Artificial intelligence is moving beyond being a mere tool to becoming an active teammate in the workplace. So-called agentic AI – autonomous software agents that can plan, execute, and adapt to achieve goals – is rapidly emerging across business functions, promising huge boosts in productivity and new modes of human-AI collaboration 1. This white paper explores four interlocking themes that together make the case for a bold reimagining of workforce development. First, we examine Agentic AI in the Enterprise, explaining how AI “colleagues” will transform roles in finance, marketing, operations, project management and beyond. Humans will shift from performing routine tasks to orchestrating outcomes with AI, requiring new skills in judgment, oversight, and AI coordination. A short “day-in-the-life” vignette illustrates what working with AI agents might look like for a project manager in 2025. Next, we highlight the Breakneck Evolution of AI vs. Corporate Learning Lag. While AI capabilities surge ahead – with generative AI and automation advancing at record pace – many companies’ learning & development (L&D) practices remain stuck in the past (e.g. static courses, infrequent training) 2. We cite recent reports from McKinsey, Deloitte, and the World Economic Forum revealing a widening skills gap: most employees need continuous upskilling, yet traditional L&D is too slow and fragmented. New concepts like continuous learning, microlearning, and “learning in the flow of work” are introduced as critical responses. Third, we discuss Upskilling the “Unlikely” Talent, challenging the notion that only tech experts can drive AI value. The reality is that business-domain professionals (in sales, HR, operations, etc.) can become effective “AI translators” and analytics innovators with the right training 3. We share data on the high demand for such hybrid talent and give examples (sales manager to sales data analyst, HR generalist to people analytics lead) that champion inclusive upskilling of non-traditional talent. Finally, we explore The Education Gap – Why Universities and Bootcamps Can’t Keep Up. Formal degree programs are often too slow to update, and coding bootcamps may teach narrow skills that quickly date 4. We discuss the shift from legacy credentials to skills-based hiring (e.g. portfolios, micro-credentials) and how companies are launching in-house academies and AI-driven learning platforms to deliver real-time, adaptive training 2 5. Each theme builds toward a coherent argument: to thrive in the age of AI colleagues, organizations must urgently modernize their upskilling models. The paper blends compelling stats, mini case studies, and expert insights to paint a visionary but pragmatic roadmap. We conclude with a call to action for business and HR leaders, policymakers, and investors: invest in continuous workforce learning now, or risk falling behind. The conclusion also spotlights how planAI’s AI-powered upskilling platform exemplifies the kind of solution needed in this new era. I am optimistic about AI’s potential, while empathetic to the very real challenges faced by workers and L&D teams.
Defining “Agentic AI.” Today’s advanced AI systems are increasingly agentic – meaning they can act with a level of autonomy to pursue goals, not just follow explicit instructions. Unlike generative AI (GenAI) that produces content on prompt, agentic AI uses contextual decision-making and iterative planning to carry out multi-step objectives1. In other words, an agentic AI is designed to function independently in open-ended environments: it can plan tasks, execute them, monitor progress, and adapt based on feedback, all in pursuit of a higher-level outcome 1. As one NVIDIA executive explains, earlier AI tools were like co-pilots or assistants, but “now with agentic AI we will see the next level… goal-oriented reasoning and iterative planning to autonomously solve complex, multi-step problems”, which is “set to reshape how businesses operate, potentially redefining roles, workflows and human-AI collaboration models” 1. In essence, agentic AI moves AI from passive tool to active collaborator. It represents a shift from AI systems that only output analysis to those that can take action within defined parameters 6. For example, an agentic AI in a marketing department might not just generate an analytics report, but also continuously adjust campaign spending across channels to optimize a target outcome (like maximizing lead generation) – all while notifying the team of significant changes or asking for human input if uncertainties arise. This level of autonomy is why we talk about moving from straightforward task execution to outcome orchestration. Instead of a human delegating one task at a time to a tool (e.g. “generate this report” or “schedule emails for this list”), we will increasingly see humans setting a goal and AI agents orchestrating many interrelated tasks to achieve that goal.
AI as a Teammate, Not Just a Tool. The rise of agentic AI means that AI systems will function more like virtual team members rather than just software applications. Consider how roles in an organization might evolve when you have AI colleagues handling significant workloads. In such scenarios, AI isn’t replacing people so much as augmenting them – taking over routine or data-heavy tasks and enabling humans to focus on higher-level judgment, creativity, and strategy. Early signs of this collaboration are already visible: businesses are deploying AI agents in various departments, reporting productivity boosts “by a factor of ten” in some cases 1. Below are a few examples of how agentic AI is being applied as a “co-worker” across functions:
Human Resources: Agentic AI agents can automate routine HR tasks (like screening resumes or answering common employee queries) and provide personalized support to employees. This “improves resolution times [and] employee satisfaction” while freeing HR staff to focus on complex people issues and strategy. For instance, an AI HR assistant might handle benefits questions or leave requests autonomously, only escalating to a human HR partner for exceptional cases.
Finance & Accounting: In finance departments, AI agents can perform multi-step processes such as auditing expense reports, checking compliance, detecting fraud anomalies, and even generating forecasts. By automating complex tasks like these with minimal supervision, organizations reduce costs and increase agility in decision-making. A finance manager might rely on an AI agent to continuously monitor transactions for fraud signals and compile monthly financial summaries, intervening only to review discrepancies the AI flags.
Customer Service: Companies are experimenting with AI customer service agents that manage entire customer interactions from start to finish. These agents use conversational AI to address inquiries, solve problems, and can learn from each interaction, adapting their responses over time for a more natural experience. Unlike a simple chatbot, an agentic customer service AI might troubleshoot technical issues by consulting knowledge bases, schedule a repair visit, follow up with the customer, and only involve a human rep if it encounters a novel situation. This not only reduces wait times but ensures 24/7 support with consistent quality.
These examples highlight that AI is taking on tasks that span decision-making and execution, altering how work gets divided between humans and machines. When an AI agent can “manage entire customer journeys independently” or “enhance decision making” in finance, the human role shifts toward oversight of the outcomes those AI agents produce 1. As a result, the job descriptions for people are changing. A marketer in the age of agentic AI might not spend hours tweaking ads — instead, they supervise an AI that runs and optimizes hundreds of ad variations, while the marketer focuses on brand strategy and interpreting market context (areas where human judgment still excels). In project management, rather than manually chasing every task, a project manager could rely on an AI agent to monitor task status, send reminders, and flag risks, allowing the manager to devote more time to client communication and decision-making. One tech analyst describes this future as a “step change in how work is done, with agentic AI acting as a true partner in decision making and execution rather than a passive tool” 1.
