AI tools are helping companies surface hidden talent and connect employees to internal roles by analyzing skills, experiences, interests, and career goals. Platforms like Gloat, Fuel50, and Eightfold.ai are being used to power internal talent marketplaces, recommend learning paths, and suggest gigs or roles that match both business needs and personal growth plans. One example—Schneider Electric used Gloat to boost internal mobility and reportedly saved over $15 million by reducing attrition and external hiring. At UCI, Fuel50 helped cut attrition by half, and engagement improved with 74% of users returning to explore opportunities. These tools aren't just matching people to jobs—they're driving retention, lowering costs, and improving employee satisfaction. Challenges do come up—data privacy is a big one since these platforms rely on sensitive employee info. Bias in algorithms is another concern, especially if training data skews towards certain profiles. Change management can also be tough. Managers often resist losing talent to other teams, and success depends on shifting that mindset. For companies looking to get started, it helps to pilot with a clear goal—like increasing internal hires by a set percentage—and involve both HR and business leaders early. Also important: keep transparency front and center so employees trust how their data is being used.
I would point out that AI tools are revolutionizing talent management by turning what was once a reactive process into a proactive strategy. Platforms like Gloat, Fuel50, and Eightfold.ai are identifying skill gaps and mapping out entire ecosystems of opportunity within organizations. For example, Unilever leveraged Gloat to create an internal talent marketplace, resulting in over 60,000 project matches and a 41% increase in employee satisfaction. This isn't just about filling roles; it's about empowering employees to take charge of their career paths while aligning their growth with organizational goals. The measurable outcomes are compelling. Companies using AI for internal mobility have reported retention rate improvements of up to 20% and significant reductions in time-to-fill for critical roles. Implementation challenges like change management and employee privacy concerns are real. My advice is to start small for organizations considering AI adoption. Pilot the technology in a single department or region, measure the impact, and iterate.
AI is becoming a backbone for internal mobility by making skills visible and actionable. One of the biggest shifts I've seen is how platforms like Eightfold.ai and Gloat help companies uncover adjacent skills and match employees to roles they might not have considered — or even known existed. What stands out is the ability to move from generic training plans to skill-specific upskilling tied to internal opportunities. At Edstellar, AI is used to assess workforce capabilities and design learning journeys that directly align with career paths. This has helped organizations improve internal role-fill rates by up to 30% and significantly boost mid-career retention. But there's no shortcut around trust. Employees want to know how their data is being used. Transparency in algorithms, consent-based profiling, and regular audits for bias are non-negotiable. Also, change management is often underestimated — AI adoption in talent mobility works best when HR, L&D, and tech teams move in sync. The technology is powerful, but success hinges on how ethically and thoughtfully it's implemented.
I'm Dan, CEO at AppMakers LA, an app development agency where we leverage AI not just for clients, but also to power internal growth and mobility. We integrated an AI platform similar to Gloat by feeding it anonymized data from project histories, performance reviews, and peer endorsements. This helped us surface underdeveloped skills—like UI/UX capabilities among our junior developers—and enabled smart reassignments or training. In six months, we saw a 15% increase in internal project moves, with team members stepping into roles that truly fit them. Previously, we defaulted to hiring externally when gaps appeared. But with AI recommendations, 30% of last year's staffing needs were filled internally. Our churn rate dropped from 18% to 12%, and internal surveys reported a 20-point rise in perceived career growth. Still, adoption wasn't automatic because folks were wary of algorithmic favoritism. To address this, we made every AI suggestion transparent, showing the rationale based on skills, project performance, and peer ratings. Privacy and ethics were also top priorities. We anonymized all inputs and gave staff control over how their data was used, and added human review on every AI match. My advice for others? Start with one clear use case like surfacing transferable skills between teams. Build a feedback loop so the system evolves and show wins visibly—like in Slack channels where people share career moves or learning wins triggered by the AI. Most of all, frame AI as a co-pilot, not the boss. It's a guide to help people grow, not a judge deciding careers. Bottom line: When implemented thoughtfully, AI-based internal mobility tools especially when paired with human oversight and clear feedback mechanisms don't just save hiring time. They build a culture that acts on growth, not just talks about it. And at AppMakers LA, integrating this culture into our development process (for ourselves and for our clients) has bolstered engagement, retention, and quality all at once.
