Hier erfahren Sie, wie Sie als Personalberater HR-Prozesse mithilfe von Algorithmen des maschinellen Lernens optimieren können.

*Recruitment - Resume Screening: NLP - Candidate Matching: Recommendation systems - Interview Scheduling: Automation *Onboarding - Personalized Plans: Decision trees *Performance Management - Prediction: Regression analysis - Sentiment Analysis: NLP *Employee Engagement - Surveys: Text analysis - Attrition Prediction: Classification models *Learning and Development - Personalized Paths: Collaborative filtering - Skill Gap Analysis: Association rule mining *Compensation - Benchmarking: Regression analysis - Benefits Optimization: Optimization algorithms *Workforce Planning - Forecasting: Time series analysis - Succession Planning: Decision trees *Implementation - Data Collection - Model Development -Integration -Monitoring -Change Management

Automating resume screening to quickly identify top candidates, personalizing employee onboarding to meet individual needs, and enhancing employee engagement through data analysis. Machine learning can also optimize workforce planning by predicting future staffing needs and simplify performance management by tracking metrics and providing insightful evaluations. These techniques make HR processes faster, more efficient, and more effective, ultimately benefiting both the organization and its employees.

1. Data Analysis and Prediction: • Analyse historical HR data, (turnover rates, performance reviews, and recruitment data) & predict future needs. 2. Automated Resume Screening & Interview process: • Automate the initial screening of resumes. • Through AI, identify relevant skills, experience, and qualifications, speeding up the candidate selection process. • Implement AI algorithm to identify candidate skill set during the practical interview 3. Employee Engagement Analysis: • Develop ML algorithms to analyze employee feedback surveys, performance reviews and prepare employee happiness index • Implement AI algorithm which can identify physical seating gesture which will help org to measure the productivity.

I gather ML will revolutionaries HR Management, creating time for HR professional to work on more recent advance people issues such as gratitude towards organisation, from business like approach to a connected and winning together approach. ML will provide the necessary data and insights to focus on relevant issues

Automating the resume screening process with machine learning is crucial for modern HR teams. It not only saves significant time by quickly identifying the most qualified candidates but also enhances the fairness of hiring practices by minimizing unconscious biases. This data-driven approach ensures that hiring decisions are based on objective criteria, leading to better recruitment outcomes and a more diverse and inclusive workforce. As a consultant, helping HR teams implement and trust these systems can transform their hiring efficiency and effectiveness.

One of the most important benefits of ML and AI is to be able to automate time consuming activities like screening of resumes and other related screening activities. Where ever there is a need that the activity is very administrative and can be automated, AI tools can be used to improve productivity and save on time .

Segue alguns modelos de ML: 1. **Regressão Logística**: Ideal para prever a probabilidade de eventos binários, como a rotatividade de funcionários. 2. **Árvores de Decisão e Florestas Aleatórias**: Boas para classificações e predições, fornecendo interpretabilidade sobre quais fatores influenciam mais os resultados. 3. **Redes Neurais**: Úteis para padrões complexos em grandes conjuntos de dados, como prever desempenho futuro baseado em múltiplas variáveis. 4. **Modelos de Clustering (K-means, DBSCAN)**: Eficazes para segmentar funcionários em grupos com características semelhantes para intervenções personalizadas. Escolha o modelo que melhor se adequa a sua jornada e mãos a obra!

So, I am the type of person they call an Innovator and Early Adopter but on the topic of resume screening I’m 100% a Laggard. I truly don’t think people should be screened by a Machine. Look, I get it, you have to go through thousands of CVs. I understand it is a monumental task. However, the thing that separates humans from machines is exactly what as a hiring manager I focus when hiring people. Looking for keywords, patterns, years of experience, time spent at each company isn’t going to help you see the soul and the grit of the person you are looking for. In fact, CV screening should be a task that is only done to separate those people that apply to absolutely everything from the ones that actually want that job with all their hearts.

This use case of machine learning and the application of AI is a game changer for HR teams and consultants. Sifting through resumes is a daunting task that AI will be most efficient at. The outcome I believe will carry a higher degree of validity and reliability compared to the old fashioned way of reading through CVs.

