AI

Key Takeaways

Upskilling and reskilling do not need to start as large enterprise-wide projects. In fact, if the scope includes every job, every employee, and every skill from the beginning, it becomes too large and accountability becomes unclear. The approach needed in 2026 is to select one or two critical jobs, run a small 90-day experiment, and expand based on the results.

CompTIA’s 2026 Workforce and Learning Trends reports that 83% of organizations place a high priority on addressing skills concerns, and 62% of HR professionals and IT leaders expect AI training budgets to increase over the next year. However, only 34% of companies have formal, organization-wide upskilling or reskilling programs. Interest and budgets have grown, but execution systems remain insufficient.

Therefore, HRD should begin with a “verifiable pilot” rather than “building a perfect system.” The goal of a 90-day pilot is not to open many courses. It is to select changing work, define required skills, diagnose current levels, design learning pathways and work-application assignments, and then check application results and role-transition potential.

A 90-Day Pilot Should Start Smaller Than an Enterprise-Wide Project

Upskilling and reskilling are strategically important, but execution should start small. If the organization tries to build an enterprise skills dictionary, all-job diagnosis, an integrated platform, and a large-scale training system all at once, timelines stretch and business attention declines. A pilot should be an experiment to confirm what actually works in the organization.

CompTIA explains that in building workforce development programs, training costs as well as execution and measurement are major challenges. Survey populations and industry composition may differ from Korean companies, but the signal is clear: HRD must design workable operations before big slogans.

Deloitte’s 2026 Global Human Capital Trends explains that competitive advantage is shifting from static workforce placement to orchestrating people, skills, data, and technology in real time. From this perspective, a 90-day pilot is not simply training operations. It is a small operating experiment in which HRD, HRBPs, business leaders, people analytics, and IT or HR tech owners connect work, skills, learning, application, and outcome data.

The narrower the pilot scope, the better. For example, choose one or two roles where AI or automation is clearly changing work, such as customer support, recruiting, sales operations, production management, or learning operations. The target group can start not with all employees, but with 20 to 50 people in the job where role-change potential is high. What matters is not the number of participants, but whether real work application can be checked after learning.

Days 1–15: Choose Changing Work and Critical Jobs

The goal of the first 15 days is not to choose training courses, but to choose changing work. The question is not “Let’s run AI training,” but “Which work is being automated, augmented, or redesigned?” At this stage, HR should review business interviews, work lists, recent automation-tool adoption areas, the share of repetitive work, and customer or internal-user complaint data.

CompTIA’s finding that 83% of organizations place a high priority on addressing skills concerns shows the urgency of this stage. SHRM’s 2026 AI in HR report also states that in organizations where AI has been deployed, job responsibility changes were reported at 39%, new roles at 24%, and some job displacement at 7%. This signals that workplace AI has a greater impact on changing job responsibilities and creating new roles.

Therefore, during Days 1–15, three decisions must be made. First, the pilot job. Second, three to five key units of changing work within that job. Third, work assignments whose application results can be checked within 90 days. For a recruiting role, for example, candidate sourcing automation, interview-question design, recruiting data analysis, and onboarding linkage could be candidate areas.

The output of this stage is a pilot topic definition document. It should state “which work change we are responding to” before “whom we will train.” Only then will the later skills map and learning pathway connect to actual work change rather than becoming a training catalog.

Days 16–30: Diagnose the Work-Skills Map and Current Levels

During Days 16–30, create a work-skills map for the pilot job. A skills map is not a massive competency dictionary. In a 90-day pilot, three to five changing units of work and five to ten skills required to perform that work are enough. For example, “recruiting data analysis” might connect to data cleaning, metric interpretation, bias review, business reporting, and AI tool use.

CompTIA identifies current training formats such as job role-based training at 64%, foundational AI skills training at 64%, workflow-related training at 62%, and advanced AI training at 53%. These figures show that learning design is moving toward job-role and workflow units. Therefore, the skills map should not list abstract competencies under a job title, but should be built around work and observable performance behaviors.

Current-level diagnosis does not need to be complex at the beginning. Self-diagnosis, manager confirmation, simple task performance, and review of existing outputs can be combined. A four-level proficiency scale is enough: Level 1 is conceptual understanding, Level 2 is performance with a guide, Level 3 is independent performance, and Level 4 is coaching others or proposing work improvements.

The outputs of this stage are a work-skills map and a current-level diagnosis table. The important point is to communicate that the diagnosis is not an evaluation. The pilot diagnosis is not a tool to rank employees; it is the starting point for designing learning and work application over the 90 days.

Days 31–60: Design Learning Pathways and Work-Application Assignments Together

During Days 31–60, design the learning pathway. The common mistake at this stage is to arrange only courses. A reskilling pilot’s learning pathway must include content, practice, business assignments, and manager feedback. The structure should not end with course completion; it should require at least one actual application in work.

TalentLMS’s 2026 L&D Report identifies ways companies respond to skills gaps: upskilling or reskilling current employees at 64%, automating work with AI at 62%, and hiring external specialists at 57%. This signals that companies use multiple responses together. HRD should design learning pathways for internal employees while also considering which work will be automated and which roles may need external hiring.

A learning pathway can be designed in three layers. The first is common foundational learning, such as understanding AI, data, and work change. The second is work-specific practice, using documents, data, customer issues, or processes that employees actually use in their jobs. The third is an application assignment: a small improvement assignment that can be performed in the business within two to four weeks.

The outputs of this stage are a learning pathway table and a list of work-application assignments. Assignments must be defined together with business leaders. Assignments created by HRD alone may not match actual business priorities. Pilot success depends less on the completeness of training content and more on whether work-application assignments are actually performed.

Days 61–90: Validate Application Results and Role-Transition Potential

During Days 61–90, confirm application results. What must be checked is not completion rate alone. HR should examine which work employees applied the learning to, what outputs were produced, whether managers confirmed them, and whether the skill can expand into other work or adjacent roles.

TalentLMS reports that 44% of HR managers prioritize external candidates over internal employees for new roles, while recommending that organizations build internal mobility paths and use skills data to assess role readiness before hiring externally. From this perspective, a 90-day pilot is also an experiment in checking the role readiness of internal candidates.

SHRM reports that 56% of HR organizations do not formally measure the success of AI investments. To avoid this measurement gap, minimum validation criteria must be set before the pilot ends. Examples include completion of application assignments, output quality, manager confirmation, reduced work time, error reduction, customer or internal-user response, and readiness for adjacent-role assignment.

