured into permanent infrastructure across most large organizations.

• Adoption Growth Across Enterprise Learning: By mid-2026, roughly seven in ten enterprise learning suites will operate on AI-led design systems. Scenario generation and adaptive sequencing will become standard features within core platforms rather than external tools. The change will register more as infrastructure than innovation.

• Normalization of Adaptive Personalization: Adaptive learning won’t feel new anymore. What once looked like a differentiator becomes routine. Systems will guide learner paths automatically and adjust them as data builds up. Most teams will step back from manual control; design and delivery will run in the same loop, quietly recalibrating in the background.

• Integration of Learning and Enterprise Systems: Connections across LMS, HRIS, and workflow tools will shift from goal to necessity. Shared data pipelines will sustain adaptive accuracy and contextual relevance. Most organizations will prioritize interoperability before adopting new formats or modalities.

• Formalization of Governance Frameworks: Roughly 40% of large L&D operations will establish AI review and validation protocols, drawing from HR or compliance audit models. A few will take longer to adapt, leaving gaps in how accountability is shown across industries.

• Convergence of Roles and Capabilities: Instructional designers, technologists, and analysts will start working inside the same review loops. The lines between their roles will thin out. Knowing how to read and act on data will be part of everyday work, not a niche skill.

By 2026, generative and adaptive AI will no longer sit at the edge of enterprise learning- they will define its structure. The challenge ahead is not growth but stability: keeping systems accurate, sustainable, and aligned with intent. 

For most organizations, that stability will depend on how effectively design, data, and technology come together inside the learning ecosystem. Few teams can manage that convergence alone. It calls for partners who understand both instructional logic and system behavior- how learning experiences are built, measured, and governed in practice. 

This is where Upside Learning becomes relevant. 

Upside Learning and the Future of Learning Design

As learning systems evolve toward AI-led design, the role of instructional partners is shifting from content development to system stewardship. Upside Learning operates within this change, helping organizations translate AI capability into structured, meaningful learning experiences. The goal is not to replace design expertise with automation but to integrate both-building processes where AI supports logic, flow, and scalability while human judgment preserves intent and accuracy. 

Key focus areas: 

• Design integrity: Ensuring that AI-generated learning maintains instructional soundness, narrative clarity, and alignment with defined outcomes.

• Data-informed frameworks: Linking learning analytics with adaptive logic so that design decisions are based on learner behavior and system evidence, not assumption.

• Governance support: Helping organizations establish validation frameworks that track how AI modifies content, structure, and assessment logic over time.

• Workflow calibration: Integrating AI tools into production processes without disrupting quality control or review standards.

• Continuous adaptability: Supporting teams as they evolve design practices to coexist with automated systems that update in real-time.

In a learning landscape defined by automation, Upside Learning’s value lies in maintaining design continuity- keeping learning purposeful, measurable, and grounded in human expertise even as systems become increasingly intelligent. 

This balance between automation and intent leads naturally to the next consideration: how learning will be sustained, not just created, in the years ahead. 

Design Shifts from Creation to Maintenance

The next phase of learning will not depend on how much AI can generate but on how well organizations can maintain what has already been automated. Systems will continue to build, adjust, and optimize, but sustaining relevance will depend on human calibration. Design becomes an act of maintenance- of reviewing, refining, and deciding what endures. The work does not end with creation; it continues in supervision. 

Organizations looking to align their learning strategy with this new reality can explore how custom eLearning solutions from Upside Learning help bridge intelligent automation with human-centered design.

FAQs: Custom vs. Off-the-Shelf eLearning

How do I know if my team needs custom eLearning?

If your workflows, tools, or brand vibe are one-of-a-kind- or if behavior change matters- custom’s your cheat code. 

Isn’t off-the-shelf cheaper?

Yep, upfront it’s lighter on the wallet. But for long-term wins and role-specific skills? Custom flexes harder ROI. 

Can I mix both approaches?

For sure. Start with off-the-shelf for the basics, then sprinkle in custom modules where it really counts. 

How long does custom eLearning take?

Depends on how ambitious you get- usually weeks to months. Planning ahead keeps you from sweating deadlines. 

Are off-the-shelf courses engaging enough?

For general topics, yeah. For real-life scenarios or changing habits? Engagement can ghost you. 

Can custom courses be updated easily?

Totally. You own the content, so edits, tweaks, or upgrades? All yours. 

What if my team learns differently?

Custom can adapt paths, toss in interactive exercises, and mix multimedia to match every brain type. 

Do off-the-shelf courses track performance?

Mostly basic stuff- completion rates, quiz scores. Custom digs deeper: behavior, skill gaps, all the good analytics. 

How do I choose between speed and impact?

Quick wins? Off-the-shelf. Lasting change? Custom. Pick your lane- or flex both. 

Can Upside Learning handle both?

Yep. They make it seamless- fast deployment, tailored experiences, or a mashup. 

