Redesigning Career Paths: How AI Uncovers Hidden Talent in Your Organization

Uncovering Hidden Potential: AI’s Role in Redesigning Career Paths

In today’s fast-changing business world, many organizations are sitting on a goldmine of untapped talent without even realizing it. The skills and potential of their employees often extend far beyond what traditional resumes and job descriptions capture. In fact, research reveals that while an average employee might list around 11 skills on their profile, an AI assessment can uncover roughly 34 skills, essentially tripling the recognized talents and opening up career pathways previously unimagined. This stark underestimation of employee capabilities means that hidden talent is everywhere in your organization, waiting to be discovered and developed.

Leveraging artificial intelligence (AI) in talent management is emerging as a game-changer to address this gap. AI tools can sift through employee data to reveal skills, experiences, and aptitudes that managers might overlook, enabling leaders to redesign career paths and align hidden talent with organizational needs. For HR professionals, Chief Information Security Officers (CISOs), business owners, and enterprise leaders across all industries, the implications are profound. By using AI to shine a light on employees’ hidden strengths, companies can not only fill skill gaps from within but also boost morale, retention, and innovation. Before diving into how AI uncovers hidden talent, let’s outline what will be covered in this comprehensive guide.

Modern organizations face a talent landscape that is continually evolving. On one hand, rapid technological advancement and economic shifts are transforming the jobs market; on the other, companies struggle with skills shortages and high turnover. By 2025, for example, 85 million jobs may be displaced by machines, but 97 million new roles may emerge, requiring skills that many workforces do not yet have. Moreover, about 50% of all employees will need reskilling in the next few years to keep up with changing job requirements. These realities put pressure on businesses to adapt quickly or risk falling behind.

Traditionally, when faced with new skill needs, organizations tended to look outward, hiring new talent or consultants. But external hiring alone is not a sustainable solution. Recruiting is time-consuming, expensive, and often unable to keep pace with the speed of change. This is why forward-thinking companies are increasingly turning inward. They are recognizing that within their own ranks lies a reservoir of untapped skills and capabilities that could be redeployed or developed to meet emerging challenges. In many cases, the best candidate for a new role is someone already on payroll, if only you know where to look.

The challenge is that identifying internal talent is not always straightforward. Large enterprises may have thousands of employees across departments and geographies, making it hard to know who has which skills beyond their current job title. Even in smaller companies, biases and outdated perceptions can cause managers to overlook people’s potential. Employees themselves might not advertise skills they acquired in past roles or hobbies, and managers might pigeonhole individuals based on their current position. This is where AI Training and intelligent analytics enter the scene as powerful allies — essentially high-powered talent microscopes that enable a more detailed and dynamic view of your workforce’s capabilities.

Leaders across the organization, from HR to IT to Security (yes, even CISOs concerned with skill gaps in security teams), need to appreciate this shift. The global talent shortage and the breakneck speed of innovation require a new approach. To stay competitive, companies must become talent innovators, cultivating from within. AI technology, with its ability to process vast amounts of data and detect patterns, is uniquely suited to help navigate this new landscape. Before exploring the tech, let’s clearly define what we mean by “hidden talent” and why it often goes unnoticed.

Hidden talent refers to the skills, abilities, or potential in employees that are not immediately obvious or are undervalued in their current role. These could be competencies the employee hasn’t had a chance to demonstrate, secondary skills learned through past experiences, or even aptitudes that no one thought to look for. For example, an administrative assistant might also be a whiz at coding automation scripts, or a salesperson might have a flair for graphic design, talents that fall outside their job description and thus remain “hidden” to the organization.

Why do these talents stay hidden? One reason is the limits of traditional HR data. Résumés, job titles, and annual performance reviews provide only a snapshot of what a person has done, not a full picture of all they can do. Many employees possess “adjacent skills” or untapped potential that isn’t captured on paper. There’s also a psychological factor: people often underestimate their own skills. In the pilot study mentioned earlier, workers could list only 11 skills on average, whereas AI discovered they actually had thrice that number. This inherent bias, where individuals don’t recognize their full skill set, means valuable abilities may never come to light unless actively sought.