New Skills for the Human-AI Workplace. As AI agents handle more of the doing, humans must excel at guiding, validating, and maximizing AI-driven work. Several emerging skill sets will become critical: 1) Judgment and Critical Thinking: Employees will serve as the last line of validation for AI outputs. They’ll need sharp judgment to review AI decisions, catch errors or bias, and make ethical calls. For example, if an AI agent proposes a cost-cutting plan, a human leader must judge not just its financial impact but also implications for customers and employees – things the AI might not fully grasp. 2) AI Orchestration: Think of this as AI project management. Just as a conductor leads an orchestra, professionals will coordinate multiple AI agents and tools, ensuring they work in concert toward business outcomes. This requires understanding the strengths and limitations of different AI systems, how to integrate their workflows, and how to hand off tasks between humans and AIs smoothly. An operations manager, for instance, might oversee a fleet of AI agents each handling inventory, logistics routing, and demand forecasting – orchestrating them so that supply chain goals are met without conflict. 3) Outcome Supervision: Rather than supervising individual tasks, managers will focus on whether the AI-agent’s overall output meets the desired outcome and quality standards. This is a higher-level oversight role – setting the goals, defining success metrics, and then checking that the AI’s autonomous work actually achieves those KPIs (and intervening when it doesn’t). 4) Data and AI Literacy: Working with AI agents also demands a baseline understanding of how AI works – not to the degree of a programmer, but enough to trust (or question) the AI’s process. Employees must know, for example, if an AI’s recommendation might be skewed due to biased data. 5) Collaboration and “Teaching” Skills: Oddly enough, soft skills will matter even more. Workers will need to effectively “communicate” with AI systems – through prompts or goal definitions – and even train or correct them over time. Providing clear instructions to an AI, giving constructive feedback on its output (e.g., refining a prompt or adjusting a parameter), and sharing domain knowledge so the AI can improve are all part of working alongside these systems.
Another crucial skill area is AI ethics and governance. With AI agents making decisions, employees at all levels must be vigilant about fairness, transparency, and compliance. Businesses will need to instill practices like regular AI audits, bias checks, and setting boundaries on AI autonomy. The ability to implement oversight and accountability mechanisms – essentially to “maintain trust and safety” in AI operations – will be in high demand 1. For example, a loans department in a bank might use an AI agent to approve or deny loan applications; the staff must continuously ensure the AI’s decisions don’t inadvertently discriminate and that there’s a clear framework for a human to review contested cases.
Case Study: A Day in the Life – Project Manager 2025 (Working with AI Agents). To make this concrete, let’s imagine “Jane,” a project manager in 2025, running a complex project with the help of several AI agents. Jane starts her morning by consulting her AI Project Orchestrator, which has been working overnight. It greets her with a dashboard of updates: tasks completed, any delays, and risk alerts. One alert catches Jane’s eye – the AI notes that two critical tasks are behind schedule due to a supply shipment delay. However, it also suggests a mitigation plan: it already negotiated with an alternate supplier (via an AI procurement agent) and can re-route resources, but it needs approval for a slightly higher cost. Jane reviews the plan, considers the trade-offs using her human judgment (the cost increase vs. keeping the timeline), and approves the AI’s suggestion, noting the smart solution it found.
Before the team’s daily stand-up meeting, Jane’s AI Documentation Assistant has drafted a brief progress report and even pre-filled the project status in the project tracker. During the video stand-up, one of Jane’s human team members raises a concern about a technical design issue. Jane asks the AI Research Agent in the meeting (perhaps an AI integrated with their conferencing tool) to quickly pull up relevant past projects and knowledge base articles. Within seconds, the AI surfaces two similar past issues and how they were resolved, which Jane and the team discuss and decide to implement – saving hours of manual research.
Throughout the day, Jane delegates numerous small tasks to various AI assistants: scheduling meetings, updating Gantt charts, checking code integration results from the engineering team’s AI tools, and even drafting an email to stakeholders about a project milestone. Rather than doing these herself, Jane focuses on supervising outcomes – she double-checks that the tone of the stakeholder email is appropriate, ensures the schedule update accurately reflects priorities, and verifies that the code integration passed all critical tests (flagging one test result that the AI overlooked due to a business rule it wasn’t aware of). In essence, Jane’s role has evolved into managing the manager: the AI orchestrator manages routine project elements, and Jane manages the orchestrator and overall project narrative.
At day’s end, Jane reflects on her role. She didn’t spend time compiling status updates or chasing team members for inputs – the AI agents handled those. Instead, she tackled higher-level decisions: approving that supplier switch, coaching a team member through the design fix (with help from AI info), and strategizing how to communicate changes to the client. Jane also kept an eye on the outcomes – ensuring the project remains on track in scope, time, and quality. When the AI agents made suggestions, it was Jane’s judgment that ultimately green-lit them. And when the AI overlooked a subtle detail (the business rule in testing), Jane’s expertise caught it. In this future workflow, the AI agents amplify Jane’s productivity immensely, but Jane’s human expertise, oversight, and leadership ensure the technology’s efforts align with real-world project goals and stakeholder expectations.
This scenario exemplifies how work could be redefined. Project management becomes less about manual coordination and more about validation, guidance, and exception handling – focusing where humans add value and trusting AI agents with the rest. Scale this pattern across roles: a marketing manager works with AI to run campaigns, a supply chain director collaborates with AI planners, a doctor in a smart hospital supervises AI doing routine diagnostics, etc. In each case, human-AI teamwork is the norm. Businesses will thus need to cultivate employees who can thrive in these roles – comfortable delegating to AI, but also adept at stepping in as the “adult in the room” to steer things the right way.
Technology Outpacing Skills Development. The past few years have seen AI capabilities advance at an unprecedented rate. Breakthroughs in machine learning and especially generative AI (like large language models that can produce text, images, code, etc.) are being commercialized within months of discovery. Organizations are rapidly deploying these tools to automate tasks and augment knowledge work. For example, by 2023–2024, AI chatbots and co-pilots became common in offices, and a majority of business leaders made AI a strategic priority. A global survey by Boston Consulting Group found 80% of organizational leaders regularly use AI tools in some capacity 7. The benefits of adopting AI swiftly can be huge – early adopters have seen improvements like 40% higher quality and 25% faster output in their operations by using AI 7. In dollar terms, a McKinsey analysis estimated that a $20B company using generative AI across the business could add $500M to $1B to its profits, with sizable gains appearing in just the first 18 months 7. In short, the pace of technology innovation is breakneck.
However, this creates a serious challenge: Can the workforce keep up with the skills needed to exploit these new technologies? Current evidence suggests a growing gap. As Deloitte’s experts put it, “We’re experiencing more rapid change now than ever in our 175-year history… we can’t just hire from the outside to get the skills we need”. The “half-life” of skills – the time in which half the knowledge or skills in a field become obsolete – is shrinking fast. Not long ago, many technical skills would remain relevant for 4–5 years; now that span is often just 2 to 3 years 8. In fact, Deloitte leaders note that “the average half-life of many skills has shortened from five to two-and-a-half years” 8. This means that an employee who learned a particular software or programming language or best practice two or three years ago might already need to update their knowledge significantly to stay current. The workforce simply has to learn continuously to remain effective.