Pick Smart, Train Better

Picking off-the-shelf or custom eLearning? Don’t stress. It’s really about your team, your goals, and the impact you want. Quick wins? Off-the-shelf has you covered. Role-specific skills or behavior change? Custom eLearning is your move. 

Upside Learning makes both options effortless. Whether it’s ready-to-roll courses or fully tailored experiences, we handle the heavy lifting- interactive modules, adaptive paths, branded visuals, and analytics that tell you something. No wasted time, no generic content- just learning that sticks. 

Ready to level up your team’s learning game? Connect with Upside Learning today and see how we make training fast, engaging, and results-driven. Your team deserves training that works- and we deliver. 

The post The AI-Designed Learning Experience: What’s Next for Corporate Training first appeared on The Upside Learning Blog.

Most corporate learning programs fail quietly. Not because the design is poor, but because the vendor selection was reactive. Budgets close, timelines compress, and the team picks whoever fits the brief fastest. A typical RFP runs on checklists- experience, tools, cost, delivery time. All valid, but partial. What it does not capture is how a vendor actually works once the contract begins. 

Learning is a production system. Each vendor carries a distinct production logic. Some optimize for speed, some for volume, some for depth. When those logics do not align with the organization’s learning culture, friction appears. Modules stall mid-development. Review cycles loop. The course looks fine but misses intent. It happens not from negligence but from mismatch. 

Procurement tends to see vendor selection as a transaction. L&D leaders see it differently. It decides how ideas move through people, platforms, and review chains. And how fast those ideas turn into usable training. 

What Defines a “Custom” eLearning Partner

The term “custom” is misleading. It suggests a blank slate, a high degree of flexibility. In practice, most vendors offer semi-custom solutions- template frameworks adapted with branding and limited logic changes. True custom work involves structural design: workflow mapping, skill architecture, learner profiling, and content intelligence. 

Enterprises rarely need full customization. What they need is context-fit. A vendor who can read the business structure fast and translate it into learning architecture. Not everything must be built from scratch. What matters is whether the vendor knows what to reuse and what not to. 

For instance, if a compliance module needs to integrate with an existing risk dashboard, the vendor must understand not just SCORM but data flow between LMS and HRIS. Many “custom” providers still operate in isolation from enterprise systems. The outcome is friction during rollout- manual uploads, broken tracking, misaligned reports. 

Customization, then, is not about creative freedom. It is about architectural alignment. The right vendor spends more time on the blueprint than on the first storyboard. 

Where Most Vendor Evaluations Go Wrong

Most evaluation matrices reward presentation skills. Clean slides, reference projects, familiar tools. But the real differentiator sits in production depth. How does the vendor handle iterative feedback? What QA process sits behind visual design? How do they ensure consistency across multiple IDs or developers? 

Few organizations probe these layers. The assumption is that standard SOPs exist. But many vendors rely on ad hoc coordination between designers and developers. No shared review logic, no fixed file-naming, inconsistent SME communication. When scaled across a global L&D ecosystem, those gaps multiply. 

Even strong vendors can underperform if they are over-scoped. A mid-tier vendor can deliver fast and lean if project governance is tight. The same vendor can collapse under enterprise-scale documentation. Evaluation, therefore, should look at scale matching. Not who can do the most, but who can do this- under current constraints, with current systems. 

What to Check Before Signing a Vendor

Due diligence in L&D is more operational than legal. The contract protects budget; the process defines outcome. A few factors quietly determine success: 

• Workflow Transparency: Does the vendor share their full workflow model? Not just milestones, but dependencies- design, media, QA, deployment.

• Team Continuity: Are the same people who pitch also executing? Vendor switching within teams often resets context.

• Tool Compatibility: Is the authoring stack compatible with the organization’s current LMS and data reporting formats?

• Version Control: Many content breakdowns happen from outdated versions. A vendor’s internal versioning method is a proxy for their discipline.

• Review Logic: How does feedback move? Email chains or tracked dashboards? The latter cuts turnaround time by half in most cases.

Organizations assume these are post-contract details. They are not. These are the difference between a 12-week delivery and a 6-week one. 

How AI Is Changing Vendor Capabilities

AI has begun to separate production vendors from design vendors. A decade ago, the difference was minor. Now it is structural. Vendors with integrated AI pipelines can compress scripting, media, and localization phases simultaneously. Not by replacing humans, but by collapsing sequence dependency. 

For example, AI-assisted media generation allows design and voice production to run parallel to content approval. Instead of waiting for a final storyboard, the system iterates in sync. It changes cost behavior- not by discounting, but by shortening cycle time. 

Some vendors have started using AI-powered learning platforms internally for analysis and testing. They simulate learner behavior before rollout, identifying weak engagement points early. This kind of predictive QA was impossible with manual testing. 