Name: Ali Yilmaz Title: Co-founder and CEO of Aitherapy | Product Advisor to Startups Location: Las Vegas, NV While we have not implemented platforms like Gloat or Eightfold.ai at Aitherapy, I have advised startups and early growth companies that are exploring AI-driven internal mobility solutions. One approach I have seen work well is using AI to analyze employees' past projects, internal communication, and performance data to uncover skills that are not captured in formal job titles or resumes. This helps match them to new internal opportunities they might not have considered. For example, one company I worked with discovered through AI analysis that a support team member had prior experience in data analysis. That insight led to an internal transfer into a junior product analytics role. This improved engagement, reduced the need for outside hiring, and showed how internal skills mapping can unlock overlooked potential. AI works best here because it can process internal data quickly and at scale. It picks up on patterns that human managers might miss, especially in fast-moving environments where job descriptions are constantly evolving. That said, implementation is not simple. The biggest challenge is change management. If employees do not trust the system or feel it is making decisions without transparency, they are unlikely to engage with it. Ethical concerns are also real. Employees want to know how and why they are being matched to certain roles. If the algorithm is not explainable, it creates more anxiety than opportunity. Privacy is another sticking point. Analyzing internal messages or project history raises concerns, especially in smaller companies where anonymity is hard to protect. My advice to organizations adopting AI in talent management is to keep the process collaborative. Use AI to surface opportunities, not to make final decisions. Always include human oversight and focus on clear communication. The real goal is not just internal movement but trust in the system that supports it.
Diversity, Equity, Inclusion, and Accessibility Strategist at Lekeshia Angelique Consulting
Answered 10 months ago
AI is transforming internal mobility by enabling HR leaders to more effectively identify skills gaps and match employees with growth opportunities within their organizations. As a workforce planning and DEIA professional with hands-on experience guiding clients through AI adoption, I've seen how platforms like Gloat, Fuel50, and Eightfold.ai bring visibility and structure to career paths that were once informal and biased. Tools like these use AI to map employee skills, often inferred from performance data, training history, and work outputs, and match them to internal roles, gigs, or learning paths. Johnson et al. (2022) note that this shift allows for dynamic alignment between workforce capabilities and business needs. In practice, companies like Schneider Electric and Ubisoft have reported measurable success. Schneider achieved faster internal hiring and retention gains using Gloat, while Ubisoft's Eightfold rollout resulted in over 55% employee engagement with the internal career platform. The benefits are clear: better retention, employee satisfaction, and a more agile talent strategy. Fuel50 also reports boosts in engagement when internal mobility becomes more transparent and accessible. Importantly, these platforms can help reduce bias by surfacing candidates based on skills, rather than tenure or who is in the inner circle, thereby supporting DEIA outcomes (Sharma et al., 2022). But AI adoption isn't plug-and-play. Cost, data quality, and change management can be real barriers, especially for smaller firms (Sun & Medaglia, 2019). Ethical concerns around algorithmic bias and data privacy also demand thoughtful safeguards. Ghassemi et al. (2021) caution against over-relying on "explainable AI" and instead advocate for rigorous validation and oversight. Organizations must ensure that transparency, employee consent, and fairness are baked into every implementation step. For HR leaders exploring AI tools, I recommend a phased approach: start small, gain leadership buy-in, thoroughly train users, and establish ethics checks from the outset. AI should support, not replace, human judgment. When done right, AI helps us not only fill roles faster but also build equity-centered career paths where employees don't have to look elsewhere to grow. Sources: Johnson et al. (2022); Shimokawa et al. (2025); Sharma et al. (2022); Sun & Medaglia (2019); Ghassemi et al. (2021).
As an HR Director at a mid-sized tech firm in Austin, I've overseen the rollout of an AI-driven internal mobility platform similar to Eightfold.ai. We used it primarily to identify hidden skills gaps by analyzing employee profiles against evolving role requirements. One standout success was reducing voluntary turnover by 15% within a year, largely because employees found clearer career paths and felt their development needs were acknowledged. Implementation wasn't without hurdles: initial skepticism from managers slowed adoption, and we had to invest heavily in change management workshops to build trust. Privacy concerns also arose—so we were transparent about data use and limited access strictly to HR and individuals themselves. Ethically, we constantly audit algorithms to minimize bias, especially around gender and ethnicity, ensuring fair recommendations. While AI tools aren't a silver bullet, combining them with human judgment has made our talent development more proactive and personalized—ultimately fostering greater employee engagement and retention.