Yet, the reality in many organizations is that learning & development (L&D) is lagging woefully behind this technology evolution. Traditional corporate training models were not built for speed or constant updating. Many companies still rely on static Learning Management Systems (LMS) filled with generic e-learning modules, or periodic training sessions (quarterly workshops, annual seminars, etc.) that try to “catch up” employees on new skills occasionally. These approaches struggle to keep pace with AI’s rapid introduction of new tools and processes. While AI is evolving in real-time, corporate training content might be updated only once a year, if that. As a result, a skills gap is widening: employees are increasingly behind the curve on the latest tech, even as their companies invest in deploying that tech.
Multiple authoritative reports highlight this disconnect. Deloitte’s 2024 Global Human Capital Trends survey found that 73% of organizations acknowledge the importance of keeping employee skills up-to-date with technology – but only 9% feel they are ready and have made progress on that front 2. Similarly, in BCG’s 2024 survey of 1,400 C-suite executives, 66% of top leaders voiced dissatisfaction or ambivalence about their company’s upskilling efforts so far, even though most of them rank AI as a top priority. Tellingly, close to 62% of these executives cited a “shortage of talent and skills” as their biggest challenge to implementing AI, ahead of other barriers like unclear ROI or lack of strategy 7. Yet despite this awareness, action has been slow – only 6% of companies said they have begun upskilling their people in AI in a meaningful way. This is a precarious situation: many firms know they’re behind, but haven’t mobilized effective solutions at the needed speed and scale.
To put the problem plainly, AI innovation is sprinting ahead, while workforce learning is crawling. One McKinsey commentary described this as companies running two races – one to adopt AI, and one to reskill their people – and being in danger of winning the first but losing the second. A telling sign of L&D’s struggle is how employees often feel. Workers frequently report that formal training programs at work are not keeping them current. They resort to Googling answers, watching YouTube tutorials, or taking online courses on their own time to learn new tech because the company’s training happens too infrequently or covers only basics. Josh Bersin, a noted L&D industry analyst, remarked that “generative AI is about to change [corporate learning] forever” because it exposes how inadequate many current learning programs are at delivering help *“in the flow of work” 5.
Symptoms of the Corporate Learning Lag:
Outdated Content: Many corporate training libraries are filled with modules created years ago (for example, a “Data Analysis with Excel 2016” course still sitting on the LMS). Technology and methods have moved on, but the learning content hasn’t. As one Forbes article quipped, rapid tech advancement “spotlights outdated L&D practices”, noting that if we’re teaching people skills that are already past their prime, we’re just widening the gap 8.
Infrequent Training Intervals: Employees often get formal upskilling opportunities only during annual performance reviews or scheduled workshops. This “batch” approach doesn’t work when new tools might emerge every few months. Imagine a company that offered a social media marketing course in early 2022 – by early 2023, the emergence of AI tools for content creation means totally new skills (prompt engineering for copy, AI image generation) are needed, but if the next training update isn’t until 2024, the team loses a year of potential advantage.
One-Size-Fits-All Programs: Traditional L&D often deploys broad curricula that aren’t tailored. Everyone might be asked to take the same digital skills course, regardless of their role or prior knowledge. The result is that it’s too basic for some, too advanced or irrelevant for others – and truly engaging for few. People in such programs tend to tune out.
Learning Siloed from Work: A big issue is that learning is treated as a separate activity, disconnected from day-to-day work. Employees go to a training session or log into a course platform occasionally, but these learning moments are isolated from their immediate problems on the job. There’s little support for just-in-time learning – the kind of immediate help or coaching needed when facing a task. As an example, a customer service rep encountering a new product question might have to dig through a policy manual or request formal training later, rather than having an AI mentor at their elbow guiding them through it in the moment.
New Learning Paradigms: Continuous, Micro, and In-Flow. To close this widening gap, many experts advocate a shift to “always-on” learning models. The key idea is to move away from episodic training to continuous upskilling integrated into the rhythm of work. Here are a few concepts gaining traction:
Microlearning: Instead of 3-hour workshops or 50-slide e-learning modules, microlearning delivers knowledge in bite-sized units – think 5-10 minute lessons, quick videos, or even single interactive quiz questions that reinforce a concept. Microlearning is powerful because it fits into tight schedules. Modern employees can rarely allocate hours for training; in fact, a Bersin by Deloitte study found the average employee has only 24 minutes per week for formal learning. Every minute counts. Microlearning focuses on one nugget at a time (for example, a short clip on how to write an effective prompt for an AI tool, or a daily quiz question on data literacy). Over time, these nuggets accumulate into substantial knowledge. And because they’re brief, they can be consumed in between meetings or during a work lull, aligning with how “time-poor workers” operate.
Learning in the Flow of Work: This phrase, popularized by Josh Bersin, encapsulates the idea of learning being embedded in the work process itself. Instead of pausing work to learn, the learning comes to you while you work. Generative AI is making this very practical. For example, imagine an engineer writing code: instead of stopping to read documentation, they can ask an AI assistant (trained on the company’s code base and best practices) how to implement a function, and get an immediate answer or even a code snippet suggestion 5. Or a customer support agent dealing with a complex complaint could type a natural language question to an AI advisor integrated in their CRM, getting on-the-spot guidance sourced from all past similar cases. This is contextual, just-in-time learning. It’s more efficient and often more retained because the knowledge is applied right away to real work (which reinforces it). Bersin gives an example where instead of taking a generic management course, he asked an AI (Galileo™) a specific question: “How do I deal with an employee who’s always late, with a narrative to help?”. The AI responded with a situational answer, effectively acting as a micro-tutor at the exact moment of need 5. This is what learning in the flow looks like: it feels like part of work, not an extracurricular activity.
Personalized Learning Journeys: Another emerging practice is treating each employee’s skill profile individually. AI can help analyze what someone already knows, what their job requires next, and what the gaps are. Then, instead of everyone taking the same courses, each person gets a tailored development plan. If an employee in marketing is already a whiz at SEO but weak in data analytics, their learning path can emphasize analytics. Meanwhile, their colleague might skip basic marketing strategy lessons and focus on AI content generation tools. Personalization prevents boredom and frustration, and it makes learning directly relevant, which increases motivation. Modern L&D platforms with AI recommendation engines are enabling this, recommending content “Netflix-style” based on one’s role, current skill levels, and career goals 5.
These approaches – micro, in-flow, personalized – all contribute to what we can call a continuous learning culture. It’s a shift from seeing training as a one-off expense to seeing it as an ongoing investment that happens daily. Companies like Deloitte are heavily investing in such models. Deloitte’s “Project 120” initiative (a $1.4 billion investment) aims to create a “predictive and dynamic curriculum” for their employees 8. They recognize that agility in L&D must mirror the agility of the business. A Deloitte leader stated, “Technology is ever-changing at a pace that’s incredibly difficult to keep up with… We want our [L&D] to mirror the pace of creativity within our business” 8. In practice, this means constantly updating learning content and making it available at the point of need, not in a classroom a year later.