The real impact, however, lies in workflow design. Vendors who build AI into their process can iterate 3x faster without compromising compliance checks. But speed alone is not advantage. What matters is integration- how AI tools align with corporate review hierarchies, data privacy policies, and LMS governance. 

Not all vendors are equipped for this. Many experiment on the surface- using AI for copy generation but not for structural optimization. Selecting a vendor today requires understanding how AI sits within their development logic, not just in their sales pitch. 

How to Evaluate Cost vs. Value in Custom eLearning

Price comparisons look simple on paper. In practice, they hide more than they reveal. The quoted rate per learning minute or module often excludes factors that define the real cost of delivery. A better approach is to unpack where value actually lives. 

• Throughput, Not Volume: Measure how much usable learning content is produced per project cycle. A 30-minute course completed in 4 weeks can be more efficient than a 45-minute one that takes 10 weeks. The metric is not duration - it’s usable learning per production week.

• Rework and Internal Lag: Each revision or SME review adds hidden cost. Vendor models that reduce rework through structured feedback loops save far more than a lower quoted rate.

• Integration Overhead: If the vendor’s content needs manual LMS uploads, reformatting, or reporting adjustments, the internal cost rises sharply. Seamless integration is financial efficiency.

• Scalability Without Dilution: The right vendor maintains quality when volume increases. Scaling without governance loss is a form of value often missed in early pricing discussions.

• Outcome-Based Pricing: Some enterprises now align payment milestones with adoption or completion metrics. Vendors confident in their process maturity often agree to this model- and usually perform better under it.

A vendor’s price matters, but cost behavior over time matters more. In large programs, efficiency compounds quietly. Those who measure value by throughput and friction, not hourly rates, make better long-term choices. 

How Upside Learning Fits Into This Framework

Upside Learning has positioned itself in the intersection of scale and precision. It operates with a modular production structure, balancing human expertise and AI-assisted workflows. Its systems focus on collapsing non-creative time- the intervals between design, approval, and deployment. 

Projects often integrate AI-driven media synthesis, automated translation layers, and behavior-mapped QA systems. This shortens average project turnaround from months to weeks without reducing quality or governance compliance. 

Unlike template-based providers, Upside’s model emphasizes architectural fit. It begins with a diagnostic of client systems- LMS compatibility, review cadence, SME bandwidth- and builds the workflow around those realities.  

This approach also extends beyond content creation into performance consulting , aligning learning architecture with measurable performance outcomes

In recent projects, Upside Learning has used AI-powered learning analytics to simulate learner engagement before deployment, refining module flow before it reaches production. That kind of pre-emptive validation cuts error loops significantly. 

Its approach reflects an understanding that “custom” is not about novelty; it is about operational accuracy. A vendor that maps learning architecture correctly the first time rarely needs rework. 

The Real Measure of a Good Vendor

No single metric defines the right vendor. Not delivery speed. Not price. Not the number of past clients. What defines value is the stability of collaboration- the ability to sustain consistent quality across cycles, across people, across contexts. 

Good vendors do not market innovation; they maintain predictability. They integrate into the enterprise rhythm without adding administrative load. They reduce the noise in review loops, track decisions clearly, and keep content pipelines moving when projects scale. 

In enterprise learning, predictability is underrated. It is not glamorous. It does not headline product brochures. But it decides which programs survive the fiscal year and which stay in backlog. 

And that is ultimately the quiet distinction between a vendor who builds learning and one who sustains it. 

If you’re exploring how to choose or work with the right custom eLearning partner, Upside Learning can help. Get in touch to start the conversation. 

FAQs: Custom vs. Off-the-Shelf eLearning

How do I know if my team needs custom eLearning?

If your workflows, tools, or brand vibe are one-of-a-kind- or if behavior change matters- custom’s your cheat code. 

Isn’t off-the-shelf cheaper?

Yep, upfront it’s lighter on the wallet. But for long-term wins and role-specific skills? Custom flexes harder ROI. 

Can I mix both approaches?

For sure. Start with off-the-shelf for the basics, then sprinkle in custom modules where it really counts. 

How long does custom eLearning take?

Depends on how ambitious you get- usually weeks to months. Planning ahead keeps you from sweating deadlines. 

Are off-the-shelf courses engaging enough?

For general topics, yeah. For real-life scenarios or changing habits? Engagement can ghost you. 

Can custom courses be updated easily?

Totally. You own the content, so edits, tweaks, or upgrades? All yours. 

What if my team learns differently?

Custom can adapt paths, toss in interactive exercises, and mix multimedia to match every brain type. 

Do off-the-shelf courses track performance?

Mostly basic stuff- completion rates, quiz scores. Custom digs deeper: behavior, skill gaps, all the good analytics. 

How do I choose between speed and impact?

Quick wins? Off-the-shelf. Lasting change? Custom. Pick your lane- or flex both. 

Can Upside Learning handle both?

Yep. They make it seamless- fast deployment, tailored experiences, or a mashup. 