Organizational culture and bias can play a role too. If managers have formed fixed opinions about employees (“Tom is in accounting, so he wouldn’t have marketing skills”), they may not see beyond those labels. Similarly, employees in junior or support roles might be overlooked for leadership potential due to unconscious bias about qualifications or background. Everyone has talent, as one HR expert put it, whether they are a front-desk clerk or a fast-track executive, it’s up to leadership to give them opportunities to use it. In many companies, however, talent identification focuses on external candidates or those already on obvious leadership tracks, while the broader employee base is underutilized.

The cost of overlooking hidden talent is high: it can lead to disengagement (talented staff feel stuck and leave) and missed opportunities (roles stay unfilled while suitable internal candidates go unrealized). Recognizing this, organizations are now striving to surface these deep cuts of talent, much like discovering B-sides of a record that turn out to be hits. The next section will explore how AI technologies make it possible to find these “B-side” skills and bring them into the spotlight.

Advanced AI algorithms and talent intelligence platforms act as ultra-observant talent scouts within your organization. They analyze a wide range of employee data to uncover skills and patterns that humans might miss. This process is often called skills mapping, creating a detailed inventory of skills across the workforce. According to the World Economic Forum, an astonishing 66% of organizations are now leveraging some form of skills mapping to ensure their teams have the capabilities needed for today and tomorrow. AI supercharges this effort by handling the complexity and volume of data involved.

How does AI actually identify hidden skills? It starts by aggregating data from numerous sources: résumés/CVs, work histories, performance reviews, completed projects, certifications, and even informal contributions. Modern AI can parse text and context from all these records. For example, using natural language processing, an AI system might read through project documents or emails to spot that a software developer also frequently contributed design ideas, indicating a skill in UX design. Likewise, an AI can analyze learning records to see if an HR coordinator completed advanced Excel courses, a sign of analytical skills that could be applied elsewhere. By interpreting such career trajectories and real-time signals, AI builds a rich skills profile for each employee.

Crucially, AI doesn’t stop at what’s formally recorded. It can infer skills that are adjacent or emerging. For instance, if an employee has mastered Python programming, an AI might infer they likely understand general software development lifecycle concepts. Some AI platforms use large skills taxonomies and even scan external labor market data to infer what skills a job role typically requires. The result is a more complete picture of each person’s capabilities, including those talents the individual or their manager may not have realized were there.

This AI-driven analysis also highlights employees’ potential for growth. It can match patterns in a person’s profile with those of successful performers in different areas, essentially predicting where someone might excel if given the chance. In other words, AI can say, “Employee X has never done data analytics formally, but their pattern of skills and learning suggests they could pick it up quickly and thrive in a data-heavy role.” In fact, with today’s AI-powered skills intelligence, it’s easier than ever to make such predictions about employee potential.

An illustrative example comes from a collaboration between Unilever, Walmart, and an AI startup as part of a World Economic Forum pilot. They used AI to map workers’ skills and match them to emerging roles, aiming to close skill gaps. The AI found that people often had many more skills than they acknowledged, and in some cases, an employee would need only a few additional skills to transition into a completely different career path. In one case, an IT manager at Walmart was found to have a 50% skills overlap with the requirements for a product manager role, something that might not have been obvious without AI analysis. This demonstrates AI’s power to reveal non-linear career moves and candidates for internal roles that hiring managers might not consider on their own.

In essence, AI serves as a talent x-ray machine, peering beneath job titles to see the skills bones and muscles in the organization’s body. It quantifies and visualizes the skill landscape: what skills exist, where gaps are, and who could be developed for new challenges. Over two-thirds of business leaders say that AI is enabling them to get ahead of the competition, and talent intelligence is a key area where that advantage manifests. With a clear map of hidden talent, the next step is to channel it effectively, which is where AI-driven career pathing comes into play.

Identifying talent is only half the story; the real impact comes from redeploying and developing that talent. AI-powered career pathing is about charting dynamic, personalized growth routes for employees, helping them move into roles where they can flourish. The era of fixed, linear career trajectories has faded, organizations are embracing more fluid and dynamic career paths enabled by AI recommendations. By analyzing employee skills alongside market trends and organizational needs, AI can propose personalized career moves that benefit both the individual and the company.

For instance, imagine a software engineer who aspires to become a product manager. Traditionally, this might require the engineer to leave and get an MBA or hope for a rare opportunity. AI-driven career pathing can accelerate this process. It could analyze the engineer’s current skill set (say, technical skills, some project leadership experience), compare it with the target role’s requirements (user empathy, communication, domain knowledge), and then design a tailored development plan. The AI might suggest specific steps: perhaps taking on a cross-functional project, enrolling in a product management course, and pairing the engineer with a mentor from the product team. All these recommendations would be data-informed, drawn from what has helped others successfully make similar transitions.