Why Companies Must Modernize L&D or Fall Behind. The risk of not adapting corporate learning is straightforward: companies will fail to fully leverage AI (and other innovations) and could see their competitiveness slip. If your competitor has an AI-augmented workforce that quickly learns new tools and tactics, and your workforce is 6-12 months behind because they’re waiting for the next training session, that’s a strategic disadvantage. It could manifest in slower product development, poorer customer service, or higher costs. There’s also a talent retention angle. In-demand professionals are drawn to employers that will grow their skills. As one Deloitte expert noted, “Development at work is a key differentiator in recruiting talent… The opportunity to personally grow becomes the distinguisher in the talent market”. Talented people don’t want to stagnate; if an employer is known for cutting-edge learning opportunities, they’re more likely to join and stay. Conversely, outdated learning can lead to attrition – nobody wants to feel their skills are becoming obsolete due to employer neglect.
It’s also worth noting that being bad at upskilling can nullify investments in technology. There’s evidence of an emerging “AI adoption gap”: companies invest in AI software but don’t see promised productivity gains because workers weren’t trained to use it effectively. BCG research refers to this as the “AI impact gap” – much of AI’s potential isn’t realized due to organizational and skill barriers. In the banking sector, for example, two-thirds of firms struggled to hire or upskill talent for AI, resulting in pilot projects that never scale up 6. In essence, failing to invest in people means failing to get ROI on technology investments.
On the flip side, those that do get L&D right will have an agile, resilient workforce capable of riding the wave of technological change. They’ll capture more value from AI and be less hampered by skill shortages. A statistic from Deloitte’s research underscores this: organizations that take a skills-based approach (prioritizing continuous learning and skill development) are 63% more likely to achieve their business outcomes 2 than those that don’t. That’s a huge competitive edge.
In summary, the current state is a tale of two velocities: AI is transforming work at high speed, and corporate learning must accelerate to match that pace. Companies need to transition from legacy L&D (slow, generic, off-site) to modern upskilling (continuous, tailored, in the flow). The next section will delve into one crucial dimension of solving the skills challenge: tapping into the talent you already have in-house, including those who might not be “tech” today but could be your AI champions tomorrow.
Broadening the Talent Pool Beyond Tech Specialists. When organizations think about becoming AI-driven, they often fixate on hiring data scientists, machine learning engineers, or people with advanced STEM degrees. While those experts are certainly important, they are not the whole story. In fact, a critical – and often underutilized – group in the AI talent equation is existing employees from non-technical backgrounds who can be upskilled to work effectively with data and AI. These are what we might call the “unlikely” talent, because traditionally one wouldn’t expect, say, a sales manager or an HR partner to be central in an AI initiative. But that assumption is changing fast.
The concept of an “AI translator” or “analytics translator” has gained prominence in recent years. An analytics translator is essentially a professional who bridges the gap between the technical data teams and the business units. McKinsey actually dubbed this role “the new must-have” in the age of AI. Translators do not need deep programming or modelling skills; instead, they combine domain expertise (knowing the business, whether it’s marketing, supply chain, finance, etc.) with enough understanding of analytics/AI to identify opportunities and to ensure AI solutions address real business needs. These are often people who today reside in non-IT departments. By 2026, the demand for analytics translators in the U.S. alone could reach 2 to 4 million positions – a staggering number, and far more than the projected demand for data scientists. This stat reflects that every company department could use multiple such hybrid professionals.
Why focus on upskilling existing staff into these roles? Because the supply of ready-made AI specialists is limited and expensive, and they often lack the crucial business context that insiders have. A survey by EY in 2023 found “81% of organizations are experiencing a shortage in skilled tech workers”, especially in roles like “power users” or developer-type talent 9. It’s simply not feasible for most companies to close their skills gap solely by hiring externally – there aren’t enough experts available, and competition for them drives salaries sky-high. Plus, even when you hire a brilliant data scientist, that person might struggle to create impact if they don’t deeply understand your industry or company operations. Enter the internal talent. Employees from marketing, operations, finance, etc., already know the business processes and pain points. With targeted upskilling, they can learn how to apply AI tools to those domain problems.
Moreover, relying only on STEM hires often inadvertently narrows diversity of thought. Those from non-traditional backgrounds can bring creativity and perspective to AI projects (e.g. understanding customer psychology or operational nuances that pure techies might overlook). Upskilling “unlikely” talent is also an inclusive strategy – it says that someone without a computer science degree still has a big role to play in the AI future, which can be very motivating across the workforce.
The Rise of the AI Translator and Similar Hybrid Roles. Many leading companies have started training cohorts of these hybrid professionals. Sometimes they are called “citizen data scientists,” “analytics translators,” “AI champions,” etc. The core idea is similar: leverage business-savvy people and teach them enough tech. For example, a sales operations manager who’s Excel-proficient might learn SQL and Tableau to become a sales analyst generating data-driven sales strategies. Or an HR business partner could learn basic statistics and a people analytics platform to become a people analytics lead, uncovering insights on retention and recruitment. These transitions are happening. A report in Harvard Business Review noted that translators often aren’t in IT – they might sit in marketing or supply chain, but they act as liaisons, ensuring the sophisticated models actually translate to decisions and actions on the business side. McKinsey’s research underscores their impact: translators help “ensure that the deep insights generated through analytics translate into impact at scale”, essentially turning data science output into business value.
Consider a concrete example: “Maria” – a regional sales manager at a manufacturing company. Maria doesn’t code, but she deeply understands her customers and products. The company trains her on an “AI-driven CRM analytics” tool and some basic data interpretation. She learns how to formulate business questions in ways data scientists can answer – for instance, “Which customer segments are most likely to respond to a 5% price increase?” – and how to interpret the model results. Over time, Maria becomes a go-to person connecting the sales team and the data team. She can take a predictive churn model output and craft a practical retention campaign for her salespeople to execute. Maria has effectively become an AI translator for sales: she didn’t need a PhD in stats, but her new data skills combined with her sales acumen create tangible revenue impact. By promoting Maria into this new role rather than hiring an external analytics consultant, the company benefits from her years of customer knowledge and earns buy-in from the salesforce (since Maria speaks their language and has credibility).
Statistics Highlighting the Need for Inclusive Upskilling: The workforce is hungry to contribute in the digital era, if given the chance. In PwC’s 2023 CEO Survey, 72% of CEOs said “upskilling the business” is a top priority, which indicates companies know they must tap into their existing human capital. Additionally, People Management (UK) reported 80% of businesses are struggling to fill talent gaps in tech and digital roles 8. If you can’t hire fast enough, developing internal talent is the obvious solution. Another data point: a World Economic Forum analysis suggests that investment in reskilling could pay off massively – potentially leading to a net 5.3 million new jobs globally by 2030 if done well 8. That’s because while automation may displace some jobs, upskilling can enable employees to move into the new jobs that tech creates (often roles that didn’t previously exist). Significantly, these new roles are not all hardcore technical roles – many are hybrid. For instance, “AI-assisted healthcare technician” or “IoT facility manager” are emergent roles combining traditional domain work with new tech management.