The output of this stage is a pilot results report. A good report does not end with “how many people completed the program.” It should show which work changed, which skills were needed, who reached which level, which work the learning was applied to, and what should be expanded in the next 90 days.

Five Outputs HRD Should Leave After the Pilot

When the 90-day pilot ends, HRD should leave behind scalable operating assets, not just a training results report. Considering TalentLMS’s supporting L&D success indicators—business impact at 37%, career growth outcomes at 31%, and training satisfaction at 28%—the outputs must explain training operations together with career and work outcomes.

First, a work-change map for the pilot job. It should organize which work is shrinking, which work is growing, and which work is newly emerging.

Second, a work-skills map. Instead of listing competencies under a job title, it should connect changing work, required skills, and proficiency standards.

Third, learning pathways and application-assignment lists. The organization should record which learning content, practice, and business assignments actually worked so that the model can expand to the next job.

Fourth, skills diagnosis and application-results data. Pre- and post-level changes, assignment outputs, manager confirmation, and project assignment should be organized.

Fifth, an expansion decision proposal. HRD should recommend which job should be the next pilot, whether a platform or external training is needed, and how the next stage should connect to internal mobility or role transition.

If these five outputs remain, the 90-day pilot becomes the starting point for a skills-based HRD operating model rather than a one-off training program. Deloitte’s real-time orchestration of people, skills, data, and technology also becomes possible when these small operating assets accumulate.

What HR Should Watch Next

Upskilling and reskilling in 2026 are not about increasing the training list. They are about connecting work change, skills data, learning pathways, work application, and performance metrics into one flow. The core message of this series is the same: reskilling is not about selecting people whose jobs will disappear, but about finding changing work and roles into which people can move.

HRD must now ask “which work change should we respond to?” before “what should we teach?” And it must explain “who became able to perform which new work?” rather than “how many people completed the course?” Skills data and performance indicators make that explanation possible.

A 90-day pilot is a realistic way to start this transition. There is no need to wait for an enterprise-wide project. Start with one or two critical jobs, three to five changing units of work, five to ten required skills, and a few application assignments. Start small, but leave data and outcomes behind. Only then will upskilling and reskilling become not buzzwords, but an HR strategy that changes how the organization works.

Key Takeaways

If reskilling outcomes are measured only by completion rates, HRD can explain training operations but not workforce transition. Whether employees attended a course, were satisfied, or passed a test are necessary indicators. But the essence of reskilling lies in whether they can perform a new role or changed work.

TalentLMS’s 2026 L&D Report focuses on an HR manager sample and covers learning design, budgets, priorities, and performance measurement. It presents supporting measures of L&D success: business impact at 37%, career growth outcomes at 31%, and training satisfaction at 28%. The fact that business impact and career growth outcomes appear alongside satisfaction is important.

SHRM’s 2026 AI in HR report also reveals a measurement problem. Fifty-six percent of respondents said their HR organizations do not formally measure the success of AI investments, and only 16% said they use their own ROI metrics. As AI training and reskilling investments grow, HRD will remain stuck reporting that it “trained many people” unless a measurement system follows.

Completion Rates Are Necessary, but They Are Not the End of Reskilling Outcomes

Completion rates should not be discarded. They are basic indicators that confirm whether training was delivered, whether the target population participated, and whether a minimum learning experience occurred. The problem arises when completion rates are treated as the final outcome of reskilling. Even with high completion rates, reskilling is not complete if employees cannot take on new work or if the business has not designed role transitions.

CompTIA’s 2026 Workforce and Learning Trends identifies training costs as well as execution and measurement as major challenges in building workforce development programs. Although survey populations and industry composition may differ from Korean companies, this signal shows that HRD must design measurement structures in addition to cost and operations.

Reskilling metrics should be divided into three layers. The first is training-operation metrics: completion rates, attendance rates, satisfaction, and pre- and post-assessments. The second is work-application metrics: what work was applied after training, what outputs were produced, and whether managers confirmed them. The third is workforce-transition metrics: internal mobility, role transition, project assignment, and readiness to perform new work.

The First Indicator Is Work Application

The first outcome of reskilling is “where the learning was used.” Even if satisfaction is high immediately after training, it is hard to connect learning to organizational outcomes if it is not applied to work. Therefore, HRD should collect work-application data at 30-, 60-, and 90-day intervals after course completion.

TalentLMS’s 37% business-impact figure as a supporting measure of L&D success points in this direction. SHRM also mentions improved productivity, cost savings, improved decision-making, and employee satisfaction as metrics for measuring AI investment outcomes. Both sources show that the outcomes of learning or AI investment should be explained as “work results changed,” not “training was attended.”

Work-application metrics do not need to be grand. For AI training, examples include reducing report-drafting time, improving the accuracy of customer-inquiry classification, reviewing meeting-summary quality, automating repetitive work, or preparing data-interpretation reports. For reskilling training, HRD should also review the number of new-work attempts, participation in business assignments, output review results, and manager feedback.

What matters is not ending with employee self-reports. Self-reports should be combined with manager confirmation, outputs, and project-assignment records. Only then can HRD explain what work employees became able to perform after taking a course.

The Second Indicator Is Internal Mobility and Role Readiness

Reskilling is not a learning program; it is a workforce mobility strategy. Especially when AI adoption changes job responsibilities and creates new roles, internal mobility and role readiness become core metrics. SHRM reports that in organizations where AI has been deployed, respondents cited frequent upskilling and reskilling opportunities at 57%, changes in job responsibilities at 39%, new roles at 24%, and some job displacement at 7%. It also explains that workplace AI is 5.7 times more likely to shift job responsibilities and three times more likely to create new roles than to displace jobs.

These figures support measuring reskilling outcomes not as “training completion” but as “role-transition potential.” HRD should look at whether trained employees entered adjacent-role candidate pools, were assigned to projects, held job-transition conversations, or met the required skill criteria for new roles.

TalentLMS also directly mentions the importance of internal mobility. It reports that 44% of HR managers prioritize external candidates over internal employees for new roles, while recommending that organizations build internal mobility paths and use skills data to assess role readiness before hiring externally. This is why internal mobility and role readiness must be included in reskilling metrics.

In practice, role readiness is better managed through a condition table rather than a simple score. HR can review required-skill fulfillment, related project experience, manager recommendation, post-learning application cases, the employee’s willingness to move, and possible timing for placement. This is how HRD data becomes connected to hiring, placement, succession, and performance management.