Pick Smart, Train Better

Picking off-the-shelf or custom eLearning? Don’t stress. It’s really about your team, your goals, and the impact you want. Quick wins? Off-the-shelf has you covered. Role-specific skills or behavior change? Custom eLearning is your move. 

Upside Learning makes both options effortless. Whether it’s ready-to-roll courses or fully tailored experiences, we handle the heavy lifting- interactive modules, adaptive paths, branded visuals, and analytics that tell you something. No wasted time, no generic content- just learning that sticks. 

Ready to level up your team’s learning game? Connect with Upside Learning today and see how we make training fast, engaging, and results-driven. Your team deserves training that works- and we deliver. 

The post How to Choose the Right Custom eLearning Vendor: A Practical Guide first appeared on The Upside Learning Blog.

While lighter moments are welcome, two sets of specialists are taking the new hybrid era very seriously. Human Resources (HR) and Learning & Development (L&D) have teamed up to steer modern business through the current choppy seas. They are doing their best to ensure that the organization has a stable team that is prepared to weather the next unanticipated upheaval, whether it involves a virus or not.

Learning takes the lead

Once upon a time, L&D was one of the many functions under the HR umbrella. Then dawned the realization that continuous learning was a business necessity. Came the pandemic and business is now scrambling to recruit, upskill, retain, and engage the team, with L&D at the helm. Organizations are now having to tackle several challenges including remote working, digital transformation, the great resignation, and issues of diversity, equity, and inclusion.

Before, one could count on a vibrant company culture to keep employees engaged. Now, with employees scattered across time zones and coping with connectivity problems, how does one even redefine culture? The senior, who was an expert in getting the best out of a machine or executing a critical task is now struggling with participating in a video call. How can they be kept engaged as they learn to coach remotely so that the organization can continue to gain from their experience?

It may be no panacea, but modern, digitalized learning is the way to stay alive and innovative. It is the way to stay ahead, even when one can’t be sure of the lay of the next lap.

Effective employee development

The seven ways that Keith Ferrazzi recommended some years ago to improve employee development hold good even today.

1. Support and encourage managers to coach their team. More than bosses, we need mentors and facilitators today.
2. Knowledge and skills are valuable but get obsolete fast. It is important to strike a balance between giving too much at one go and giving just what is needed now.
3. Teach employees to take charge of their own learning, and provide personalized solutions to facilitate that without losing sight of the overall business goal.
4. Make it possible for every employee to learn when it suits them best. On-demand, mobile solutions would be one obvious solution.
5. With the office losing its fixed geographical co-ordinates, virtual members are part of the team too. In addition to online courses, they must have easy access to online mentoring and coaching.
6. Want your subordinates to show some more interest in learning? Well, lead by example and become a perpetual learner!
7. Be aware of the learners’ profile. Ensure learning is as palatable to the young who live by the mobile as to those who think the buttons are too small for their fingers.

It is obvious that learning has an important role in employee development. Not surprisingly, L&D also features prominently in Eric Friedman’s list of the top 10 HR to-do issues for 2022.

1. Improve diversity, equity, and inclusion (DEI).
2. Pave the way for remote, in-office, and hybrid work.
3. Improve change management.
4. Build critical skills.
5. Equip future leaders who are empathy-driven and conscious of the needs of their subordinates.
6. Find and nurture talent during a shortage.
7. Provide great employee experiences.
8. Identify skills employees will need going forward.
9. Modernize training practices.
10. Foster a healthy work environment (more so when everyone is not under the same roof).

Engaging learning, engaged employees

As “normal” remains in flux and new challenges pop up, there can be no progress without learning. Without engagement, there is neither learning nor learning transfer to facilitate performance. And therein lies a very real HR problem.

The lack of learning engagement can trigger a downward spiral. When learning slips, it dilutes employee experience (EX), and the entire organization can stagnate.

Andrea Boatman describes EX as an indicator of “how satisfied employees are with the full spectrum of their dealings with their employer, such as meaningful work, inclusive environment, or growth opportunities.”

Digital Employee Experience (DEX) is a subset of EX. DEX reflects “how effectively people interact with their workplace digital tools, which allows them to be engaged, proficient, and productive.”

In the typical modern organization, we have HR responsible for overall EX, IT for DEX, and L&D for learning experience (LEX). These are not silos but functions that share equal responsibility for the stability and success of business through continuous learning and learning-driven innovation.

According to Peter Hirst,  Senior Associate Dean of Executive Education at the MIT Sloan School of Management,  a culture of continuous learning is the mark of a healthy organization with engaged employees. “Research increasingly shows professional development is one of the top drivers of employee retention and a positive employee experience. Organizations that support continuous learning understand the enormity of the challenges their leaders are facing today and their need to be supported, connected, and empowered to lead.”