This is easier said than done. Because what matters is what we have captured in our solutions, and therefore in our data. Would you feel comfortable when a machine uses this data? How about past decisions: were they reasonable? Unbiased? Fair? I can’t stress enough the importance of understanding your data before you apply machine learning. But when you do have great data, the insights will help you make better predictions!

The application of technology regarding the loyalty of employées must be link with one important thing : all the onboarding process, in order to accompany at the maximum the employée within its company journey.

This one is absolutely fascinating! The amount of time spent collecting data, cleaning data, identifying patterns in absences and attrition, and then creating the presentation, paper, or dashboard is completely unnecessary. I would argue that two interventions are possible by combining large models and humans: interpreting the data and creating action plans. Then humans alone are the only ones who can act and make it happen.

I believe the applications in this instance are endless. One key area is in succession planning. Imagine blindly including an ‘at risk’ employee in a succession plan.

Hoy en día hablamos del proceso de "reclutamiento, selección y retención de talentos", dejando de lado la premisa de solo abocarnos a las primeras dos. Pero qué difícil es cumplimentarlo. Son diversos los factores que influyen: podemos tener la mejor remuneración, competitiva en el mercado y aún así contar con una fuga importante de talentos; o a su vez, tener el personal motivado y con desafíos constantes pero no contar con un salario competitivo; éste último genera la fuga. La pregunta es ¿cómo generar una armonía entre ambas?

I think that we should combine both the lecture of each talent card based on the development need that might change in a yearly basics based on restructuring, role change or enlargement of scoop thus lead us to a continuous sizing to respond tangibly to the needs expressed by the employee We need also to have a feedback tool that it will allow the company to gather feedback on the experience and what can be improved in the future

I would add it aligns with what they learn. How many times do companies implement a new system or go through a change, give the training, but the documentation in their Knowledge Base or the one the teams use remains with the outdated version? Linking these two won´t only help HR but also help everyone in the company.

Trilhas de capacitação flexíveis, cheias de caminhos possíveis, são o que há de mais valioso da tecnologia como nossa aliada. Ao trazer o "modus operandi" das redes sociais, dos streamings, para dentro das empresas, viabiliza-se por exemplo, a disponibilidade de temas "à escolha" - o que nos ajuda a entender por onde o colaborador está demonstrando interesse/desejo/necessidade de desenvolvimento (sem ele precisar falar! Suas atitudes e escolhas nos mostrarão). Só olhando por esse prisma, já conseguimos ver tendências, viabilizar experiências que engajam e extrair informações valiosas para continuar o desenvolvimento das pessoas e das corporações!

Uma lacuna muito grande nas empresas portuguesas é a falta de uma definição clara de papéis de cada colaborador. Antes que sejam desenvolvidos programas de desenvolvimento é necessário diagnosticar a clareza do contributo de cada um e do seu impacto a curto e médio prazo na Organização e sua estratégia. Serão criadas sinergias e os programas de desenvolvimento terão um impacto mais ajustado às reais necessidades, além de fomentarem um sentimento de compromisso e responsabilidade muito maior.

Continuous Feedback Loop: Establish a feedback loop where machine learning algorithms continuously gather data on employee performance and training outcomes to refine and adjust personalized training programs over time. This iterative approach allows organizations to adapt quickly to changing employee needs and evolving skill requirements, ensuring that training remains relevant and impactful.

One of the use case for performance management that has shown in last year successful is AI integration into the Performance Management tool (this time within Workday) that helped leaders to create reviews quicker and give them time for creative thinking on how to tailor specific support for employees to help them achieve their own potential. If you need help please feel free to connect, I am happy to share my knowledge and experience to help you bring the change in your HR team.

ML-gestützte Datenerkenntnisse für optimales Talentmanagement Die Leistungsanalyse von Mitarbeitern über manuelle Prozesse ist komplex und zeitaufwändig. ML-Algorithmen können diese Herausforderung meistern. Durch die Verarbeitung komplexer Datenquellen wie Leistungsbewertungen, Projektstatistiken und Umfragedaten, gewinnen ML-Systeme tiefgreifende Einblicke in Produktivität, Engagement und Potenzial der Mitarbeiter. Vorteile von ML-gestützten Performance-Analysen: • Effektive Datennutzung: Automatische Analyse großer Datenmengen • Umfassende Einblicke: Identifizierung von Leistungsträgern und Verbesserungspotenzialen • Datengestützte Entscheidungen: Fundierte Grundlage für Personalentwicklungsmaßnahmen

