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Why Alternative Investment Education Is the Next Competitive Advantage
This white paper explores the forces driving the expansion of alternative investments and the increasing interest of younger investors.Business Thought Leadership
September 29, 2026
For years, HR and Learning & Development leaders have divided workforce development into two categories: upskilling and reskilling. Need an employee to become better at the job they already have? Upskill them. Need to prepare someone for a different job? Reskill them. And, while that distinction still matters, it is getting harder to apply.
The difficulty of applying this distinction is underscored by recent data from the World Economic Forum (WEF), which reveals that one in four workers in financial services expects their jobs to be transformed or require reskilling due to AI. Furthermore, a Cambridge Centre study of FinTechs and traditional financial institutions shows a divided industry: 25% expect significant reskilling and transformation. AI is changing individual tasks faster than most organizations can change job titles, job descriptions, or career paths. New technologies appear. Routine tasks disappear or shrink. Critical thinking, communication, and problem solving become more important.
As a result, there is a significant gap between awareness and action: while 75% of US workers expect their roles to shift over the next five years because of AI, only 45% have undergone recent upskilling. In financial services, this shift is happening particularly fast. The World Economic Forum (WEF) reports that 97% of financial services employers expect AI and information processing technologies to transform their businesses by 2030, compared to 86% of employers overall.
This reality moves the conversation beyond the question of upskilling vs. reskilling. The question is now: How far does an employee need to travel from the skills they have today to the work the organization needs tomorrow? Managing “reskilling and upskilling pathways” is now just as critical as the speed of AI adoption itself.
When roles are changing faster than talent systems, organizations can end up hiring externally for capabilities they could develop internally by training employees who possess the skills, and eagerness, to obtain emerging roles. Today, the challenge of upskilling or reskilling is about becoming precise about what employees need to learn, why they need to learn it, and how to get trained on it.
What is upskilling? Upskilling is the act of helping employees build additional or deeper capabilities so they can perform their existing work more effectively or take on more sophisticated responsibilities. Think of this as vertical growth, where employees are equipped with new competencies to perform their current roles in a fundamentally different way.
In the AI context, it is no longer just about teaching a financial analyst to use new software. Upskilling unfolds across three dimensions: baseline AI literacy, workflow adoption, and domain transformation. An advisor might deepen their retirement-planning expertise by learning to leverage generative AI to analyze client portfolios faster, blending human emotional intelligence with machine data processing. A compliance professional might build stronger knowledge about cybersecurity. An analyst might learn to interrogate AI-generated analysis rather than manually produce analysis.
Conversely, what is reskilling? Reskilling prepares an employee to perform substantially different work. This is a lateral pivot for employees whose previous jobs have been displaced or fundamentally altered by AI. For example, L&D might shift displaced talent from legacy back-office operations, like manual data entry or basic compliance checks, into emerging, high-demand areas like AI governance, cybersecurity, or AI-driven data analysis. Someone in a client services role might develop the professional capabilities and licenses to move toward a financial planning role.
In this environment, what is the difference between reskilling and upskilling? Increasingly, the answer depends on the distance between an employee’s current capabilities and the capabilities the work requires. In other words, upskilling and reskilling are no longer two separate boxes, they are a continuum where employees refresh, deepen, expand, and transition.
At one end of the continuum, employees maintain knowledge, licenses, and continuing education requirements. Next comes deeper expertise in their current roles. Then employees add significant adjacent capabilities that expand the role itself. Finally, they develop the knowledge and qualifications required to move into substantially different roles.
Viewed this way, workforce upskilling is no longer a single program. Neither is reskilling employees. Both depend on understanding the size and nature of the skills gap first. Whether upskilling for vertical growth or reskilling for a lateral pivot, the overarching narrative is constant: talent mobility requires ongoing investment in both.
Frequently “upskilling” and “reskilling” are used interchangeably as corporate buzzwords, blurring the lines of how budgets are allocated and success is measured. The terminology is not the real problem, however, job titles are. Job titles can remain remarkably stable while the work changes underneath them.
Consider how a financial analyst may still be called a financial analyst even as AI takes on more data aggregation and first-pass analysis. An advisor remains an advisor even as technology handles more of the routine research and administrative work. Compliance officers may keep the same titles even while new technologies introduce different oversight and risk-management responsibilities.