He is excited about the impact of digitalization on learning and, consequently, on employee engagement. “I think a big wave is building around the demand for well-designed, very engaging, live online, and asynchronously delivered experiences for professionals at all levels. I believe that we can deliver more compelling experiences virtually now than ever before. And some elements actually work better online.”

Learning is career development

For his book 1,001 Ways to Engage Employees Bob Nelson did a regression analysis of three million employee surveys and found that “the second-most significant driver of employee engagement is career development—that is, learning, development, and advancement opportunities that are provided to employees on a systematic basis. Career development isn’t an occasional training class or periodical promotions, but rather the daily journey of learning, job skills, and networking that puts employees on the course they most want to travel in their career—with their manager’s help in getting there.”

“Linking learning and career development to employee interests and aspirations is a simple yet powerful way to build employee engagement in your organization,” he affirmed.

Companies looking to tide over the Great Resignation, need to invest in long-term upskilling. “Upskilling is a longer-term investment in augmenting the knowledge, skills, and competencies that help employees advance their careers. When employees are offered and encouraged to take advantage of upskilling opportunities for their personal or professional growth, people metrics, such as employee engagement and retention, also go up.”

Three considerations are important as culled from the views of several HR and learning executives across industries:

1. Empower employees to own their career development, supported by their managers and enabled by the company.
2. Show where ideas go by providing feedback and sharing impact.
3. Provide a road map so that employees can identify clear paths and milestones for performance measurement, going beyond promotions and designation changes.

The story goes that John F. Kennedy, on a tour of NASA headquarters in 1961 at the height of the space race, stopped to speak to a janitor who was mopping the floor. Kennedy asked what the man did at NASA. The man with the mop replied. “I’m helping to put a man on the moon.”

If a random employee can articulate (in person or over a call) your business mission that well, it is a good indicator of impactful learning and engaged employees.

The post Better learning experiences lead to better employee engagement in the modern workplace first appeared on The Upside Learning Blog.

Scientists say that the brain needs to forget if we are to stay sane. Learning experts say a little forgetting before you retrieve something from memory makes it stay longer. Does that mean “unforgettable memory” is not the right prescription for effective learning? By inference, is modern digital amnesia a good thing?

Understanding memory

If you could see it, you are unlikely to find memory neatly laid out like a document. According to André Fenton, a neuroscientist at New York University’s Center for Neural Science, it is more like something inscribed on wet clay. Just as clay gets smudged every time you handle it, “biochemical processes cause memories to shift over time.”

Researchers warn that to maintain that plasticity, “not only the generation of memories but also forgetting (memory loss) is essential.” In other words, if we do not forget some details that do not matter, we will go crazy.

Jill Price should know. She states in her book, The Woman Who Can’t Forget:“I know very well how tyrannical the memory can be. I have the first diagnosed case of a memory condition that the scientists who have studied me termed hyperthymestic syndrome—the continuous, automatic autobiographical recall of every day of my life from when I was age fourteen on…. As I grew up and more and more memories were stored in my brain … I became a prisoner to my memory.”

When it comes to learning and development (L&D), memory is about storing information that is relevant to one’s work and recalling that efficiently when it needs to be applied. This is where some forgetting helps.

Beating the curve

According to Hermann Ebbinghaus, the German psychologist who developed the Forgetting Curve in 1885, we forget 60% of what we have processed within 20 minutes. Most memory would be gone in about eight hours. While that is bad enough in the academic sphere, it can be disastrous in the workplace.

The solution lies in spaced retrieval. We remember our multiplication tables, not because we spent a few hours at a stretch on each table, but because we kept returning to them at intervals. Such spacing is one of the desirable difficulties that enhance the effectiveness of learning.

The idea of desirable difficulties as introduced by Robert Bjork and Elizabeth Bjork (Making things hard on yourself, but in a good way) involves three apparent puzzles.

1. Remembering a recurring event is easier if one forgets the first before the next occurs.
2. Retrieving an event can be a more potent learning opportunity than restudying it.
3. Testing an event has a greater effect if it happens after some forgetting, to make retrieval more effortful and difficult.

Several studies have established that remembering gains a lot from forgetting. The authors of Make It Stick elaborate: “Learning, remembering, and forgetting work together in interesting ways. Durable, robust learning requires that we do two things. First, as we recode and consolidate new material from short-term memory into long-term memory, we must anchor it there securely. Second, we must associate the material with a diverse set of cues that will make us adept at recalling the knowledge later. Having effective retrieval cues is an aspect of learning that often goes overlooked.”

They add: “Effortful recall of learning, as happens in spaced practice, requires that you ‘reload’ or reconstruct the components of the skill or material anew from long-term memory rather than mindlessly repeating them from short-term memory. During this focused, effortful recall, the learning is made pliable again: the most salient aspects of it become clearer, and the consequent reconsolidation helps to reinforce meaning, strengthen connections to prior knowledge, bolster the cues and retrieval routes for recalling it later, and weaken competing routes.”