It’s also worth noting the internal morale and retention benefits of upskilling unlikely talent. When employees see that the company is willing to invest in them and open new career pathways, it builds loyalty. It sends a message: you have a future here, even as technology changes. This can be especially impactful for employees who feared being left behind by automation. For example, a warehouse supervisor might worry that AI and robotics will make their experience irrelevant – but if the company trains them to oversee the new automated systems (e.g. managing robotic workflows or analyzing logistics data), suddenly they have a second career act. Amazon’s Upskilling 2025 program is a real-world example: Amazon committed over $700 million to retrain 100,000 employees for higher-skilled roles within the company, including turning warehouse workers into tech roles like data technicians and IT support engineers. This kind of initiative recognizes that potential is widely distributed in the workforce; what’s needed is training and opportunities.
Examples of Non-Tech to Tech Transitions:
Sales to Analytics: A sales manager transitions into a sales analytics lead. She learns how to use business intelligence tools to analyze CRM data, segment customers, and identify sales opportunities. Instead of direct selling, her role shifts to providing data-driven guidance to the sales team (e.g. which leads to prioritize, which product bundles work best). With her frontline sales experience, she knows what insights salespeople will actually use. After upskilling, she becomes the bridge between the data science team (who build predictive lead scoring models) and the on-the-ground sellers, translating model output into actionable playbooks. The result is a sales force that is both data-informed and contextually savvy – and the company doesn’t have to hire an expensive outside analyst who lacks the sales context.
HR to People Analytics: An HR generalist grows into a people analytics specialist. Having managed HR processes, she is familiar with recruiting, performance reviews, and turnover issues. The company trains her on workforce analytics platforms and statistical methods for things like employee engagement analysis. Now she can crunch attrition data to pinpoint why certain departments have higher turnover or use data to show how a new training program impacts performance. Since she understands the nuances of HR (privacy concerns, employee sentiment, etc.), she can work with data scientists to ensure analyses make sense and then guide HR leaders on interpreting the findings. A 2021 survey found 62% of HR leaders admit they can’t use analytics to identify trends in their data 10 – upskilling HR staff like this addresses that gap directly.
Operations to Automation Lead: An operations supervisor at a manufacturing plant learns about IoT sensors and AI-driven maintenance. He becomes an operations analytics lead, responsible for working with engineers to deploy sensors on machines and using AI to predict equipment failures. His years on the shop floor mean he knows what downtime truly costs and how to schedule maintenance minimally disruptively. With new training in data analysis, he now crunches the sensor data and suggests operational adjustments. Instead of fearing that “smart factories” will replace him, he becomes integral in running the smart factory.
Customer Support to AI Trainer: A customer support agent is upskilled to a chatbot trainer/analyst. She’s an expert in customer queries and resolutions. The company implements an AI chatbot; she is trained to feed it the right knowledge base information and continuously improve it by reviewing where it fails. Her role evolves from answering questions directly to making sure the AI can answer them. Because she empathizes with customers, she guides the AI to have a better tone and clarifies responses. It’s a role that didn’t exist a few years ago, but now many companies have “conversation designers” or chatbot curators in their support teams.
These scenarios all involve leveraging internal talent. Importantly, they also promote a culture of internal mobility. Instead of a dead-end job, an HR coordinator might see a path to become a data-driven advisor; a marketer might evolve into a marketing technologist. This helps reduce the feeling some employees have of being “left out” of the tech revolution. Nearly everyone can play a part if given training – that’s an empowering message.
Fostering Inclusive Upskilling Programs. To successfully upskill non-tech employees into tech-related roles, companies should implement structured programs:
Skills Identification: First, identify what new roles or skill combinations are actually needed (e.g., do we need more data-driven decision makers in department X?). Also identify employees with aptitude and interest. Not everyone will want to dive into analytics, but many are eager if asked. Sometimes people with an analytical mindset are hiding in plain sight in non-technical departments.
Targeted Training Curriculum: Develop or source training that is applied and relevant to the business. For example, rather than a generic programming course, a bank created a “data transliterator” program specifically to teach bank product managers how to use machine learning on loan data. The curriculum included just enough Python to run loan models, plus domain-specific cases. The training should mix technical skills with how to apply them in context.
Mentoring and Communities: Pair upskillers with mentors (perhaps a data scientist buddies with the marketing person learning analytics). Create communities of practice – e.g., monthly meetups of all “citizen data scientists” in the company to share tips and troubleshoot. This peer support helps non-tech folks feel they are not alone and can accelerate learning.
Recognition and Career Paths: Acknowledge the achievements of those who transition roles. Perhaps create new job titles or levels that reflect their hybrid skill (like “Senior Marketing Analyst” instead of just “Marketing Associate”). Reward managers who develop internal talent in this way. Success stories should be celebrated – e.g., showcase that last quarter, 50 employees earned an “AI Translator” certificate and have now filled critical roles. This encourages others to step forward.
Inclusive Culture: It’s important to communicate a mindset that learning is valued over credentials. Companies like IBM have championed “new collar” jobs – roles where skills matter more than degrees. IBM actually removed college degree requirements from many job postings and instead looks for skill evidence (they also invested in training for current employees to earn digital badges as proof of skill) 11. By doing so, they tapped a wider talent pool and signaled to current staff that advancement is about capability, not just formal pedigree.
In summary, upskilling the “unlikely” talent is a win-win. The individual employee gets a career upgrade and the chance to stay relevant in the AI age. The organization expands its effective talent pool and alleviates the tech talent shortage internally. It also usually costs less to retrain an existing employee than to hire a new one (consider recruitment costs, onboarding time, etc.). Plus, internal hires have higher success rates since they fit the company culture and know the business. A BCG study on future-ready companies observed that those successful in AI “prepare workers for change – individually, at the team level, and organization-wide”, including unlocking employees’ willingness to learn via incentives and leadership support. This indicates that cultural and organizational factors, not just the training content, are crucial. Leadership should champion stories of a diverse set of employees moving into AI-related roles – this helps break stereotypes about who “can do tech.”
It’s often said that talent is evenly distributed, but opportunity is not. By democratizing AI skills across the workforce, companies ensure opportunity is distributed – and they might find some of their best AI innovators are people who were nowhere near the IT department to begin with.
Higher Education’s Slow Clock-Speed vs. Industry Needs. Traditional education pathways (universities, colleges) have long been the pipeline for skilled talent. But when it comes to the fast-evolving world of AI and digital skills, academia is struggling to keep pace. University curricula often take years to update – new courses require approvals, faculty training, etc. By the time a new AI course is introduced, the technology may have leaped ahead again. For example, very few university programs had content on generative AI before 2023, yet tools like ChatGPT became globally used practically overnight. Many graduates in 2025 will have never formally learned about large language models during their studies, despite these models now being ubiquitous in workplaces. This lag is creating an education gap: industries are charging forward with AI adoption, but new graduates (and certainly those who graduated a decade ago) aren’t adequately prepared with relevant AI skills, unless they learned on their own.