The Third Indicator Is Skill Validation and Manager Confirmation

The challenge of reskilling is that completion and proficiency are different. Taking a course does not immediately mean an employee can perform a new role. That is why skill-validation metrics are needed. Validation does not mean only test scores. It can include real work assignments, project outputs, simulations, manager observation, peer feedback, and responses from customers or business users.

SHRM’s finding that 56% of HR organizations do not formally measure the success of AI investments reveals a gap in validation systems. Only 16% use their own ROI metrics. As AI and reskilling investments grow, HR must be able to explain which skills have actually been validated.

Validation metrics can be simplified into four levels: Level 1 is conceptual understanding, Level 2 is performance with a guide, Level 3 is independent performance, and Level 4 is coaching others or proposing work improvements. These levels connect to the skills data structure covered in Article 6. Organizations should manage not only skill names, but also actual behavior levels and work-application levels.

Manager confirmation is also important because HRD cannot directly judge proficiency in every job. However, manager confirmation needs criteria so that it does not become purely subjective. Observable standards should be used, such as whether there is a work-application case, whether the output meets criteria, whether the employee can repeat the work, and whether the employee can explain it to others.

HRD Practitioner Dashboard: Viewing Reskilling Outcomes in Five Stages

A reskilling outcomes dashboard does not need to be complex from the start. The key is not to break the flow after completion rates. Considering TalentLMS’s supporting L&D success measures—business impact at 37%, career growth outcomes at 31%, and training satisfaction at 28%—together with SHRM’s 56% figure for unmeasured AI investment outcomes, the dashboard should show both training operations and work outcomes.

CompTIA also cites execution and measurement, not only training costs, as challenges in workforce development programs. Therefore, an HRD dashboard is more practical when designed as an operating table that connects participation, learning change, work application, mobility and transition, and organizational outcomes—not as a simple completion-rate table. The following five-stage structure can be a starting point.

First are participation metrics: target population, participation rate, completion rate, dropout rate, and satisfaction. These are basic metrics for checking training-operation quality.

Second are learning-change metrics: pre- and post-diagnosis, skill proficiency change, assignment pass rates, and simulation results. From this stage onward, the data moves beyond simple attendance records.

Third are work-application metrics: work applied within 30 or 60 days after training, outputs, manager confirmation, and project participation. These metrics connect training to work.

Fourth are mobility and transition metrics: inclusion in internal mobility candidate pools, role-transition discussions, assignment to adjacent-role projects, actual placement transfers, and performance of new roles. If the purpose of reskilling is workforce transition, these metrics cannot be omitted.

Fifth are organizational outcome metrics: productivity improvement, cost savings, quality improvement, decision-making speed, customer experience, employee retention, and hiring substitution effects. Not every course can be immediately converted into organizational outcomes, but each pilot should connect at least one or two work-outcome metrics.

This dashboard should not be an HRD-only report. It should become an operating table shared by HRD, HRBPs, business leaders, people analytics, and executives. Deloitte’s real-time orchestration of people, skills, data, and technology becomes possible only with this kind of connection structure.

What HR Should Watch Next

When reskilling metrics change, HRD’s role changes as well. Training operators become people who gather evidence of work change and role transition, not simply people who finish courses. Completion rates are the starting point; work application, internal mobility, and skill validation become the center of outcomes.

The next step is not to turn these metrics into a massive enterprise-wide project. Attempting to build a perfect dashboard for every job from the beginning is likely to fail. A more realistic start is one or two critical jobs, three to five changing units of work, five to ten required skills, and a few application assignments.

The final article, Article 8, will organize this flow into a 90-day pilot roadmap. The approach is to start small with diagnosis, skills maps, learning pathways, work application, and performance measurement. Reskilling is not a training project; it is a workforce-transition experiment. Its performance indicators must be able to explain that experiment.

Key Takeaways

It can look as if adopting an AI learning platform will automatically complete skills-based HRD. In reality, the opposite is true. A platform is a container for data. If the organization has not decided which skills connect to which jobs, work, and outcomes, learning recommendations, AI coaching, and capability diagnosis remain superficial features.

CompTIA’s 2026 Workforce and Learning Trends reports that 83% of organizations place a high priority on addressing skills concerns, and 62% of HR professionals and IT leaders expect AI training budgets to increase over the next year. Yet the same research states that only 34% of companies have formal, organization-wide upskilling or reskilling programs. Interest and budgets are growing, but operating structures for defining and accumulating skills are still insufficient.

Therefore, HRD’s 2026 question must be “What decisions will our organization use skills data for?” before “Which AI learning platform should we buy?” Skills data is not a record of training completion. It is HR operating data that connects job change, learning pathways, work application, internal mobility, and performance validation.

Skills-Based HRD Starts With a Data Model, Not a Platform

When organizations begin skills-based HRD, they often first review LMSs, LXPs, and AI learning recommendation tools. Tools are, of course, necessary. But when tools come first, an organization can build a system that recommends a lot of learning content while failing to build a system that explains which skills are needed for which work transitions.

Deloitte’s 2026 Global Human Capital Trends explains that competitive advantage is shifting from static workforce placement to orchestrating people, skills, data, and technology in real time. The key here is not technology itself, but the connection structure. People’s current roles, required skills, changes in work, data, and technology use must be connected in one flow.

CompTIA’s survey includes respondents related to workforce development, including HR professionals and IT leaders. In this sample, priority for addressing skills concerns is high, but only 34% have formal organization-wide upskilling or reskilling programs. This gap is difficult to explain as a platform shortage alone. The more fundamental problem is that organizations have not decided what unit to use for defining skills, what data to use for verification, and which HR decisions to connect them to.

A skills data model must answer at least four questions. First, which skills matter by job? Second, how will the current skill levels of employees be confirmed? Third, how will actual work application after learning be assessed? Fourth, how will the results be connected to placement, internal mobility, role transition, and performance management?

The First Data Is Not “Job Title,” but the Connection Between Work and Skills

The starting point for skills data design is not the job title. Even within the same job title, automated work, judgment work that humans must continue to perform, and AI-supported validation work can differ. As discussed in Article 5, reskilling candidate selection should also be based on changing work rather than the entire job. The same principle applies to data design in Article 6.

CompTIA identifies current training formats including job role-based training at 64%, foundational AI skills training at 64%, compliance and security at 62%, workflow-related training at 62%, and advanced AI training at 53%. These figures show that learning is already moving toward the units of job role and workflow. Therefore, skills data is insufficient if it merely attaches a list of competencies below a job title.