This kind of personalized career development plan aligns individual ambition with company strategy. The engineer gains new skills and a clear path forward, increasing their engagement and loyalty. Meanwhile, the organization cultivates a product manager who already understands the company’s technology and culture. It’s a win–win scenario. At scale, offering such individualized growth paths fosters a culture of continuous learning and internal mobility. Employees see that they don’t need to leave the company to advance their careers; they can reinvent themselves from within.

AI makes these internal transitions more feasible by identifying “bridge skills” and quick upskilling opportunities. The WEF pilot mentioned earlier found it could take as little as six months to reskill an employee for a role in a completely different function, if the right training is provided. This speed is achievable because AI pinpoints the small set of new skills required to make the jump. It essentially says, “You’re 70% of the way there already; here are the exact courses or experiences to get you to 100%.” Such guidance might reveal, for example, that a marketer could become a data analyst after gaining just a certification in statistics and a project assignment to build those skills, transitions workers might not have identified for themselves, but which are quite attainable.

Enabling internal mobility has concrete benefits for retention and engagement. According to LinkedIn’s data, employees who make an internal move within two years have a much higher chance of staying longer with the company. In fact, one report noted a 75% likelihood of an employee staying if they’ve made a lateral or upward move by the two-year mark, compared to only 56% for those who haven’t. This stark difference (75% vs 56%) underscores how vital career progression opportunities are to keeping talent. When people see a future for themselves within the organization, they are far more likely to remain and contribute. AI-powered talent marketplaces and career pathing tools can facilitate those moves by efficiently matching employees to internal openings or projects where their hidden talents can be put to use.

Real-world success stories are emerging. For example, one Fortune 50 telecom company leveraged an AI talent intelligence platform to boost internal mobility by 25%, simply by better matching employees to new opportunities based on their skills. The AI helped surface hidden internal talent, enabling roles to be filled faster with existing employees. This not only saved recruitment costs but also invigorated the workforce, as people were excited to be recognized and promoted from within. Similarly, global firms like IBM and Unilever have invested in AI-driven internal talent platforms to encourage employees to apply for internal roles or gigs, with notable success in filling roles internally and upskilling staff. These cases demonstrate that AI-powered career pathing isn’t just a theoretical idea, it’s actively transforming how companies manage careers and talent pipelines.

Adopting AI to reveal and develop hidden talent offers a multitude of benefits at both the organizational and individual level. Here are some of the key advantages:

• Better Talent Utilization and Agility: By knowing all the skills your employees have, you can deploy talent much more effectively. Projects get staffed by the right people, not just the available people. AI-driven insights allow for quick talent redeployments when priorities shift, making the organization more agile. In fast-paced environments, this agility, the ability to pivot teams quickly based on skills, is a strategic edge. Organizations that prioritize internal career development also tend to outperform others in adaptability and confidence to meet future skills needs.
  
• Cost Savings and Faster Hiring: Filling a role internally can be significantly faster and cheaper than recruiting externally. The time to productivity is shorter since the employee already knows the company. There are savings on recruitment advertising, agency fees, and onboarding costs. One study noted that internal hires often reduce hiring costs and ramp-up time by leveraging existing company-specific knowledge. In essence, AI that matches internal candidates to openings can act like an always-on internal recruiter, trimming both the time and expense of hiring.
  
• Higher Employee Retention and Engagement: As mentioned, providing career growth inside the company dramatically improves retention rates. Employees who see a path forward are more likely to stay (and stay motivated). Uncovering someone’s hidden talent and giving them an opportunity to use it is a powerful form of recognition. It tells employees that the company values them holistically, not just for their current role. This boosts morale and engagement, as people feel their potential is being noticed and nurtured. A culture of internal mobility also tends to attract talent, candidates hear that “this is a place where you can grow” which is a strong employer brand factor.
  
• Closing Skills Gaps and Future-Proofing: AI-driven skill mapping helps identify gaps in your workforce’s competencies early. You might discover, for example, that you have plenty of certain technical specialists but a shortage of strategic thinkers or emerging tech skills. Once these gaps are known, you can proactively train or rotate employees to fill them. Over time, this builds a more future-ready workforce. Companies that excel at internal talent development are often better prepared for industry changes. For instance, when a new technology arises, they can quickly see which employees have adjacent skills to be reskilled for it, rather than scrambling in the external market.
  