AI is starting to reshape internal mobility by making skills—not titles—the primary currency for growth. In observing how platforms like Eightfold.ai are applied, the most impressive outcomes emerge when AI uncovers talent that might have gone unnoticed. One example involved identifying employees with dormant or adjacent skills who were then matched to reskilling programs and transitioned into high-demand roles, leading to a 20-25% reduction in external hiring for those positions. What stands out is how AI moves beyond keyword matching. It assesses experience, intent, and learning agility to offer career paths that often surprise both the employee and the manager. But the technology doesn't solve everything. Costs aside, the biggest friction tends to be cultural—skepticism from managers, fear of surveillance among employees, and the complexity of change management. There's also a responsibility to handle algorithmic recommendations with care. Bias can creep in if historical data reflects outdated talent norms. Guardrails, regular audits, and transparency are essential to build trust and make internal mobility truly equitable.
Once they're fed internal data, they're very good at matching what either the tool or the company using the tool defines as gaps to look for. It's an incredibly efficient way of at least categorising gap areas, but I wouldn't recommend relying just on the AI output, you need to retain a human element to the process too.
We've long used AI-driven recruitment tools at Summit Search Group, but our application of AI for internal mobility and career pathing is more recent. Even so, we're already seeing its potential to enhance both our internal planning and the guidance we provide to clients. One example involves a client undergoing a digital transformation. We used Eightfold.ai to assess their current workforce capabilities and identify skill gaps against future needs. The platform helped uncover underutilized skills and missing competencies, enabling us to design a targeted upskilling program focused on digital competencies like data literacy, automation readiness, and agile project management. Eightfold's strength lies in how it matches people to opportunities beyond their current title. It incorporates employees' interests, inferred skills, and broader experience to surface internal roles or projects they may not have otherwise considered. This allows employers to create more personalized career paths and retain high-potential talent. Since adopting AI tools, our average time-to-fill has decreased by nearly 35%. While we use several tools, we believe Eightfold has played a key role by helping us identify strong internal candidates faster, especially for mid- and senior-level positions. One client who implemented an AI-powered internal mobility initiative with our help saw voluntary attrition drop by almost 25% within a year, a clear sign of improved employee engagement and retention. The biggest challenge is data quality. These tools rely heavily on comprehensive and unbiased employee data. If profiles are incomplete or outdated, recommendations can misfire or reinforce existing biases. Integration with the existing HRIS can improve data flow, but implementation requires thoughtful change management, especially in companies without internal AI or tech expertise. For organizations exploring AI in talent development, my advice is simple: don't go it alone. Partner with a firm experienced in AI-driven workforce planning to help structure your data, integrate your platforms, and apply best practices for implementation. This collaborative approach not only helps you maximize the value of the platform but also mitigates risks around bias and poor adoption.
I must say that AI in talent management is like having a GPS for workforce development. It helps you navigate skill gaps and chart career paths with precision. For instance Schneider Electric used Eightfold.ai to analyze employee skills and match them to internal opportunities, leading to a 15% increase in internal mobility and a 10% boost in retention rates. These platforms are also invaluable for upskilling. Well, the road to AI adoption isn't without potholes. Costs can be significant, especially for smaller organizations, and change management is a hurdle. Employees may resist new systems, fearing job displacement or data misuse. Ethical considerations, like algorithmic bias, are another concern. I suggest focusing on the ROI. Start by quantifying the cost of turnover and the value of retaining top talent. Then, build a business case for AI as a solution.
AI-powered internal mobility works by reverse-engineering how recruitment algorithms think. At Interactive CV, we've analyzed dozens of ATS platforms to understand exactly how they classify and rank candidates. This insight drives our internal talent matching approach. The breakthrough comes from integrating multiple data sources. We ingest employee CVs, LinkedIn profiles, and internal skills assessments. AI processes this through natural language processing to extract both explicit skills and hidden capabilities that traditional systems miss. Our most successful case involved a consulting firm struggling with project staffing. We built a recommendation engine that analyzes incoming project requirements and matches them against their internal talent pool. The system considers technical skills, industry experience, and employee preferences from internal questionnaires. Results are immediate. We generate personalized CVs in seconds using standardized templates customized for each client. This eliminated manual HR reviews of hundreds of employee profiles for each opportunity. The technical architecture uses continuous learning algorithms. As employees complete projects and update information, the system refines its understanding of their capabilities, improving matching accuracy over time. The biggest challenge isn't technology—it's data quality. Success requires clean, structured data and consistent skills taxonomies across fragmented company systems. This response is based on real-world implementation experience from consulting projects with mid-sized professional services firms. Pedro Marchal, Founder & CEO, Interactive CV, Madrid. AI specialist focused on resume optimization and talent matching technology
AI-tools are used to stop waiting for managers to spot talent and have bias By using an AI agent that manages to scan self-reported skills as well as any recent project contributions, it manages to highlight candidates who are ready for more and could possibly be relevant for openings that they would otherwise not be considered for. For example, an operational hire with an interest for prompt engineering could be considered for prototyping agent flows with the product team. That realization would've have originally been possible by using a regular organizational chart. Instead of using a big platform, it is possible to get these results with clear intent, a short internal skills form that employees fill in themselves and a routing script that highlights possible candidates and shares them with managers. The results are excellent with as high as 40% internal roles being filled with AI-suggested matches. Instead of focusing on extra headcounts and going through resumes, it is just an excellent method to repurpose your existing talents and make use of them in the best place possible and gaining better visibility.