In practice, it is useful to break jobs into three levels. The first is the job title: for example, recruiter, learning professional, or sales manager—the role units the organization already uses. The second is key work. For a recruiter, this might include candidate sourcing, interview operations, recruiting data analysis, and onboarding linkage. The third is the skills required for each unit of work. These need to be broken into smaller units such as data interpretation, interview design, stakeholder communication, AI tool use, and privacy judgment.

Designed this way, the platform’s learning recommendations also change. Instead of “recommend AI training to recruiters,” the system can recommend strengthening data validation and candidate-experience design skills that remain after candidate sourcing is automated. Skills data should be a map of work change, not a classification table for a training catalog.

The Second Data Is Skill Proficiency Change, Not Learning History

Many organizations’ LMSs contain course history, completion rates, satisfaction scores, and test scores. But these alone cannot explain which skills employees have actually improved or which work they can now take on. In skills-based HRD, the needed data is not “the employee took a course,” but “a specific skill proficiency moved from one level to another.”

SHRM’s 2026 AI in HR report states that 56% of respondents do not formally measure the success of HR organizations’ AI investments. Survey populations and industry composition may differ from Korean companies, but this shows that measurement structures are not keeping pace as AI adoption and learning investments increase. The same applies to AI learning platforms. If only completion rates remain, it is hard to explain investment outcomes.

Skill proficiency data does not need to start with excessive complexity. A four-level model can be enough. Level 1 is conceptual understanding, Level 2 is performance with a guide, Level 3 is independent performance, and Level 4 is coaching others or proposing work improvements. The key is that these levels must connect to real work behaviors, not course names.

For example, if “generative AI use” is managed as a single skill, the data becomes vague. For HRD professionals, it should be divided into work-specific behaviors such as drafting learning needs analyses, structuring learning content, summarizing survey responses, and reviewing draft training-effectiveness reports. Only then can pre- and post-training diagnosis, manager feedback, and project application cases connect into one skills-change dataset.

The Third Data Is Work Application After Training

If skills data remains only inside HRD, it may support a report that “training was well run,” but it cannot explain that “the organization’s way of working changed.” The organization must also record which work the skill was applied to after training, which projects employees were assigned to, and which role transitions followed.

TalentLMS’s 2026 L&D Report focuses on an HR manager sample and covers learning design, budgets, priorities, and performance measurement. It presents supporting measures of L&D success: business impact at 37%, career growth outcomes at 31%, and training satisfaction at 28%. The fact that business impact and career growth outcomes appear alongside satisfaction is important. It signals that learning data must connect to actual work and internal mobility.

Work-application data does not need to begin with grand productivity metrics. It can start with small data points: work applied within 30 days after training, the output produced, manager confirmation, peer feedback, project assignment, and the unit of work automated or improved. The key is not to break the link between “training completion” and “work change.”

For example, after AI training, HR should check whether employees reduced report-drafting time, improved customer-inquiry classification standards, automated repetitive work, or created criteria for reviewing meeting-summary quality. Only when this data accumulates can the performance indicators discussed in the next article be designed around application, mobility, and role transition rather than completion rates.

HRD Practitioner Checklist: Six Data Questions Before Platform Adoption

Before reviewing an AI learning platform or LXP, HRD must first organize its data questions. CompTIA’s 2026 finding that 62% of HR professionals and IT leaders expect AI training budgets to increase shows growing investment pressure. But the fact that only 34% of companies have formal upskilling or reskilling programs is also a warning to check the data structure before investing.

First, what unit does our organization use to define skills? If job titles, job competencies, work, behavioral indicators, and tool-use capabilities are mixed together, search and recommendation quality will be low even after data is loaded into a platform.

Second, is there a connection table between skills and work? Broad expressions such as “AI capability” need to be replaced by work-behavior phrases such as “summarizes open-ended training satisfaction responses and categorizes improvement tasks.”

Third, how will current levels be diagnosed? The organization must decide whether to use only self-diagnosis, add manager confirmation, review task performance, or look at project outputs. Without a diagnostic method, learning recommendations remain recommendations based on individual interests.

Fourth, how will change after learning be recorded? Completion status, diagnosis-score change, work-application cases, manager confirmation, and project assignment can serve as minimum candidate data.

Fifth, which HR decisions will use this data? The required level of data differs depending on whether it will be used only for learning recommendations, or also for internal mobility, role transitions, succession candidate pools, project assignment, and workforce planning.

Sixth, how will privacy and evaluation risks be managed? Skills data is useful for employee growth support and placement decisions, but it loses trust if used for opaque evaluation or labeling. The purpose of data collection, access rights, retention period, and personal feedback approach must be set before the platform contract.

What HR Should Watch Next

The core of upskilling and reskilling in 2026 is not deploying more learning content. HR needs to be able to explain which work is changing, which skills are needed, who is at which level, and what work employees applied their learning to afterward. That explainability is the role of skills data.

A platform can collect and display this data well. But without a data model, the platform only repeats completion rates and content recommendations. Conversely, if the flow of work-skill-diagnosis-learning-application-validation is organized, a pilot can begin even with a small LMS or spreadsheet.

The next article will examine which performance indicators this data should lead to. Reskilling outcomes are hard to explain with completion rates alone. Metrics such as internal mobility, role transition, work application, productivity improvement, manager evaluation, and project assignment must be included. Skills data is the foundation for building those performance indicators.

Key Takeaways

The most dangerous question when selecting reskilling candidates is, “Which jobs will disappear?” This question quickly creates fear, but it is too crude as an execution standard. A more useful question is: “Which work is shrinking, which work is growing, and which roles can people move into?”

SHRM’s 2026 AI in HR report, based on a survey of 1,908 HR professionals, states that respondents in organizations where AI has been deployed reported frequent upskilling or reskilling opportunities at 57%, changes in job responsibilities at 39%, new roles at 24%, and some job displacement at 7%. It also explains that the organizational impact of workplace AI is 5.7 times more likely to shift job responsibilities and three times more likely to create new roles than to displace jobs.

Therefore, reskilling candidates should be found not among “people in jobs that will disappear,” but among “people whose mix of work is changing, who can move into adjacent roles, and whose learning and placement can be designed together.”