Digitalization and retrieval

Technology has been accused of causing digital amnesia. Smartphones have made it unnecessary (and unfashionable) to jot down and remember telephone numbers. Does that make retrieval all too easy to be beneficial?

There is more to digitalization and modernization than smartphones. Technology need not be used to undermine the established principles of effective workplace learning but to make it more efficient.

One University of Kansas study has recommended that workers should be granted a few short smartphone microbreaks every day. The study found that this improved efficiency and achievement of goals ahead of or on schedule. Like every tool, technology too may be misused. However, imagine the impact if the workers could also access some relevant microlearning modules during such microbreaks. Or if a salesperson could use commuting time to better understand how to address a customer’s needs.

Technology is here to stay whether in business applications or for personal use. The challenge is not just in digitalizing business processes and the methods to master those, but it is also in making learning match modern digital-centric living. When it comes to learning, the digital must be subservient to thoughtful design. Effective learning design will always bank on time-tested learning principles like spaced retrieval so that the learner can productively apply it on the go and, over a period, retain what matters. Keen to discuss how we can help incorporate spaced retrieval in your learning design? Drop us a note at elearning@upsidelearning.com to set up a chat.

The post A little forgetting before retrieval makes memory stickier for workplace application first appeared on The Upside Learning Blog.

As the Great Resignation runs the virus close in hogging global headlines, is it prudent to freeze all workplace learning and wait for some semblance of stability? Or is it time for L&D to move front and center to empower and engage the team to ensure that the organization survives not just this but every crisis ahead?

In other words, is it time to digitalize and modernize workplace learning  so that it can play the role it needs to today?

The great learning opportunity

Anthony Klotz¹, who is credited with coining the term “great resignation” believes that it is “a moment of empowerment” for employees. Leaders are pursuing flexibility and asking how they can “give people more power over their schedules.” He expects work to “fit around our personal lives rather than our personal lives fitting around work.”

Isn’t the need to empower employees and to ensure they enjoy greater flexibility a signal for L&D to step up to the rescue?

Talking of rescue acts, teachers all over the world deserve applause for their continuing efforts in very trying circumstances to “ensure a generation of students can reach their full potential.” L&D professionals may not call themselves teachers, but it is interesting to peep over the wall and see what has been happening in schools in the pandemic era.

“In this crisis,” reads a UNICEF statement², “teachers have shown, as they have done so often, great leadership and innovation in ensuring that #LearningNeverStops, that no learner is left behind. Around the world, they have worked individually and collectively to find solutions and create new learning environments for their students to allow education to continue.” We need to think beyond COVID-19 and “work to build greater resilience in our education systems, so we can respond quickly and effectively to these and other such crises.”

Doesn’t that hold good for workplace learning systems, too?

There are more interesting parallels. According to a World Bank blog³, “pedagogical adaptations have proven to be pivotal as the traditional lecturing in-person models do not translate to a remote learning environment. No matter the type of channel used (radio, TV, mobile, online platforms, etc.), teachers need to adapt their practices and be creative to keep students engaged as every household has become a classroom – more often than not – without an environment that supports learning.”

Business leaders must be aware of the need to “adapt their practices and be creative” to keep essential learning going. The L&D function is all too keenly aware of the need to explore multiple channels to reimagine workplace learning to suit the new work world.

Learning must take the lead

Modern learning is not about giving the old a new coat of digital paint. Pamela Stroko⁴ of Oracle is probably right to expect “learning playing a larger part in overall organizational strategy.”

Before beginning learning renovation, the top leadership (including L&D) need to zoom in on a few answers first.

1. How have your business priorities changed and are likely to change?
2. What do you need to do to stay nimble? How can you slash the response time and keep the team ready for a quick pivot?
3. Where are the gaps in skill? Who needs to learn what first? Who needs cross-training?
4. How can you ensure valuable practical skills do not disappear with a departing employee?
5. How well do you know your team? Are you aware of their sensitivities and preferences? What is the best approach to engage them?
6. How can you make learning available where and when a team member needs it?
7. How will you measure the impact of your new approach to learning?

• How have your business priorities changed and are likely to change?
• What do you need to do to stay nimble? How can you slash the response time and keep the team ready for a quick pivot?
• Where are the gaps in skill? Who needs to learn what first? Who needs cross-training?
• How can you ensure valuable practical skills do not disappear with a departing employee?
• How well do you know your team? Are you aware of their sensitivities and preferences? What is the best approach to engage them?
• How can you make learning available where and when a team member needs it?
• How will you measure the impact of your new approach to learning?

Starting the transformation

Once there is clarity about the business goals and how learning can help achieve those, it is time to start the process of transformation. For most organizations, modernization of learning is all about converting “old” learning assets into their virtual, mobile-friendly avatars. However, there is more. and we, at Upside Learning say this from experience. Having led the way for many transformation and modernization initiatives for our clients, this is how we see the bigger picture.