Identifying Skills Gaps and Opportunities with AI: Platforms like Gloat, Fuel50, and Eightfold.ai serve as internal GPS systems for talent. They accurately pinpoint employees' existing skills and identify gaps. At InsurancePanda, when we piloted Eightfold.ai, it uncovered hidden internal talent, such as customer service representatives who were ideal for data analytics roles. Real-World Impact and Examples: One notable case involves an insurance company that implemented Fuel50. Within 18 months, internal mobility increased by almost 60 percent. Employee engagement rose substantially, and retention rates improved nearly 40 percent year over year, resulting in significant cost savings. A banking client used Gloat's platform and reduced internal role fill time from 45 days to less than 20, boosting team energy and employee satisfaction through clear career pathways. Implementation Challenges: Adopting AI solutions comes with significant challenges. Costs are considerable, with expenses for implementation, training, and ongoing support. Change management is another critical hurdle. Employees naturally hesitate to trust an algorithm with their career progression. Clear communication about data usage and early demonstrations of tangible benefits are essential. Ethical Considerations: Algorithmic bias is a serious issue due to historically biased data sets. At InsurancePanda, initial tests revealed biases unintentionally excluding certain employee groups. Immediate action to retrain the model and broaden data sets was crucial. Employee privacy must also be prioritized, with clear governance and transparency about data use built into the process from day one. Practical Strategies for AI Adoption: Start with a pilot program in one department, gather data, demonstrate clear successes, then scale up. At InsurancePanda, our strategy is "pilot, measure, scale." Clear and frequent communication is essential. Employees should be engaged early and continually updated, avoiding surprises. Focus on Measurable Outcomes: The value of AI-driven internal mobility solutions lies in measurable outcomes. Trackable improvements such as increased retention, reduced hiring costs, and better employee engagement are critical benchmarks. Technology alone is insufficient. Demand transparency, prioritize tangible results, and thoughtfully manage implementation. AI, used strategically, becomes a powerful driver of organizational advantage.
In my experience working with workforce analytics at a mid-sized tech company, AI tools like Eightfold.ai have transformed how we identify skills gaps and match employees to internal roles. By analyzing employee skills, performance data, and career aspirations, the platform suggests personalized development paths and job openings, which increased our internal mobility rate by 20 percent within a year. This not only boosted retention but also employee satisfaction, as people felt their growth was actively supported. Implementation challenges included the upfront cost and initial resistance, which we addressed through transparent communication and training sessions focused on privacy and bias mitigation. Ethical considerations are crucial—our team worked closely with vendors to audit algorithms for bias and ensure compliance with data protection laws. For organizations considering AI adoption, my advice is to start small, pilot with clear success metrics, and prioritize human oversight to balance AI insights with empathy and fairness. This approach leads to more effective and trusted talent development. Georgi Petrov, Workforce Analytics Lead, AIG Marketer, Dubai.
I've consulted for a mid-sized B2B SaaS company that successfully piloted an AI-powered internal mobility platform (Eightfold.ai) to match employees with growth paths based on their existing skills, hidden capabilities, and learning trajectories. One highlight was when more than 40% of the lateral moves within the first 6 months were filled by non-obvious matches: employees moved into positions they hadn't originally been thinking about but were a good fit for from predictive skill mapping. This didn't just boost internal fill rates, but retention in those positions jumped 22% higher than the year before. But execution wasn't always smooth sailing. There was skepticism (albeit healthy and understandable) regarding algorithmic "judgments," so we complemented the tech launch with manager training and peer review to guarantee human judgment cut through the machine's recommendations. Concerns regarding privacy were alleviated by enabling employees to volunteer for data sharing, and openness regarding how the recommendations were created mattered a lot. The experience taught me that being mindful and open about the use of AI has the potential to be a strong catalyst for talent development and organizational flexibility.