Reskilling Candidates Come From “Changing Work,” Not “Disappearing Jobs”

Looking only at job titles can cause organizations to define reskilling candidates too broadly or too narrowly. For example, within the same customer support role, simple inquiry responses may be automated, while complex issue coordination, customer complaint analysis, and AI response quality review may become more important. If the entire job is treated as a risk group, the organization misses the work that remains and the roles that grow.

SHRM’s survey scope is a global HR professional sample, and its industry and job-group composition may differ from Korean companies. Even so, the figures—39% changes in job responsibilities, 24% new roles, and 7% job displacement—show that reskilling should start from work change, not job elimination. Candidate selection must begin by mapping changes at the unit-of-work level rather than by job title.

The First Criterion Is Not Automation Potential, but the Judgment Work That Remains

Identifying what AI can do is not enough to select reskilling candidates. Even when work has high automation potential, judgment work that humans need to perform better may remain around it. This is why SHRM’s point that workplace AI is 5.7 times more likely to shift job responsibilities than to displace jobs matters.

The first criterion, then, is not “what will be automated,” but “what judgment remains after automation.” If repetitive data entry decreases, exception handling and quality checks may remain. If report drafting becomes faster, data interpretation and decision support may become more important. If counseling responses are automated, customer issue analysis and service-improvement proposals may grow.

The Second Criterion Is Adjacent Roles Into Which People Can Move

Reskilling does not end with training. There must be adjacent roles into which people can be placed. TalentLMS’s 2026 L&D Report states, “Training builds skills, but mobility builds futures,” emphasizing the need to connect internal career paths with learning. At the same time, it reports that 44% of HR managers prioritize external candidates over internal employees for new roles.

This figure shows how important internal mobility paths are in selecting reskilling candidates. If a company says it will develop internal candidates but fills new roles through external hiring, reskilling loses motivation. When selecting candidates, HR should examine the distance between current skills and the target role. If the distance is too small, the case is closer to upskilling; if it is too large, short-term reskilling may not be realistic. It is practical to begin with groups where adjacent roles are visible.

The Third Criterion Is Learning Potential and Placement Potential Together

Reskilling candidate selection can fail if it looks only at willingness to learn. Learning without placement potential becomes an ambiguous promise for both the individual and the organization. TalentLMS reports that 64% of HR managers choose upskilling or reskilling current employees to address skills gaps, 62% use AI to automate work, and 57% rely on hiring external specialists.

TalentLMS also states that half of companies are restructuring roles or responsibilities, and 29% are eliminating positions that rely on outdated skills. These findings should be interpreted differently depending on each company’s situation, but one thing is clear: reskilling is not a training program. It is a workforce-strategy choice among automation, role restructuring, external hiring, and internal mobility.

Practical Matrix: Four Questions for Separating Reskilling Candidate Groups

First, are shrinking work and growing work within the current job clearly distinguished? Second, is there adjacency between current skills and the target role? Third, can learning produce a verifiable output within three to six months? Fourth, is there a project or role available after training?

Using these four questions makes execution priorities clearer. A group with major work change, adjacent roles, and high placement potential is a first-priority reskilling target. A group with major work change but low role adjacency or placement potential needs longer-term transition or separate workforce planning. A group with limited work change whose main need is better current-role performance is closer to an upskilling target than a reskilling one.

Since CompTIA reports that 83% of organizations place a high priority on addressing skills concerns, many companies face similar questions. What matters is not labeling all employees as reskilling targets at once, but distinguishing survey population, job groups, work change, and placement potential—and starting with a small pilot.

What HR Should Watch Next

Selecting reskilling candidates is not a process of classifying individuals as risk groups. It is a workforce-strategy decision that looks at work change and role-transition potential through data and checks whether the organization can actually open mobility paths. HR must look at work, not job titles, and consider placement potential alongside willingness to learn.

The next article will cover the skills data design that makes this judgment possible. Before adopting an LMS or AI learning platform, organizations need to define what skills data should be accumulated and validated, and how.

Key Takeaways

A skills-based organization is not completed when the training team creates a skills dictionary and opens courses. The core question is not whether the organization records “who learned which skill,” but whether those skills are connected to hiring, placement, performance management, internal mobility, and compensation discussions.

Deloitte’s 2026 Global Human Capital Trends explains that competitive advantage is shifting from placing talent inside static organizational structures to orchestrating people, skills, data, and technology in real time. CompTIA also reports that 83% of organizations place a high priority on addressing skills concerns, but only 34% have a formal, organization-wide upskilling or reskilling program for current employees.

This gap shows why skills-based organizations often end as HRD-only projects. If training is offered but not connected to jobs, business demand, internal mobility, and performance indicators, skills remain learning records rather than operating data.

A Skills-Based Organization Is an Operating Model, Not a Skills Dictionary

When many companies begin building a skills-based organization, they first create a skills dictionary or competency model. A common language is, of course, necessary. But Deloitte’s key point is orchestration, not lists. What matters is a way of connecting people, skills, data, and technology in real time and reconfiguring capabilities around performance.

Survey scope and industry composition differ by report, but the direction is consistent. CompTIA describes skill building as a top-tier business priority while reporting that only 34% of organizations have formal, organization-wide upskilling or reskilling programs. The language of skills has spread, but many organizations have not yet converted it into actual workforce operations.

So the first question is not, “Does our company have a skills list?” It should be, “Which decisions use skills data?” Does it change hiring requirements, recommend internal candidates, set learning priorities, or serve as a growth goal in performance conversations? These are the questions that matter.

HRD Builds Learning Pathways, but It Cannot Change Placement Alone

HRD is an important starting point for a skills-based organization. But HRD’s work is mainly diagnosis, learning pathways, course design, and learning-record management. Actual role changes and placement must connect with the business, HRBPs, organization design, hiring, and performance management.

In CompTIA’s respondent sample, 46% said development budgets sit with HR/L&D, while 43% said they sit with individual departments. Another 10% said they come from external funding. This split in budget ownership signals that skill development is already not moving through HRD budgets alone.

SHRM’s 2026 AI in HR report carries a similar implication. In a sample of 1,908 HR professionals, AI adoption is more often connected to IT, legal and compliance, and cross-functional teams than led by HR alone. The same is true for skills-based organizations. The training team can create courses, but the business must help decide which work is changing and which roles are emerging.

HRBPs and the Business Must Translate “Needed Skills” Into Work Change

In a skills-based organization, the role of HRBPs and the business is not merely to forward training requests. A request for “data analytics training” must be translated into “which data must be interpreted for which work decision.” A request for “AI training” must be made more specific: which repetitive work should be automated, and which validation responsibilities must remain human?