• Improved Diversity and Inclusion: An often overlooked benefit is that AI might highlight talent that was hidden due to bias or lack of exposure. Perhaps some employees from underrepresented groups haven’t had the same high-profile projects, so their abilities were underestimated. A data-driven approach can surface those skills more objectively. Additionally, by reducing over-reliance on linear career progression (which historically favored certain demographics with access to education or mentorship), AI-enabled career pathing can open doors for a wider range of employees. Of course, this benefit only holds if the AI itself is designed and monitored for fairness (more on that shortly). But done right, internal mobility programs can contribute to a more inclusive workplace, where advancement is based on skills and potential rather than just networks or tenure.
  
• Innovation and Competitiveness: When employees are encouraged to apply their diverse skill sets, they often bring fresh perspectives to roles. A scientist with a hidden knack for marketing could bring creative new ideas to a product launch. A salesperson with coding skills might automate a tedious process. These cross-pollinations can spark innovation. In the bigger picture, companies that effectively harness all their internal talent are more competitive. They’re less likely to have critical skill shortages and more likely to have the right people leading new initiatives. It’s telling that in a Deloitte survey, 63% of business leaders viewed AI as very or critically important to business success, with that number expected to rise to 81% within two years. Tapping into internal talent via AI is a prime example of how AI can drive success.
  

Collectively, these benefits make a strong case for using AI in talent management. However, one must also be clear-eyed about the challenges and responsibilities that come with this technology. Unlocking hidden talent with AI is powerful, but it must be pursued thoughtfully. In the next section, we’ll discuss the potential pitfalls, from algorithmic bias to data privacy, and how to navigate them.

While AI offers exciting capabilities, it also introduces challenges and risks that organizations need to manage carefully. HR is a people-centric domain, and decisions about careers can profoundly affect lives. Therefore, ethical considerations when using AI for talent decisions are paramount.

One major concern is bias in AI algorithms. AI systems learn from historical data, and if past hiring or promotion data reflect bias (e.g. favoring certain genders, ethnicities, or backgrounds), the AI can inadvertently perpetuate or even amplify those biases. A cautionary tale is Amazon’s experimental AI recruiting tool, which was found to be downgrading resumes that included the word “women’s” (as in “women’s chess club”), because it had learned from past data dominated by male candidates. Amazon ultimately scrapped the tool when it realized the model was not gender-neutral and was discriminating against female applicants. This example underscores that AI is only as fair as the data and design behind it. If left unchecked, an AI system meant to identify talent might systematically overlook certain groups or erroneously rank people in ways that mirror past prejudices.

To address this, companies must ensure human oversight and intervention in AI-driven talent processes. AI should augment, not replace, human judgment. As a best practice, AI recommendations (whether it’s a hiring shortlist or an internal candidate match) should be reviewed by HR or managers who can consider context and fairness. It’s also important to train AI models on diverse data and regularly audit their outcomes for disparate impact. Some organizations are establishing AI ethics committees or using third-party bias detection tools to monitor their HR algorithms. The message is clear: HR must include human intelligence (HI) alongside artificial intelligence, especially in decisions that affect people’s careers.

Another challenge is employee trust and privacy. For AI to effectively map skills and performance, it may need to analyze sensitive data, like work emails, feedback reports, or learning history. Employees need to trust that this data will be used responsibly and securely. Transparency is key: organizations should communicate what data is being collected and how the AI uses it for career development purposes. Privacy laws (such as GDPR in Europe) also require careful handling of personal data, so any AI deployment in HR must comply with regulations and protect employee confidentiality. CISOs and IT leaders have a role here in ensuring that the AI tools and data storage meet security standards, to prevent breaches of HR data.

There is also the risk of over-reliance on AI or false precision. Just because an AI suggests someone is a great fit for a role doesn’t make it infallible. People are complex, and cultural fit or personal motivation, things an AI might not fully grasp, matter in career moves. If managers start treating AI outputs as gospel, they might make missteps or overlook nuances. A healthy approach is to use AI insights as a starting point for exploration. For example, if AI flags an employee as a potential fit for a data scientist role, the manager could initiate a conversation: “Would you be interested in this path? Let’s assess your aptitude further.” AI can generate hypotheses and options, but human conversation and judgment validate them.