Applicable learning first, technology next

According to experts, the virus has not caused the rush for digitalization but simply speeded it up. David Collings and John McMackin of Dublin City University Business School observe⁵ that organizations were already struggling to keep pace with the new skills demanded by the shift to digital business models. “The pandemic accelerated those trends, putting an increased premium on learning and development (L&D) as a means of equipping companies to handle both long-term challenges and short-term crises.”

They found that companies that had a strong long-term L&D orientation going into the pandemic were well equipped to handle the short-term pressures that it created and to adapt quickly to new ways of working.

Anthony Klotz believes that the great resignation will stabilize soon. While businesses would welcome that, that does not mean an end to all variants of crises. Digitalization and modernization can give an organization a stronger, more flexible learning base which can help it cope better with probable disruptions the future may hold.

After all, as Jack Welch put it, “An organization’s ability to learn, and translate that learning into action rapidly is the ultimate competitive advantage.” And that involves adapting learning to the latest in technology and learner preferences.

The post Time to onboard the great learning modernization first appeared on The Upside Learning Blog.

There was a time in the 1970’s, when workplace training was somewhat similar. An instructor would address a group of workers, run through some how-to’s and dare-not’s and at the end of it, the workers would hustle back to work. It did not matter if everyone got the message. There was no room for questions. You were expected to attend whether you liked it or not.

We still have big machines and training has not gone away. What has changed is the focus.

Today, we have the employees front and center. We respect their cultural background and skill level. We don’t merely talk to them; we take care to engage them.

The instructors have put away their pointers and stepped down from the podium. They have now moved to the screens intimately close to each learner, delivering just the right learning at the right time, tailored to the learner’s preferences and the organization’s larger goal.

Welcome to digitalization and modernization! It is not about simply migrating to a digital format. It is about using the opportunity presented by the new constant of frequent change. It is also about adapting learning to better suit the new generation of employees in the new work world.

Adaptation, more than conversion

For long, instructors have led training in organizations. Even when some companies shifted to the digital way of working, instructors remained at the helm of some programs. In came the virus and, with due respect to their skills, out went the instructors. The pandemic forced everyone to move from Instructor Led Training (ILT) to Virtual Instructor Led Training (VILT).

This conversion isn’t simply about digitizing the existing ILT content. It is an opportunity to revisit the purpose of the learning, redesign the media, and refashion the content for a modern, digitalized learning experience tailored to the modern, mobile employee.

At Upside Learning, we study the subject matter, the ecosystem of workplace learning, expected performance outcome, and how well we can match the learning with the learners’ profile.

It is about purposeful chunking of content, retaining only what is essential for learning. We design the new components (ranging from audio and video snippets to comic strips) with a human touch. We focus on the quality of the interaction of the learner—with the content and with trainer (who is now more of a close facilitator and guide than a distant teacher).

Going about it

We work with our customers in three phases. First, we help market the VILT, share read-this-first material, and conduct pre-assessments. Then we go about the conversion in a logical sequence of critical messaging interspersed with session breaks, activities, and interaction opportunities. After the launch, we hand out job-aid reading material, invite feedback on in-session activities, conduct post-assessments, and design follow-up interventions.

Any new initiative at the workplace is best eased in so that it can overcome resistance and even hostility. Much like the movies, we recommend a trailer or a teaser to ease apprehensions, trigger curiosity, and nurture excitement about the VILT. While the learner is the hero for every VILT, it is also important to train managers and subject matter experts in the new, virtual way to impart learning.

Virtual or in-person, learning must be interactive to be engaging and productive. A touch of gamification would add some spark, but only if the game elements are relevant to the learning.

The modern learner prefers to be an active partner rather than a passive participant. Learners must have ample opportunities to provide their feedback.

We plan breaks into the sessions to give the learners an opportunity to mull over and, more importantly, practice what they have learnt. Small groups could use breakout rooms to discuss and debate what they have gathered, thus introducing the all-important social dimension in modern learning.  

VILT is a flexible learning solution. Each employee can learn at a time that best suits one or the whole team can learn together. VILT makes it easy to scale up and adapt learning to the rapidly changing needs of business and the profile of new employees.

Virtual or real, effective learning matters

Learning in the Modern Times that Charlie Chaplin portrayed might have been different. In the modern times that we live and work in, the fundamentals of learning are well-established—know your business goals, know your learners, know their appetite, know the tools that can deliver learning, and when and where they need each tool most so that they can apply it productively.

A poll we conducted in early December 2021 revealed that for most respondents, modernization meant converting ILT to VILT or repurposing learning assets for learning experience platforms (LxP) and/or mobile (39%). For a lesser proportion of people, modernization was about conversion of Flash to HTML5 or transforming events to campaigns.

We believe there is more to modernization. There cannot be exclusive compartments or formulaic solutions. When business impact is the primary driver, learning must adapt to the times.