SHRM reports that in organizations where AI has been deployed, HR professionals cite frequent upskilling and reskilling opportunities at 57%, changes in job responsibilities at 39%, and new roles at 24%. Only 7% mention some job displacement. These numbers show that changes in work responsibilities and role combinations are a more important management target than a simple story in which entire jobs disappear.

That is why HRBPs and the business need to look at units of work rather than job titles. HRD can design real learning pathways only when the organization defines which work is being automated, which work is becoming more important, and which skills become criteria for internal mobility.

Skills Data Does Not Accumulate Unless Hiring and Performance Management Are Connected

A skills-based organization does not operate on learning data alone. Data must also accumulate on which skills are required in hiring, which skills internal candidates have demonstrated, which growth goals are agreed on in performance management, and which experiences are accumulated through project placement.

TalentLMS’s 2026 L&D Report states that 37% of companies measure L&D by business impact. It also notes that 84% of HR managers believe L&D programs are connected to career progression, even though the actual employee experience moves more slowly. This gap appears when the operating connections between learning records and career mobility are weak.

Deloitte’s real-time orchestration points to the same issue. If skills data does not lead to hiring, performance management, internal mobility, and project assignment, more data will not change decisions. People analytics should be designed not merely as a dashboard function, but as a way to confirm which workforce decisions actually used skills data.

Practical Checklist: Five Connections to Check When Starting a Skills-Based Organization

First, is each item in the skills dictionary connected to actual jobs and units of work? Second, is HRD course-completion data visible in internal mobility, project assignment, and performance conversations? Third, how is development budget responsibility divided between HRD and the business? As CompTIA’s 46% and 43% figures show, budget responsibility differs by organization, so internal standards must be set first.

Fourth, does the organization clearly define the population and sample for skills data internally as well? Dashboards and KPIs will differ depending on whether the data covers all employees, a specific job group, or critical roles. Fifth, does L&D performance reporting connect to business impact, internal mobility, work application, and performance improvement rather than completion rates? TalentLMS’s 37% figure shows that this connection is still weak in many organizations.

When these five connections are checked, a skills-based organization can start as a change in the HR operating model rather than a new training-team project.

What HR Should Watch Next

The core of a skills-based organization is not opening more training. It is converting skills into the language of workforce decisions. HRD builds learning pathways, HRBPs and the business define work change, and hiring and performance management connect skills to selection and growth standards. People analytics must verify with data whether this process is actually working.

The next article will examine how to select reskilling candidates inside this operating model. Instead of looking for jobs that will disappear, HR needs an approach that prioritizes changing work and roles into which people can move.

Key Takeaways

If employee education in the AI era is understood only as coding education, it is easy to miss the direction of change. Coding and model understanding matter for some technical roles, but for most employees, the first requirement is work judgment: the ability to work with AI. The core capabilities are deciding which problems to give to AI, what data and context to provide, how to verify results, and where human judgment must remain.

CompTIA’s 2026 research reports that 83% of organizations see addressing skills concerns as a high priority, and 62% of HR professionals and IT leaders expect AI training budgets to increase over the next year. This article should be read with the understanding that CompTIA’s HR professional and IT leader respondents, SHRM’s sample of 1,908 HR professionals, and Deloitte and TalentLMS’s 2026 research scopes differ by industry, job group, and job composition. At the same time, CompTIA says job role-based training ranks first among current formats for AI education. This means skills design needs to fit each job’s work scenarios rather than offering the same AI lecture to everyone.

AI Skills Are Work Judgment, Not Tool Usage

A common reason AI training fails is that tool usage is mistaken for skill. CompTIA’s 2026 Workforce and Learning Trends reports that 83% of organizations place a high priority on addressing skills concerns, and 62% of HR professionals and IT leaders expect AI training budgets to increase over the next year. If this budget is spent only on tool-use training, it is hard to connect it to work outcomes. Learning how to enter prompts in a generative AI interface is necessary, but that alone does not change work performance. CompTIA describes AI and digital fluency as an upskilling need for the entire workforce, while also identifying job role-based training as the top current format for AI education. In other words, AI skills must be connected not to generic tool training but to job-specific work judgment.

Deloitte’s 2026 Global Human Capital Trends also explains that competitive advantage is shifting from placing talent inside static organizational structures to orchestrating people, skills, data, and technology in real time. From this perspective, AI skill is less “knowing how to use AI” and more “knowing how to place AI inside the flow of work.”

The First Skill Is Problem Definition

The starting point for AI use is not a good question, but a good problem definition. Even within the same report-writing task, there are parts that can be assigned to AI for drafting, parts that require internal data checks, and parts that require stakeholder judgment. If the problem is defined poorly, AI can produce answers quickly—but those answers may be precisely wrong.

From an HRD perspective, employees should be taught to break work into smaller units rather than simply being told to “try using AI.” They need to distinguish what should be given to AI among repetitive writing, summarization, classification, drafting, comparative review, and decision support. Considering CompTIA’s finding that 80% of HR professionals and IT leaders believe technology factors other than AI also create skills gaps, problem-definition capability becomes the foundation for connecting multiple digital tools to work, not just AI.

The Second Skill Is Data Interpretation and Output Validation

AI-generated results can be dangerous precisely because they look plausible. SHRM’s 2026 AI in HR report, based on a survey of 1,908 HR professionals, finds that 72% believe nontechnical barriers would prevent full automation of HR functions even if technical barriers disappeared. These barriers include acceptance, trust, accountability, and contextual judgment among HR customers such as employees, managers, and candidates.

TalentLMS’s 2026 L&D Report also notes that 22% of learning leaders are concerned about the reliability of AI-generated content. This is a signal that the important capability is not using AI output as-is, but validating it. Employees need to be able to check what data an AI summary, recommendation, classification, or evaluation draft is based on, what context is missing, and whether bias or errors may be present.

The Third Skill Is Collaboration and Ethical Judgment

When AI enters the workplace, people who change how teams work become more important than people who merely use AI well on their own. SHRM reports that in organizations where AI has been deployed, HR professionals cite upskilling and reskilling opportunities at 57%, changes in job responsibilities at 39%, and new roles at 24%. This shows that AI adoption is changing roles and collaboration structures beyond individual productivity tools.