Eventually, the “upskill or reskill” question becomes harder to answer. Are you upskilling employees to succeed in their existing jobs? Or are you effectively reskilling them for jobs that just happen to have the same titles?.
AI is accelerating that ambiguity. PwC’s 2026 AI Jobs Barometer, which analyzed more than one billion job advertisements, found that the skills required for AI-exposed jobs are changing more than twice as fast as those in the least AI-exposed jobs. New tasks being added to AI-exposed roles are also 2.5 times more likely to depend on capabilities such as judgment, empathy, and creativity.
AI can perform part of a job without eliminating the job. In many cases, it may make the remaining human responsibilities more demanding. This matters enormously in financial services.
Much of the debate about AI and employment still centers on replacement: Which jobs will disappear? Which ones will survive?.
For HR and L&D, that frame is too narrow. The more immediate issue is task redistribution. With tasks projected to be handled 47% by humans, 22% by technology, and 30% collaboratively, L&D must build curricula that teach employees how to collaborate with AI rather than compete against it.
When AI handles routine work, employees may spend less time gathering information, completing repetitive analysis, or producing first drafts. That can free them for higher-value work. But it also creates a development problem that deserves greater attention: routine work is how junior workers learn how to do the job and prepares them for taking on higher-value work later. What happens when AI shortens this apprenticeship?
Fortunately, the PwC research offers some clues. AI-exposed entry-level roles are now seven times more likely than less-exposed entry-level jobs to demand capabilities traditionally associated with more seasoned roles such as strategic thinking.
This means that organizations cannot simply give employees AI tools and call the exercise upskilling. They need to plan for developing skills AI cannot supply. For financial institutions, this may include interpreting an AI-generated recommendation, recognizing when an output requires additional security, communicating complex information to a client, weighing competing risks, or understanding when human intervention is necessary.
In practice, some of that is upskilling. Some may amount to reskilling. Frequently, it’s both. An employee may need AI fluency to continue doing today’s job while simultaneously developing new analytical, advisory, risk, or interpersonal skills because the very nature of the job has changed. The most resilient workforce strategies blend upskilling for immediate technical fluency with reskilling to prepare employees for rapidly evolving core functions.
According to recent research from SHRM, more than one-quarter of organizations surveyed said full-time positions they hired for during the prior year already required new skills. To identify employee skills gaps more accurately, HR needs to understand what a job actually requires and not what an aging job description says it requires.
The problem is that traditional job descriptions are rigid and heavily indexed on specific legacy software or outdated credentials. Strict JDs, meanwhile, can prevent internal mobility. If a role demands “5 years of specialized legacy system experience,” it automatically disqualifies an internal candidate who possesses the high emotional intelligence and technological literacy necessary to adapt to the new AI-driven equivalent of that role.
To circumvent this, HR can review a representative set of roles across the organization and compare the tasks employees perform with the tasks written in the job description (separating out regulatory and licensing requirements). Identify work that AI can now automate or accelerate and then ask what employees will need to do with the time and capacity that technology creates.
A useful exercise is to examine priorities from three perspectives: the role as written, the role as it is performed today, and the role as it will be performed 12 to 36 months from now. The differences are where workforce strategy, such as reskilling and upskilling determinations, emerge.
To bridge this gap, HR should define the skills and competencies a role actually requires and rewrite job descriptions around foundational competencies such as cognitive agility, data fluency, and adaptability, rather than relying primarily on degrees or years of experience.
A skill gap analysis is the process of identifying the distance between the capabilities the organization needs and the capabilities its workforce currently has.
A strong analysis does not just produce a catalog of missing skills. It reveals:
The gaps that pose the greatest risk to the business
Employees who already possess adjacent skills and can upskill quickly
Roles that require a large development shift and need reskilling rather than upskilling
Depending on the severity of the findings, a knowledge gap may call for targeted education or continuing education. A technical gap may require a micro-credential, certification, or professional designation. Preparing for regulated responsibilities may require licensing. A substantial career transition may call for coaching and mentoring.
Knowing how and when to upskill or reskill begins with an understanding of these skills gaps. Whether addressing minor knowledge gaps through upskilling or executing a major career transition via reskilling, analyzing the gap is the essential first step.