Need help to digitalize and modernize your learning? For starters, download this eBook.

The post Beyond mere conversion, virtual ILT is an opportunity to modernize learning first appeared on The Upside Learning Blog.

What if learning were wine? Would that make digitalization and modernization of learning so much old wine in a new bottle? Or new wine in old bottle?

When it comes to learning today, both the wine and the bottle need to be new.

Learning is not wining

To forget might be one of the prime reasons to enjoy wine but the key purpose of learning is to remember—and remembering what matters is essential to deliver the right action at the right time.

Keeping the wine glass aside for a moment, why should one learn at work?

An individual may learn to pick a new skill, perform better at an old task, earn a reward, avoid punishment, or simply because others are doing it. For businesses, learning could help boost performance, derive greater value from innovation, or stay ahead of the competition. Those reasons are age-old.

What has not aged is the fundamental principles of learning. Learning must relate to business strategy, performance targets, and personal goals. What is important is the not the learning per se but doing that results from the learning.

On the other hand, what has changed is the skillset required at the workplace. What has also changed is the way the workforce prefers to learn those skills.

Power to the learner

Today change is speedy, the flow of knowledge is speedy, so learning must be too. Jane Hart points out that instead of being hierarchical and top-down, “modern learning is very bottom-up, self-organized, self-managed, self-facilitated, and autonomous.”

Top learning tools are no longer the traditional learning tools. The modern professional learns in very many ways. The organization must support that. Trainers can no longer be just classroom teachers. They must become facilitators, enablers, and guides.

Today’s employees are quite capable of learning new skills and moving into higher-priority positions. According to an analysis, “many employees who have moved into ‘emerging roles’ over the past five years came from entirely different occupations. For example, half of the employees who moved into data science and artificial intelligence (AI) roles were coming from unrelated industries. That number jumps when we look at engineering roles (67%), content roles (72%), and sales roles (75%). What is even more interesting is that the people who transitioned into data and AI had the largest variation in skill profiles, with half of them possessing skills with low similarity.”

Learning must adapt to serve the modern generation’s needs, here and now. According to the LinkedIn study, while older generations of learners are focused more on developing their soft skills, like communication and leadership, “Gen Z employees are spending their time learning how to be more productive and on business and technology basics—from learning how to code Python to understanding online marketing foundations.”

Not just conversion, but a fresh experience

A poll conducted in early December 2021 revealed that for an equal proportion (39%) of organizations, modernization meant conversion of instructor-led training (ILT) to its virtual version (VILT) or repurposing for learning experience platforms (LxP) and/or mobile (39%). While 13% opted for conversion of Flash to HTML5, only 8% thought transforming events to campaigns amounted to modernization.

Like those who responded to that poll, we at Upside Learning believe there is more to modernization. It is not all about upgrading technology or porting lessons from a defunct platform to a modern one.

When we tailor a learning experience for you at Upside Learning, you are assured of an instructionally sound learning experience rooted in your business needs. It is enriched by apt visual style, media treatment, and screen composition. It shall facilitate meaningful, contextualized interactions.

We study the subject matter, the learning and performance ecosystem, expected performance outcome, and how well we can match the learning with the learners’ profile. We revisit the purpose of the learning, redesign the media, and repurpose content for a modern, digitalized learning experience.

It is about purposeful chunking of content, retaining only what is essential for learning. We design components with a human touch. We also focus on the quality of interaction—employee-content as well as employee-instructor. We use several components depending on the need, ranging from audio files to comic strips to gaming scenarios.

We help you identify learning material that must be retained, analyze your training needs to know what must change and use proven processes to complete the conversion.

Ongoing process, not a one-time project

The coronavirus is the cause of many ills but learning digitalization and modernization is not one of them.

David Collings and John McMackin of Dublin City University Business School observe that organizations were already struggling to keep pace with the new skills demanded by the shift to digital business models. “The pandemic accelerated those trends, putting an increased premium on learning and development (L&D) as a means of equipping companies to handle both long-term challenges and short-term crises.”

We do not know if the ambitious 15-ton, 8-feet difference engine developed by Charles Babbage, some 200 years ago, would have gone on to help workplace learning. We do know that the device preferred by today’s learner to connect, entertain, and learn, also fits into the back pocket. Who knows, tomorrow’s worker may just need to clap hands to call up the appropriate virtual retinal display to perform a tricky maintenance task.

In terms of utility and impact, what is current today maybe antique tomorrow. That is what makes digitalization and modernization a continual process and not a one-time project.

Vintage wine is expensive. So is vintage learning. If you let your learning stay “good old” and ferment it for long, it can be very expensive for your business.

Download Digitalization and modernization of learning: Equip yourself to conquer the change constant to know more and to get started.

The post Modern workplace learning needs new wine in new bottle first appeared on The Upside Learning Blog.