A recent Forbes piece highlighted this readiness gap, noting that while 90% of college students surveyed use generative AI regularly for classwork, most universities lack formal AI coursework or even acceptable-use policies 12. In that survey, 77% of students wanted their schools to offer AI skills courses, but few institutions have integrated such content 12. Instead, over half of colleges were actually restricting AI tool use without offering alternatives 12. This indicates a reactive approach focused on academic integrity (stopping cheating with AI) rather than proactively teaching how to harness AI. Students are effectively “on their own” to learn these tools, which they do because they need them, but academia isn’t guiding or accrediting that learning. “Students are innovating, while institutions are hesitating,” as the Forbes article put it. The result is graduates entering the workforce with inconsistent AI literacy – some may be power users of ChatGPT (through self-learning) while others avoided it due to unclear university stances, and in either case, few have formal training in its responsible and effective use.
Another challenge: University programs (especially undergrad) tend to be broad and theory-focused. That has advantages for foundational knowledge, but employers today often prioritize specific applied skills. A computer science degree covers algorithms and theory but might not teach a cutting-edge framework that a company adopted last month. Meanwhile, a non-CS graduate might self-teach a needed skill via online courses. This is one reason we’re seeing a shift from credentials to demonstrable skills in hiring. Companies like Google, IBM, Accenture, and many others have publicly dropped the requirement of a 4-year college degree for many jobs 11. A 2024 survey found one-third of companies no longer require a college degree for any of their salaried positions – they simply list the skills needed 11. And nearly half of companies plan to eliminate degree requirements for more roles in the coming years. The reason is clear: employers have found that a degree is not always the best proxy for ability, and it artificially limits the candidate pool. They’d rather assess whether you can actually do the job tasks.
What does this mean for the education system? It implies that universities risk becoming less relevant in fields where currency of skill is crucial. If a student emerges with a degree but outdated skills, employers might bypass them for someone with a portfolio of real projects or up-to-date certifications. Already, we’ve seen the rise of nano-degrees and micro-credentials. Platforms like Coursera, Udacity, and edX offer targeted certifications in AI, data science, UX design, etc., often developed in partnership with industry. These can be completed in months and updated frequently. Many learners (including mid-career professionals) are stacking such credentials as an alternative or supplement to traditional degrees. For instance, a marketing professional might earn an online certificate in “AI in Marketing” to showcase that contemporary skill, even if their MBA curriculum didn’t cover it.
The Bootcamp Phenomenon and Its Limitations. In the past decade, coding bootcamps and similar accelerated programs sprung up to address tech talent shortages. Bootcamps typically promise to turn novices into job-ready programmers or data analysts in, say, 12–16 weeks. They tend to focus on practical skills and specific tools, which is great for hitting the ground running. However, bootcamps face their own challenges in keeping up with the breakneck change of the tech landscape. One issue is curriculum relevance. The tech industry evolves so fast that a bootcamp must constantly update its curriculum to remain current. If a bootcamp is even a couple of years old, there’s a risk it’s teaching last year’s hottest JavaScript framework which this year might be replaced by a new one. A commentary titled “Why Coding Bootcamps are Ruining the Software Industry” argues that some bootcamps “don’t regularly update their curriculum [and] run the risk of teaching outdated technologies”, leaving graduates with skills that “are no longer in high demand” 4. Such grads then have to do “extensive self-study to catch up with current technologies” – essentially undermining the point of the bootcamp.
Another issue is narrow focus vs. deep understanding. Bootcamps, by design, compress learning into a short time. They often prioritize practical coding skills over theoretical foundation. This gets students building apps quickly, but they might lack deeper problem-solving ability or understanding of “why” things are done a certain way. The same critique mentioned bootcamps “neglect crucial topics such as data structures, algorithms, computer architecture”, resulting in a “weak foundation” that can limit graduates’ ability to handle complex problems. In a fast-changing field, having solid fundamentals is what allows you to adapt to new tech. If you only learned tool X without understanding the general principles, when tool X becomes obsolete you’re stuck. Bootcamp grads might face that if they aren’t continuously learning post-bootcamp (which the good ones do, but it’s not guaranteed).
Bootcamps vs. the need for lifelong learning: Bootcamps can quickly inject needed skills into the workforce, but they’re not a one-and-done solution either. The reality is no single program, whether a 4-year degree or a 4-month bootcamp, can future-proof someone’s skills for a decades-long career. The name “bootcamp” itself implies intensity but short duration. After it, those graduates will still need continuous learning, especially because they might not have been taught how to self-learn new fundamentals. Some bootcamps are recognizing this and offering extended mentorship or alumni upskilling options. But many graduates report that once they land a job, the support drops off. One analysis pointed out bootcamps often focus heavily on job placement right after graduation, but there’s “lack of long-term career support”, which can leave grads feeling unsupported when they need to evolve beyond that first role 4.
Portfolios over Credentials: In tech fields especially, there’s an increasing emphasis on what you can demonstrate. For developers, a GitHub portfolio showcasing projects may impress recruiters more than a transcript. For data analysts, a Kaggle competition entry or a personal data visualization project could carry weight. This shift puts pressure on traditional education, which confers a diploma but doesn’t always produce tangible proof of work. Some universities have started integrating project-based learning and industry co-ops so that students graduate with portfolios. However, not all have caught on, and many still rely on grades and exams which employers may find less meaningful.
In-House Corporate Academies: Sensing the shortcomings of external education, many companies are building internal academies to train talent in exactly the skills they need, on an ongoing basis. These corporate “universities” or academies can be far more agile in updating curriculum since they aren’t bound by academic bureaucracy. Deloitte’s Human Capital blog describes “flipping the tassel on L&D” by creating corporate academies focused on the company’s strategic skill needs. Such academies often have a few key features: (a) Custom Content: Courses targeted to the company’s context (e.g., a bank’s academy will teach AI in finance, with use cases on fraud detection, not generic AI examples). (b) On-the-Job Integration: These programs often combine learning with doing – employees might rotate through solving real business problems as part of “capstone” projects. (c) Agility: Because it’s in-house, if a new tool or regulation comes out, the academy can quickly spin up a module about it next quarter, rather than waiting years. Deloitte found that “unlike generalized ‘any-company’ curricula, the academy model is nimble and agile for change”. This means if AI introduces a brand-new method in marketing, a corporate academy can incorporate that almost in real time for its marketing teams.
In-house academies also foster a learning culture because they visibly show the company investing in people. For example, tech companies like Amazon, Google, and IBM have extensive internal training programs that issue micro-credentials or “badges” upon completion. IBM has even made many of its internal courses available publicly as badges, effectively blurring the line between corporate and general education. These micro-credentials often carry more specificity than a degree – e.g., “IBM Data Science Professional Certificate” tells an employer that a candidate has been vetted on certain real-world skills (and interestingly, programs like that have enrollment from both IBM employees and external learners).