Furthermore, organizations must be mindful of the change management aspect. Introducing AI in talent processes can be disruptive. HR teams need training to understand the tool’s capabilities and limitations. Employees might fear that AI is “spying” on them or will make decisions for them. To mitigate this, frame the AI as an “AI career coach” that empowers employees rather than a surveillance or replacement tool. Invite employees to participate by updating their skill profiles or exploring the AI’s career suggestions, thus giving them agency in the process. Garnering buy-in from all stakeholders, employees, managers, and executives, is crucial for the successful adoption of AI in uncovering talent.

Lastly, there’s the challenge of data quality and integration. AI is only effective if it has rich, accurate data. Many companies struggle with HR information being siloed in different systems (learning management, performance reviews, etc.). Implementing AI might require consolidating data or encouraging employees to input their skills and experiences into a platform. Some initial effort is needed to get a “single source of truth” for the AI to analyze. Companies that have done this report gaining a far more comprehensive view of their workforce; for instance, the telecom firm mentioned earlier achieved 100% complete skills profiles for all employees after deploying an AI platform, eliminating blind spots in workforce planning. But reaching that completeness took executive support and change management to ensure everyone participated in profile-building.

In summary, the introduction of AI into talent management must be handled with care, ethics, and clear policies. When done right, the technology’s benefits far outweigh the risks. The next section provides some guidance on how to get started with implementing AI-driven talent discovery in your organization, balancing innovation with responsibility.

Adopting AI to redesign career paths and uncover hidden talent is not a plug-and-play affair; it requires a strategic approach. Here are some best practices and steps to help enterprise leaders and HR professionals implement these initiatives successfully:

1. Start with a Skills Inventory: Begin by gathering data on the skills of your current workforce. This can be through self-assessments, manager feedback, or mining existing records. Encourage employees to update their profiles or resumes internally. Some companies run “skills census” campaigns. Having this baseline data is essential for any AI tool to generate insights. Remember, garbage in, garbage out, the better your input data, the more useful the AI’s output.
   
2. Choose the Right AI Platform: There are numerous AI-driven talent management tools in the market (some focus on internal mobility, others on recruiting, some on learning). Evaluate them based on your needs, for example, do you need an internal talent marketplace that matches people to projects, or an AI that suggests training for career paths, or a full talent intelligence suite that does skills inference? Look for platforms with proven case studies and strong bias mitigation measures. It might be wise to run a pilot with a small department to see results before scaling up.
   
3. Secure Leadership and Cross-Functional Support: Implementing AI in HR is a strategic project. Get buy-in not just from HR leadership, but also from IT (for integration and data security), and from business unit leaders who will be using the insights to make staffing decisions. Make a clear business case, for instance, show how internal promotions could fill X% of open roles, saving $Y in hiring costs, or how identifying skill gaps now could avert future project delays. When executives understand the ROI, they’re more likely to champion the initiative.
   
4. Promote a Culture of Career Development: Technology alone won’t create internal mobility; you need a culture that encourages employees to grow and managers to support talent movement. Set expectations with managers that developing their team members (even if it means losing them to another department eventually) is part of their role. Encourage employees to take charge of their career paths, perhaps by exploring the AI’s recommendations or by applying to internal openings. Some companies introduce “career days” or internal career fairs where employees can learn about different roles, these pair well with AI tools that suggest roles based on one’s profile.
   
5. Ensure Transparency and Communication: Clearly communicate to employees how the AI will be used. For example, “Our new talent platform will analyze skills and suggest career opportunities. It’s like a personalized career coach available to you.” Address any concerns by explaining that final decisions will be human-driven and that the AI is there to provide additional insight and options. Offer training sessions or FAQs on how to use the new system. The more people understand it, the more they’ll trust and engage with it.
   
6. Monitor, Measure, and Refine: After implementation, keep an eye on key metrics to gauge success. Metrics could include the internal fill rate of positions (did it increase?), employee turnover/retention rates, employee engagement survey scores related to career development, or even utilization of the platform (how many employees are actively using it, how many profiles are completed). Also monitor for any biases, for instance, is the AI suggesting mostly male employees for certain technical roles? If anomalies appear, adjust the algorithms or data inputs. Regular audits and feedback loops will help refine the system. Many organizations treat this as an ongoing journey: the AI model improves over time as it gets more data and as you fine-tune it to align with your values and goals.
   