There is another obstacle hiding in plain sight in many organizations. HR teams, talent partners, and recruiters are often aligned with specific lines of business (LOB). There is a good reason for this structure. Recruiters build expertise in particular functional areas. HR partners understand the leaders, operating models, and talent needs. But lines of business specialization can create blind spots.
This dynamic creates artificial talent shortages. A recruiter trying to fill a position in one part of a financial institution may naturally look at the external market without realizing that employees elsewhere in the company already possess much of what the role requires. Retail banking might be laying off operations staff while Wealth Management spends millions recruiting data analysts externally, simply because the siloed HR teams do not share talent pools or mobility data.
For example, an employee might have 70% of the needed skills and can be upskilled for 30%. Yet, in this siloed environment, no one sees the match.
To further illustrate this disconnect, recent research backs this up. LinkedIn’s 2026 Talent Velocity research found that 89% of talent leaders worry about getting the right skills to the right work at the right time. Its conclusion is for organizations to move skills and people beyond siloed domains and static structures.
If each business unit maintains its own view of talent, an institution does not truly know what skills it already has. And, if recruiters search externally before identifying internal employees with adjacent capabilities, organizations may miss opportunities to develop people who already understand the company’s clients, culture, systems, and regulatory environments.
HR and L&D need an infrastructure that can recognize when to push reskilling and when to push upskilling. Achieving a true skills-based organization requires breaking down the walls between HR, L&D, and talent planning so learning and work can merge dynamically. Consider this actionable blueprint:
Adopt a “Lighthouse” Peer-to-Peer Model. Traditional, enterprise-wide training modules are often obsolete before they are completed. Deploy small, highly engaged groups that master AI workflows first and act as peer coaches to upskill the broader organization organically.
Create a Centralized Skills Ontology. Map the skills existing across all LOBs to identify adjacent skill sets. A centralized view helps HR to reskill, for example, a compliance officer from Commercial Lending into an AI governance role in Retail Banking.
Treat Upskilling as a Change Journey, Not a Training Event: If employees are trained on AI but managed against old KPIs, adoption will stall. HR should align performance management, career progression, and incentive systems to reward experimentation, iterative learning, and new behaviors.
Put the Learner in the Driver's Seat: Foster a culture where learning is embedded into the flow of daily work. Empower employees to own their reskilling pathways by linking skill acquisition directly to their individual career mobility and compensation goals.
Once this structural foundation is laid, audit critical jobs at the task level. Identify which tasks technology can automate, which AI can augment, and which still rely heavily on human expertise. Rewrite job descriptions based on where the work is going, not where it is.
Then, connect those future roles to the current workforce. Look at adjacent capabilities, not exact matches. Someone does not need to have held the exact target job title to possess many of the skills required to succeed in it.
Finally, change the scorecard. Course completions tell you very little about whether a workforce is ready. Instead, measure whether employees close priority skill gaps. Track:
Credential attainment
Internal moves/fills rates
Time to proficiency,
Readiness for critical roles
Retention after development
Tracking these metrics across both upskilling and reskilling initiatives guarantees that the enterprise views workforce transformation holistically.
Financial services organizations do not need better definitions nearly as much as they need a deliberate way to close the distance between today’s workforce and tomorrow’s skill demand. This changes upskilling vs. reskilling from a binary choice into a workforce strategy.
The future of financial services relies on internal mobility. When leaders reframe upskilling and reskilling from a defensive reaction to a proactive talent strategy, they transform organizational disruption into a sustainable competitive advantage. The strongest institutions will not have to predict every change correctly. They will have built a workforce capable of changing as the work changes.
Ultimately, blending targeted upskilling with expansive reskilling is the modern playbook for institutional resilience.
Kaplan Financial has built a foundation on merging technology with financial education to create an end-to-end learning ecosystem to boost pass rates, accelerate ramp-up times and drive long-term career growth for your employees. If you are interested in learning more about Kaplan’s continuous learning ecosystem, please reach out.
White Paper
Why Alternative Investment Education Is the Next Competitive Advantage
This white paper explores the forces driving the expansion of alternative investments and the increasing interest of younger investors.
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