Power your talent decisions with deep insights on engagement, performance and retention that transform your HR strategy.
Real cases where AI empowers your decisions
Narrative: A technology company developed a predictive program capable of predicting with 95% accuracy which employees plan to leave, saving more than $300 million in retention costs, while an airline reduced its voluntary turnover by 12%.
Challenge: Staff turnover generates high replacement costs, the loss of valuable talent affects operational continuity, and the lack of predictability prevents timely preventive actions.
Benefit: Saved more than $300 million in retention costs, predicted 95% of at-risk employees accurately, increased overall engagement by 20%, reduced voluntary turnover by 12%, and retained 2 out of 3 "redeemable" employees.
Narrative: An industrial conglomerate employs AI in its performance management system, observing a 10% increase in average productivity, while a Japanese company developed a system that improved the efficiency of logistics workers by 8%.
Challenge: Teams operate without clear visibility of their areas for improvement, generic feedback does not generate specific impacts, and the lack of analysis prevents optimizing task allocation according to skills.
Benefit: 10% increase in average employee productivity, ~8% improvement in operator efficiency, 23% faster call center call processing, ~15% improvement in monthly team output, and better assignment of tasks according to individual skills.
Narrative: A software company applied AI to analyze work climate responses, identifying areas of dissatisfaction quickly, achieving a 95% increase in eNPS in one year, while a technology company detected isolated remote teams and increased satisfaction by 15%.
Challenge: Work climate problems silently escalate into crises, late detection of tensions generates avoidable resignations, and lack of continuous monitoring prevents timely interventions.
Benefit: 95% increase in eNPS in one year, saving 30 work hours in feedback analysis, 15% increase in overall satisfaction, 40% reduction in intention to leave, and up to 24% less turnover with 12% more productivity.
Narrative: A technology company integrates AI into its Strategic Workforce Planning by combining internal analytics with external trends to anticipate 3-5 year talent gaps, while an Asian manufacturer used SWP to decide optimal plant construction.
Challenge: Talent gaps surprise without prior preparation, the lack of alignment between growth and capabilities generates bottlenecks, and expansion decisions are made without considering the availability of qualified personnel.
Benefit: Anticipation of need for 50% more data scientists for timely upskilling programs, generation of 300% more revenue per employee than the median, strategic decisions aligned with actual talent availability, 25% fewer unfilled critical vacancies, and 50% faster hiring times.
Narrative: A consumer goods company transformed its process with AI by saving 70,000 hours in interviews and improving onboarding, while studies show 30% more engagement in training and 25% better learning outcomes with personalized AI.
Challenge: Generic training is not aligned with specific needs, employees receive training that is irrelevant to their roles, and the lack of customization reduces the effectiveness of learning.
Benefit: Saved 70,000 hours in interviews, increased 30% in training engagement, improved 25% in learning outcomes, completed 40% plus training aligned with real needs, and increased knowledge retention to 95%.
Narrative: A Japanese company developed happiness tracking with wearables and AI showing ~8% improvement in productivity along with increased daily happiness, while an American bank achieved 23% more speed in call handling with synchronized breaks.
Challenge: Wellness and performance are seen as conflicting goals, lack of wellness monitoring impedes preventive interventions, and burnout causes costly talent losses.
Benefit: ~8% improvement in productivity with increased happiness, 23% increase in call handling speed, 10 percentage point improvement in perceived well-being, 20% reduction in absenteeism, and $3 return in productivity for every $1 invested in well-being.
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Narrative: A multinational food company implemented machine learning to refine its demand forecasts, while several industry studies demonstrate the transformative impact of AI on inventory management.
Challenge: Companies face constant stock-outs that result in lost sales, product obsolescence due to excess inventory, forecasting errors that affect planning, and overworked planning teams.
Benefit: 30% reduction in lost sales due to stock-outs, 30% less product obsolescence, 20% less forecast errors, up to 50% less planning team workload, and decreased demand errors by 30-50% with logistics costs reduced by 10-40%.