7. Combine AI with Human Coaching: A best practice is to blend AI suggestions with human mentoring. For example, if AI identifies an employee as a high-potential candidate for a certain path, pair that employee with a mentor in that field to discuss the opportunity. Human mentors can provide insight into nuances that an algorithm might not capture (like the employee’s personal passions or possible challenges in the new role). This hybrid approach ensures that career moves are well-rounded and considerate of the human element.
   

By following these steps, organizations can increase the likelihood of a smooth and impactful implementation of AI in their talent management. The technology can seem complex, but at its core, it’s serving a very human-centric outcome: giving people the chance to shine in the areas they’re truly talented in. When execution is done thoughtfully, the result is a more engaged, skilled, and adaptable workforce.

The way we manage careers and talent is undergoing a quiet revolution. AI is enabling us to see our employees in a richer light, not as static titles and past roles, but as dynamic individuals brimming with skills and possibilities. By redesigning career paths with the help of AI, companies can break free from the old one-size-fits-all ladder and adopt a more personalized lattice of growth opportunities. This shift is especially important at the awareness and educational stage, where leaders recognize the need for change and gather knowledge before taking the leap.

Think of AI as holding up a mirror to your organization, but this mirror reveals not just what’s on the surface, but the hidden strengths in the shadows. It can show you the skills your team didn’t even know they had, and help connect those skills to business needs in real time. It can suggest the unexpected, that a customer support rep could become a data analyst, or a finance associate could thrive in marketing, thereby sparking innovation and efficient talent use in ways a manual review might never achieve.

However, the most successful implementations balance high-tech with high-touch. AI might do the heavy lifting of scanning resumes and crunching skill data, but human judgment remains irreplaceable in making final decisions and in coaching employees through transitions. As one HR leader aptly noted, AI in talent management works best when paired with human insight (often coined as “AI + HI”) to ensure fairness, empathy, and strategic alignment. In practice, this means HR and managers become interpreters and stewards of AI insights, guiding people along the career journeys that the algorithms help illuminate.

For HR professionals, CISOs, business owners, and executives reading this, the message is one of opportunity and responsibility. The opportunity is that your organization likely has more talent within it than you currently utilize, and surfacing it could be a game-changer for performance, innovation, and resilience. The responsibility is to implement these powerful AI tools thoughtfully, with proper governance, transparency, and a focus on employee growth. When done right, AI-driven career pathing and talent discovery create a workplace where employees feel seen and empowered, and where the company thrives from within as much as it does by bringing in new hires.

In conclusion, redesigning career paths with AI is about building a future-ready organization from the inside out. It’s about ensuring that no skill goes undiscovered and no employee’s potential is left untapped. As we embrace these technologies, we move toward a more enlightened era of talent management, one where the phrase “our people are our greatest asset” isn’t just a slogan, but a daily reality backed by data-driven action. The organizations that master this will not only uncover hidden talent; they will cultivate an environment where talent continuously reveals itself, grows, and propels the enterprise to new heights.

FAQ

What is considered hidden talent in the workplace?

Hidden talent refers to skills, abilities, or potential in employees that are not immediately visible in their current role or résumé. These may include past experiences, secondary skills, or aptitudes that remain unused or unnoticed by managers.

How does AI help uncover hidden talent in organizations?

AI analyzes diverse data sources such as work history, project contributions, and learning records using tools like machine learning and natural language processing. This helps identify existing, adjacent, and potential skills that employees or managers might have overlooked.

What are the main benefits of using AI for talent discovery?

Benefits include better talent utilization, improved agility, faster and cheaper hiring, higher retention rates, closing skills gaps, enhanced diversity and inclusion, and driving innovation through cross-functional skill use.

What challenges should companies consider when using AI in talent management?

Key challenges include potential bias in AI algorithms, data privacy concerns, over-reliance on AI outputs, resistance to change, and ensuring the accuracy of skills data. Human oversight and transparent communication are essential to address these issues.

How can organizations successfully implement AI-driven talent initiatives?

Best practices include starting with a skills inventory, choosing the right AI platform, securing leadership support, promoting a career development culture, ensuring transparency, monitoring outcomes, and blending AI insights with human coaching.