Adaptive, AI-Driven Learning Models: One exciting development is using AI itself to improve learning delivery – essentially, AI for education/training. AI tutors can personalize learning content to each student, adapt difficulty, provide instant feedback, and even detect when learners are disengaged or confused (perhaps via eye-tracking or analyzing responses). We are seeing the emergence of intelligent learning platforms that adjust in real-time: if a learner breezes through a particular lesson, it may skip ahead, whereas if they struggle, it offers hints or supplemental material. This is a game changer for upskilling at scale, because it means thousands of employees could each get a tailored experience, which was impossible with one-size-fits-all classes.
For instance, imagine an AI-driven learning app at a company like planAI (to make it concrete with our context). An employee logs in; the AI has analyzed their usage of enterprise tools and finds they could benefit from improving their Excel macro skills (perhaps their workflow data shows they do a lot of manual repetitive tasks). The app proactively suggests a short module on Excel macros. As the employee works through it, the AI notices they made a certain error on a practice exercise, and instantly offers a correction and another problem to practice the same concept. Later, the app observes that the employee successfully applied a macro in a real spreadsheet (it can tell by integration with Office 365, say) and congratulates them, issuing a badge for “Workflow Automation – Level 1”. This kind of real-time, contextual learning closes the loop between learning and application. It’s like having a personal coach for every employee, made feasible by AI. Companies are highly interested in this because it makes learning more efficient and effective, and it generates lots of data to prove ROI (every interaction can be logged to see progress).
Why Universities and Bootcamps Alone Aren’t Enough: In summary, the reason we can’t rely solely on traditional education or quick-fix bootcamps in this new era is that learning now must be a continuous, lifelong endeavor – and one intimately tied to the evolving demands of workplaces. A four-year degree is a great foundation, but it’s just that: a foundation. Bootcamps can fill immediate needs but often lack longevity. The future points toward modular, continuous education. Degrees might gradually unbundle into smaller certificates earned throughout one’s career. Already, companies like Google offer career certificates in IT support, data analytics, project management, etc., which many employers (including Google itself) recognize in lieu of a degree for certain roles.
Many of today’s educators agree on the need for change. As one professor wrote, “universities must evolve or risk irrelevance; they need to integrate emerging tech faster, partner with industry more, and focus on skills, not just knowledge”. Some forward-thinking schools are launching things like one-year master’s programs in AI for professionals, or including industry certification prep in their courses. But academia moves slowly by nature, and not all institutions will keep up. Bootcamps, while faster, face credibility and consistency issues – some employers are skeptical of the depth of bootcamp grads’ skills 4, and outcomes vary widely by program quality.
The Path Forward – Blending Learning Models: The likely scenario for the future of upskilling is a hybrid ecosystem: people will learn from a mix of sources over their careers. Early foundational learning may still come from universities for many, but they will quickly augment with on-the-job learning, online courses, bootcamps for specific pivots, and internal company training. The credential that matters will be what you can do and have demonstrated, not just the school you went to. We might see more portfolio-based hiring where applicants submit a portfolio of projects and certifications. Even during employment, promotion decisions might start considering an internal skills portfolio – what new competencies has an employee mastered lately?
For companies like planAI (an AI-powered upskilling platform), this is an opportunity to be at the forefront. PlanAI can position itself as the solution that ties everything together: it can integrate with formal courses, offer its own micro-courses, use AI to personalize and keep content current, and work directly in the flow of an employee’s work tools. Essentially, planAI could be the continuous training layer that sits above and alongside traditional education. Businesses and even universities could partner with such platforms to ensure learners are always current. If universities can’t update a course in time, maybe they supplement with modules from planAI that cover the very latest developments. If bootcamp grads join a company, maybe they go through a planAI “bridge” program to fill any gaps.
Adapting to the New Era of Upskilling: The bottom line is that to address the education gap, organizations should not rely on external education credentials as proxies anymore but actively facilitate ongoing education themselves. When hiring, look at skill proofs and be prepared to top-up training for new hires. When developing employees, provide modern tools and platforms (like AI-driven learning) that allow them to keep learning continuously without leaving their job. Encourage a mindset that one must “learn how to learn” because the shelf-life of any given skill is short. In this way, whether someone came via a college, a bootcamp, or is self-taught becomes less important than their ability to continuously acquire new skills.
As we head into the future, a likely scenario is people earning “lifelong learning transcripts” – a collection of micro-credentials, projects, and experiences accumulated over time. In a way, companies might become the new universities, constantly developing talent from within and even credentialing them (as some have started doing). This is a radical shift from the days when one’s learning largely ended at graduation. But in the context of AI and rapid change, it’s a necessary shift to ensure the workforce is always up to date.
Across these four themes – the emergence of agentic AI teammates, the rapid-fire evolution of technology outpacing stagnant L&D, the untapped potential of non-traditional talent, and the inadequacies of our current education system – one message resounds: businesses must act now to transform how their people learn and grow, or risk being left behind. The convergence of advanced AI and shifting skill demands is not a temporary blip; it marks a new era of work. In this era, success will favor those organizations that are adaptive, learning-focused, and inclusive in developing talent.
The implications are profound but also exciting. We stand at a juncture where it’s possible to re-imagine workforce development from the ground up. Instead of viewing AI as a threat to jobs, leading companies are viewing AI as a catalyst for creating richer jobs – ones where employees are empowered by AI agents and freed from drudgery. However, reaching that vision means giving employees the tools and training to fill new roles and execute new kinds of decisions. It won’t happen by accident or with business-as-usual training. It requires strategic initiative from the top. Recall the BCG finding: “The vast majority of C-suite executives rank AI among their top priorities, yet only 6% have started upskilling in a meaningful way” 7. That gap between priority and action must close. Leadership needs to champion upskilling with the same vigor they champion quarterly results – because the two are now intertwined.
What concretely should organizations do? Here’s a high-level action plan based on the insights of this paper:
Develop an AI-Era Learning Strategy: Just as companies have a business strategy, they need a learning strategy aligned to the age of AI. This means identifying key skills (technical and soft) that will be critical in the next 5 years, assessing the current workforce’s proficiency, and mapping out how to close the gaps. It should include plans for technical literacy (so every employee understands core AI concepts relevant to them) and higher-order skills like judgment, AI orchestration, and ethical oversight. Moreover, the strategy should integrate diverse learning methods – formal courses for foundational knowledge, experiential learning (projects, hackathons), and on-demand digital learning for continuous top-ups.
Invest in Modern Learning Infrastructure: Upgrade learning platforms and tools to enable microlearning, personalization, and learning in the flow of work. This could mean adopting AI-powered learning Experience Platforms (LXPs) that recommend content and allow easy content creation. It also means leveraging AI tutors or assistants – for example, integrating a Q&A chatbot into the company knowledge base so employees can ask it questions anytime (turning every question into a learning moment). Don’t be afraid to replace or supplement old LMS content with more dynamic resources, including partnerships with online education providers or using open-source materials. A relatively modest investment in these tools can yield outsized returns in engagement.