Ethical judgment cannot be separated from this shift. In HR work that affects people—such as hiring, evaluation, compensation, and learning recommendations—responsibility, explainability, privacy, and discrimination risks must be checked before AI outputs are applied. The core skill in the AI era is not producing faster outputs; it is producing outputs that are safe and acceptable.

HRD Practitioner Checklist: Five Questions for Redesigning AI Training

First, does this training teach tool usage, or does it change job-specific decision-making situations? Because CompTIA identifies job role-based training as the leading current format for AI education and describes AI and digital fluency as a workforce-wide upskilling need, training design should start with job-specific cases. In particular, CompTIA’s 62% expected increase in AI training budgets and SHRM’s 57% figure for upskilling and reskilling opportunities show that larger budgets do not automatically lead to outcomes.

Second, can employees distinguish work that should be assigned to AI from work where humans must remain involved? Third, do they have data standards and quality criteria for validating AI outputs? Fourth, has the team agreed on collaboration rules and responsibility standards? Fifth, does increased AI training budget lead not to more one-off lectures, but to work application, validation, feedback, and performance measurement?

If these five questions cannot be answered, AI training may spread quickly but fail to become part of the organization’s skills system. If they can be answered, employees can begin building the core capabilities for working with AI even without learning coding.

What HR Should Watch Next

The 2026 HRD challenge is not to open more AI training. It is to translate work judgment in the AI era into job-specific skills. Problem definition, data interpretation, output validation, collaboration, and ethical judgment are not merely course names. They are the operating language for moving toward a skills-based organization.

The next article will address why these skills cannot be managed only inside the training team. A skills-based organization cannot be built through HRD course design alone. Job architecture, hiring, performance management, internal mobility, and people analytics must all be connected.

Key Takeaways

Upskilling and reskilling may look similar, but they must be treated very differently when designing HRD budgets. Upskilling is an investment that improves performance in an employee’s current role. Reskilling is a transition investment that enables movement into another role or a new job.

This distinction matters because the skills gaps of 2026 cannot be explained simply as a lack of training. CompTIA reports that 83% of organizations see addressing skills-related concerns as a high priority, and 62% expect AI training budgets to increase over the next year. Yet the same research shows that only 34% of companies have a formal, organization-wide reskilling or upskilling program for current employees.

In other words, many organizations see skills issues as important but have not yet systematically separated budgets, target populations, and performance indicators. When these two concepts are used interchangeably, training may increase, but workforce strategy does not become clearer.

Upskilling Is an Investment in Performance Within the Current Role

Upskilling is learning that helps employees perform their current roles better. CompTIA’s 2026 research shows that 83% of organizations place a high priority on addressing skills concerns. This makes upskilling closer to a productivity investment across the current workforce than supplementary training for only a few jobs. Examples include salespeople interpreting customer data more effectively, recruiters validating AI screening results, and learning professionals using generative AI to design courses faster.

The key question is: “How does performance improve within the current job?” The target group is therefore employees who will remain in their current roles while improving productivity, quality, speed, and judgment. The budget can also be relatively broad. Company-wide AI literacy, job-specific use of digital tools, data-driven decision-making, and manager coaching capability are closer to upskilling budget items.

CompTIA describes AI and digital fluency as upskilling needs for the entire workforce. At the same time, it argues that AI is not the only cause of skills gaps. Eighty percent of HR professionals and IT leaders said technology factors beyond AI also create skills gaps. This means an upskilling budget cannot stop at a single “AI lecture.” Although global research differs in survey population, sample, and industry composition, the direction is clear: learning must be designed around job-specific work scenarios.

Reskilling Is a Transition Investment That Enables Movement Into Another Role

Reskilling is not training that simply helps employees do their current jobs a little better. In SHRM’s 2026 AI in HR research, HR professionals in organizations that had deployed AI reported changes in job responsibilities at 39%, new roles at 24%, and upskilling or reskilling opportunities at 57%. In this situation, reskilling is transition learning that enables employees to move when the structure of work has changed, demand for existing roles has decreased, or new roles have emerged.

For example, if repetitive reporting work declines while data interpretation and business consulting roles expand, moving existing report owners into people analytics support roles is reskilling. If some call center counseling work is automated while roles in counseling quality management, customer issue analysis, and AI response review become more important, these may also become reskilling targets.

SHRM’s 2026 AI in HR research shows that AI adoption is creating more changes in responsibilities and new roles than large-scale job displacement. In organizations where AI is deployed, HR professionals reported frequent upskilling or reskilling opportunities at 57%, changes in job responsibilities at 39%, and new roles at 24%. Only 7% mentioned some job displacement. These numbers show that reskilling should be seen not as after-the-fact training following restructuring, but as a mechanism for preparing role transitions in advance.

The First Standard for Dividing Budgets Is “Current Role Strengthening” vs. “Role Transition”

When dividing HRD budgets, the first criterion should not be the training topic, but the workforce decision behind it. In CompTIA’s respondent sample, 46% said development budgets primarily sit with HR/L&D, while 43% said they sit with individual departments. Depending on whether the goal is to improve capability while keeping employees in their current jobs or to create the possibility of movement into other roles, upskilling and reskilling budget structures must differ.

Upskilling budgets usually target a broader population. The duration may be short or medium-term, and work application assignments matter. Reskilling budgets, by contrast, may involve a narrower target group and a longer duration because they must include diagnosis, selection, learning, project practice, mentoring, and placement review.

CompTIA also notes that the location of development budgets varies by organization. In its respondent sample, 46% said development budgets primarily belong to HR/L&D, while 43% said individual departments. This difference is not merely an accounting issue. HRD can build a common structure for upskilling, but reskilling requires confirmation of business demand for roles and the possibility of placement.

Performance Metrics Must Also Differ

Upskilling outcomes should be measured by application in the current role. Examples include work processing time after training, output quality, error reduction, manager evaluation, customer response, and compliance with job-specific AI use guidelines. Completion rates and satisfaction are closer to supporting indicators.

Reskilling outcomes should be measured by movement and transition. Important indicators include entry into a new-role candidate pool, passing project practice, applying for internal roles, transition placement, time to adapt to the new job, and performance after three or six months. The core question is not whether someone took the training, but whether they can actually take on another role.

If this difference is ignored, HRD reports may look good but fail to answer leadership’s questions. HRD can say “how many people attended,” but it becomes difficult to answer “which workforce risks were reduced,” “what internal moves became possible,” and “whether there was any hiring substitution effect.”