Josiah Roche, Head of Marketing at JRR Marketing, Sydney, Australia. One example comes from a mid-sized fintech that rolled out Eightfold.ai to boost internal mobility and cut attrition. They trained the system using historical employee data like performance reviews, tenure, completed training, and peer feedback. So when they fed open roles into the platform, it started matching people with opportunities based on current skills and potential growth areas instead of just job titles or past roles. Within nine months, internal mobility jumped from 22% to 44%. Recruitment costs dropped by around 30% and average employee tenure went up by close to 10 months. Internal hires ramped up about 35% faster than external ones. So teams hit targets sooner and onboarding time went down. What made it work wasn’t just the tech. It was how they used it. The AI broke roles into skill clusters and matched them against live signals from learning platforms and performance systems. It also flagged skills gaps early. So managers could assign targeted training through their LMS. That shifted development conversations from reactive to proactive. It helped people see clear career paths without having to leave. Still, there were bumps. Some employees pushed back when the AI suggested roles or tracks they hadn’t shown interest in. They were worried about being profiled or nudged by an algorithm they didn’t fully understand. So leadership added opt-in settings and explained how recommendations were generated. They also gave managers guidelines to treat AI suggestions as input, not final calls. Bias showed up early too. The system leaned toward people in client-facing roles where data was more visible. So support functions were underrepresented. To fix that, the company brought in an independent auditor to review the model. They tweaked the weighting so outcomes were fairer across departments. AI didn’t replace their talent strategy. It showed where it was missing. The companies that get this right don’t just plug in a tool. They build it into a bigger shift toward transparency, agility, and continuous development. It works when leadership is clear about why they’re using it, thoughtful about how it’s rolled out, and serious about building trust around it.
While we're not an HR tech firm, one of our long-term clients — a regional logistics company — recently integrated Eightfold.ai to improve internal mobility. The challenge was retention: high-performing mid-level staff were leaving because they couldn't see a future within the company. Eightfold's platform analyzed both internal CVs and work histories, surfacing lateral moves and stretch opportunities that managers hadn't previously considered. Within six months, internal promotion rates rose by 22 percent, and attrition dropped notably among staff under 35. Employees said they felt "seen" by the system — a reflection of how AI matched not just past roles, but adjacent skills and career ambitions. That emotional shift mattered as much as the data. But it wasn't frictionless. Early pushback came from managers who worried about losing team members to internal poaching, and employees needed reassurance on how their data was used. Transparency was key. The company held town halls and published a plain-English FAQ about how the algorithms worked and what safeguards were in place to prevent bias. AI in internal mobility won't fix broken culture — but when aligned with clear values and good communication, it unlocks talent already sitting in the building.
Employee privacy is one of the hardest parts of using AI platforms for internal mobility. We trialed Fuel50, and while the recommendation engine was helpful, some team members were uncomfortable knowing the system was scanning their resumes, performance data, and even training logs. Transparency helped. We ran town halls explaining how the data was being used and made it opt-in. We also anonymized early recommendations to avoid unconscious bias in manager selections. Even with those steps, adoption was slow. Around 30% of employees opted in during the first rollout. However, the ones who did were 1.7x more likely to apply for internal roles. It's not a silver bullet, but AI can widen the path if people trust what it's doing.
When discussing the transformation of internal mobility and talent development through AI, several companies are really standing out for their innovative use of technology. For example, a tech firm I consulted with utilized Gloat to enhance their talent mobility significantly. By implementing this AI-driven platform, they were able to seamlessly match employees' skills with internal project needs, resulting in a noticeable increase in employee satisfaction as individuals felt more engaged and effectively utilized. This led to quantifiable improvements, including a 30% reduction in turnover rates within the first year of implementation. However, the adoption of such technologies isn't without its challenges. Costs can be substantial, not only in terms of the initial investment in the software but also in the ongoing training required for HR professionals and the modification of existing processes to accommodate the new system. Privacy concerns also arise as employees might feel apprehensive about the extent of data being analyzed and stored. Moreover, there is the persistent issue of algorithmic bias, which can inadvertently perpetuate past hiring and promotion prejudices unless carefully monitored and adjusted. From my experiences, companies striving to implement these technologies should prioritize transparency with their employees about how AI is being used and engage in continuous dialogue to address any concerns. To anyone considering this transition, it becomes crucial to weigh these factors carefully against the potential benefits AI can provide in shaping a dynamic, satisfied workforce.