Create a Culture of Continuous Upskilling: Tools alone won’t do it; culture is king. Leaders and managers must send the signal that learning is part of work, not something extraneous. For instance, encourage employees to spend a certain percent of their time each week on learning (Google famously had a 20% time for innovation; companies today might implement a “10% learning time” policy). Recognize and reward learning achievements – not just big degrees, but also micro-credential completions or successful application of a new skill on the job. Make it visible: a CEO could regularly talk about new things they themselves are learning, to role-model intellectual curiosity. As one Deloitte exec said, “We start with a positive view… We want people to be drawn to [learning] because they are optimistic about growth potential, versus ‘if I don’t do this, I become obsolete’” 8.. That optimistic framing – that learning is an opportunity, not a burden – is crucial.
Leverage and Reskill Internal Talent: Rather than defaulting to hiring for every new skill need, look inward. Launch internal upskilling cohorts for roles like “AI translator” or “automation lead.” Identify employees with high learning agility and give them pathways to move into tech-driven roles. Not only will this fill roles faster, it also boosts morale and loyalty. For example, if data science talent is scarce, maybe your finance analysts can be upskilled into junior data scientists focused on financial models. Set targets: e.g., “Within 2 years, 30% of our data analytics roles will be filled by retrained internal staff from non-tech departments.” And ensure managers support their people in making these transitions (adjust workloads to allow training, etc.). As the EY study indicated, few companies even have a skills taxonomy or inventory in place (only 19% had one) 9 – start by cataloging what skills you have and which adjacent ones can be developed. If 81% face tech skills shortages 9, the smart move is to turn that 81% challenge into an opportunity by mobilizing the 100% of your employee base as potential learners.
Partner with Education, but Don’t Depend on It: Companies can form partnerships with universities or online education firms to co-develop curricula that are more up-to-date and practical. For instance, work with a local university to create a certificate program for your employees, or sponsor a course project that uses real company data. Some companies have even co-created degrees (like an “Industry MEng” where students alternate between work and campus). Such partnerships can influence academia to be more responsive. However, don’t rely on higher ed alone to supply all needed skills. Be prepared to create internal content or use Plan B when external programs are behind. An example approach is IBM’s model of hiring for aptitude and then putting new hires through an intensive IBM training bootcamp to get them job-ready – effectively finishing what college didn’t.
Embrace Skills-Based Hiring and Promotion: Update your HR policies to focus on competencies. Remove unnecessary degree requirements (as many peers are doing) 11 and instead list the skills or demonstrated experience needed. This will allow you to tap into talent from non-traditional backgrounds who have upskilled themselves via alternative routes. Internally, use skill mastery as a criterion for promotions – e.g., someone who has mastered new digital tools or completed cross-functional upskilling could be promoted faster, showing the organization truly values skill growth.
Utilize Platforms like planAI: Leverage specialized platforms built for this new upskilling era. PlanAI, for instance, is positioned to provide AI-driven, personalized learning journeys. As an AI-powered upskilling platform, planAI can assess each employee’s skill profile and learning style, then deliver bite-sized training in the flow of work. It can also connect employees with AI mentors or intelligent chat agents that answer questions on demand. Essentially, planAI and similar platforms can operationalize much of what we discussed: continuous microlearning, tracking of skills, nudging learners, and measuring progress. By adopting such a platform enterprise-wide, a company gains a unified system to “orchestrate” learning (much like agentic AI orchestrates tasks). It also provides leadership with analytics – where are we strong, where are the gaps, who might be ready for a new role, etc.
The cost of inaction is high. If a business fails to adapt its upskilling approach, it will likely face increasing skill gaps, lower productivity, inability to fully utilize new technologies, and difficulty attracting/retaining talent. The workforce could become demoralized or fearful as they see their skills become outdated with no support to refresh them. In worst cases, companies might resort to rounds of layoffs of “legacy-skilled” workers and frantic hiring of new skills – a very expensive and socially disruptive approach, akin to constantly swapping parts of an airplane mid-flight instead of upgrading and training the pilot and crew.
Conversely, the benefit of proactive upskilling is enormous. Companies that cultivate a future-ready workforce will be more innovative (because employees have the latest knowledge to spark ideas), more agile (because people can shift into new roles as needed), and more resilient (because they can weather technological disruptions without massive external hiring or layoffs). They’ll also likely enjoy better employee engagement – people stick around where they feel they are growing. We already see some evidence: in LinkedIn’s Workplace Learning Report surveys, employees at companies with high internal mobility (i.e., lots of internal moves/upskilling) tend to stay almost 2x longer than those at companies with low internal mobility.
Perhaps most importantly, embracing continuous learning at all levels fosters a mindset of growth and possibility. If every challenge (like a new AI tool or a new market trend) is met with “Great, what can we learn to leverage this?” rather than fear or inertia, that company will surge ahead of competitors still playing catch-up. As one BCG report on AI noted, “the age of incrementalism is over… those that thrive will rethink not just tools and workflows, but value, control, and differentiation from the ground up” 6. Rethinking workforce development is a core part of that. Incremental tweaks to L&D won’t suffice; a leap is needed.
In this journey, planAI stands as a valuable partner. The platform is built around many of the principles outlined: it uses AI to personalize content, supports microlearning, integrates with work systems, and likely offers robust analytics on skill development. By implementing planAI, a company signals that it’s serious about modernizing learning. Imagine a future where, through planAI, each employee has a dynamic “skill passport” that grows as they do – showing all the badges, courses, and skills they’ve acquired – and planAI constantly suggests the next steps and connects them with AI colleagues (bots) who can help practice those skills in real scenarios. That is very much aligned with the new upskilling era this white paper has described.
A Vision for the Future: Five years from now, the enterprises that heed this call to action will have transformed their workforces. In those organizations, it will be commonplace for an operations specialist to talk about the new analytics model they are learning to use, or for a customer service rep to proudly mention how they trained the AI chatbot to handle a new issue type. Employees will rotate through internal academies that feel more like immersive bootcamps, but with ongoing support. Performance reviews might include “learning goals achieved” as a key metric. On any given day, employees might spend a few minutes quizzing themselves on something new with their AI learning app, and a few minutes advising their personal AI assistant on how to fine-tune a task – a seamless blend of working and learning.
Ultimately, the companies and leaders who champion continuous upskilling will not only secure business success but also perform a profoundly human responsibility: they will turn the scary narrative of “AI taking jobs” into an uplifting narrative of “AI changing jobs for the better, and we invested in our people so they could change with them.” This kind of leadership is what’s needed now. The call to action is clear – the future of work belongs to the constantly curious, the continuously learning, and those who enable it. Businesses must seize this moment to re-skill, re-shape and revolutionize their approach to talent, with agentic AI as the catalyst and lifelong learning as the linchpin. Those that do will thrive in the new era; those that don’t may find themselves replaced – not by AI, but by more forward-thinking competitors.