HRD Practitioner Checklist: Five Questions to Reflect in 2026 Training Plans

First, does this training strengthen the current job or move people into another role? Since CompTIA reports that only 34% of companies have formal, organization-wide upskilling and reskilling programs for current employees, this question is not about tidying up course names. It is the starting point for deciding survey targets, budget ownership, and performance indicators. If the answer is the former, it is closer to upskilling; if the latter, it is closer to reskilling.

Second, is the target population all employees, a specific job family, or transition candidates? The broader the target, the more likely it is upskilling. The more selection and placement review are required, the more likely it is reskilling.

Third, are the performance indicators current work outcomes or role transition outcomes? Upskilling should include work application metrics; reskilling should include movement, placement, and new-role adaptation metrics.

Fourth, is budget ownership HRD alone, or shared with the business? Reskilling is hard to make work without business demand and actual roles.

Fifth, how different are the survey population, sample, industry composition, and respondent characteristics from our organization? Global report numbers show direction, but final budget allocation should be decided together with internal job-change data.

What HR Should Watch Next

Distinguishing upskilling from reskilling is not a terminology exercise. CompTIA’s 83% skills-priority figure, 62% expected increase in AI training budgets, and SHRM’s 57% reskilling-opportunity response all point to the same issue: HRD needs criteria for deciding where to spend its budget, whom to target, and what outcomes to report to executives.

The next article will cover the core skills employees need in the AI era. Instead of teaching coding to every employee, organizations need to examine problem definition, data interpretation, validation, collaboration, and ethical judgment—the capabilities required to work with AI.

Key Takeaways

In 2026, upskilling and reskilling are no longer just names for training courses. They are operational agendas HR must bring back to the table in order to address workforce strategy, AI adoption, productivity, and job redesign together.

AIHR’s 2026 HR priorities summarize this shift with the phrase “move from headcount to skill count.” Deloitte likewise argues that the ability to orchestrate people, skills, data, and technology in real time is becoming central to organizational competitiveness. CompTIA reports that 83% of organizations see addressing skills-related concerns as a high priority, while 62% expect AI training budgets to increase over the next year.

That means HRD’s starting point must also change. Instead of asking, “What training should we open this year?” HR needs to ask first: “Which work is changing, which skills must now be validated, and who can move into which roles?”

Capability Transformation, Not Training Courses, Has Become the Agenda

In the past, upskilling was closer to supplementary training that helped employees perform their existing jobs better. Reskilling was also treated as an exceptional program for some employees who needed restructuring or job transitions. The situation in 2026 is different.

AI adoption is not eliminating entire jobs all at once so much as redistributing units of work. Areas where human judgment and tool-based automation overlap are rapidly expanding: report writing, data organization, customer response, content production, recruitment screening, and learning design. What is needed in these contexts is not simply tool usage, but a new capability to decide what judgment humans should retain in changed workflows, what supporting role AI should play, and how results should be verified.

TalentLMS describes this as “learning debt.” When work changes faster than learning and development, invisible skills debt accumulates inside the organization. Employees are too busy to learn, managers cannot make time for learning because immediate performance is urgent, and HRD manages completion rates but cannot track actual work transformation. As this gap widens, organizations end up offering more training without achieving capability transformation.

AI Is Redistributing Work More Than Eliminating Jobs

The biggest reason upskilling and reskilling have become important again is that the interpretation of AI is changing. It is hard to build an HRD strategy with only the simple claim that “AI will replace jobs.” In real organizations, replacement, augmentation, role change, and the creation of new roles appear at the same time.

SHRM’s 2026 AI in HR research shows this relatively clearly. Among HR professionals in organizations where AI has been deployed, 57% reported frequent upskilling or reskilling opportunities as a result of AI adoption, 39% reported changes in job responsibilities, and 24% reported the creation of new roles. By contrast, 7% mentioned some job displacement.

These numbers show where HR should focus. The issue is not fear, but redesign. However, because survey populations, samples, and industry composition differ by report, these findings should be read alongside internal job data rather than transferred directly to Korean companies. HR needs to judge which work is being automated, which work is expanding with AI, and which employees can move into new roles. Reskilling should become a mechanism for redeploying people around changing work, not a refuge for disappearing jobs.

HRD’s Question Is Moving From “What Should We Teach?” to “Which Work Should We Change?”

Deloitte’s 2026 human capital trends argue that static jobs and organizational structures alone cannot keep pace with change. Organizations that connect people, skills, data, and technology in real time and reconfigure capabilities around work outcomes will have an advantage.

From this perspective, HRD must become a designer of skills transformation, not merely a training provider. Even when running generative AI training, for example, the effect will be limited if the program stops at teaching prompt-writing techniques. Recruiters need the capability to review candidate evaluation criteria and check bias. Salespeople need the capability to interpret customer data and validate proposals. Even when the training theme is the same, different job-level work changes require different learning paths and performance indicators.

CompTIA also argues that AI is not the only cause of skills gaps. Eighty percent of HR professionals and IT leaders said that technology factors beyond AI also create skills gaps. This means HRD in 2026 should not try to solve every problem with a single AI training program. It needs to consider digital tools, data use, collaboration methods, job-specific expertise, and certification and validation systems together.

Practical Application for Companies: A 2026 Upskilling and Reskilling Design Checklist

First, diagnose work change before surveying training needs. The question is not “What training is needed?” but “Which work has been automated, augmented, reduced, or expanded in the past year?”

Second, separate upskilling from reskilling. Upskilling is learning that improves performance in a current role. Reskilling is learning that enables movement into another role or job. The target population, budget, duration, and performance indicators cannot be the same.

Third, connect AI training to job-specific work scenarios. A common lecture for all employees is not enough for real application. Job-specific use cases, validation standards, risk management, and manager coaching must be designed together.

Fourth, reduce reliance on completion-rate metrics. Metrics such as internal mobility, assignment to new work, project results, manager evaluations, skill validation, and employee career movement are also needed.

Fifth, HRD should not do this alone. As AIHR argues, HR must act as a co-leader of AI transformation. But execution is only possible when HRD, HRBPs, business leaders, IT, and data teams design it together.

What HR Should Watch Next

The core of upskilling and reskilling in 2026 is not opening more training programs. It is seeing the changing structure of work, defining the skills required, and connecting learning to actual role transitions and performance.

The next article will cover how to distinguish upskilling from reskilling. Without a clear distinction between the two concepts, training budgets, participant selection, and performance measurement all become blurred. Organizations building 2026 HRD plans need to start by resetting this distinction.