AI should be limited to summarizing data for decision-making. Using AI to identify high performers, potential leaders, or gaps could be risky because AI cannot detect attitude or understand if someone is underperforming due to temporary issues. It’s crucial to set clear ethical boundaries on AI use. A successful use case for AI in performance management from last year involved integrating AI into the tool to help leaders create reviews faster, allowing them more time to think creatively about how to support employees in achieving their potential. If you need assistance, feel free to connect with me. I'm happy to share my knowledge and experience to help you bring positive changes to your HR team.

Performance analysis cannot be driven just by AI, it needs human intervention, It could be used but up to some extent, just relying on it definitely makes no sense and is not weighable.

HR relies on new platforms and technologies to keep up with the evolving work environment As a result, AI, with its analytical, diagnostic and predictive capabilities, is becoming increasingly common in HR operations. Artificial intelligence is not a new concept for human resources. But as the technology matures, it also becomes an everyday part of tools and applications. While AI is useful for streamlining processes and speeding up decision-making, it also raises concerns. Be aware of its potential and pitfalls as you decide how to incorporate it into your business.

I sharing with you action plans for leaders that you can do today without any preparation to boost your team productivity and encourage learning: 1. Encourage your team to play around with Chat GPT. Encourage them to utilize tool and explain them all they need to know about responsible use of AI (if there is Responsible AI Policy share with them). 2. Encourage your people to try and utilise tools such as: Loom, Microcopy, Tinywow, Jenni AI, Convertio, Quillbot, Remove.bg,Otter AI. Even look for budget to pay fees for employees who are interested. It is crucial to nudge people into the right direction, don't wait for people to be self aware about new technology. Nudge them towards AI!

I guide organizations in leveraging machine learning to align their workforce strategy with business objectives by identifying and forecasting talent needs. By analyzing data, we pinpoint critical skill gaps and future demands, ensuring the right people with the right skills are in place when needed. I also help integrate these insights into their strategic planning. This proactive approach supports organizational growth and adaptability. Consequently, businesses can achieve their goals more efficiently with a well-prepared and skilled workforce.

Como psicóloga, percebo a importância fundamental do planejamento estratégico da força de trabalho, um ponto intrigante é a capacidade do ML em analisar diversos dados, como tendências de mercado e internos da organização, para prever com precisão as futuras necessidades de habilidades. Essa capacidade capacita os profissionais de RH a anteciparem demandas do mercado e a tomarem decisões mais fundamentadas em relação às contratações e ao desenvolvimento de talentos. Nesse contexto, como consultora de RH, vejo uma oportunidade singular de guiar as organizações na aplicação efetiva do aprendizado de máquina, assegurando que estejam preparadas para enfrentar futuros desafios com uma equipe robusta e habilidosa.

Again, this is quite dangerous. Without comprehensively governing, bias and discrimination will impact who the right people are and what the needed skills are. There is a considerable risk that we will hire the same type of individual repeatedly. One thing is to use analytics and modeling; another is to use it to align with the business objectives without keeping control of the recommendations or actions.

When integrating ML into HR processes, companies must also consider: Defining Duties: It's crucial to clearly define the responsibilities for those implementing and overseeing algorithmic decision-making to ensure accountability and prevent discrimination. Understanding Your Data: Make sure there's a solid understanding of the training data sets used by external service providers. This helps align the algorithms with the company culture and avoids potential biases. Audits: Research shows that regular audits and a reasonable degree of human intervention in algorithmic decision-making processes are required to ensure contextual fairness. By paying attention to these aspects, companies can effectively streamline HR processes using ML.

Apart from the actual HR Processes, it is also the documentation that is equally important. Versions & final documents can be done. Policy making and the SOPs can be automated and documentation of these can also be done. Knowledge Management - especially the internal knowledge perspectives can be stored as a reference point for business continuity and succession planning.

Employing AI and few of the renowned HR and management information systems present diverse advantages both that of time and space . These enable us to observe , analyze and identify trends thus making futuristic planning and time phased decision making based on real time data. Dealing with thousands of employees generating reports , performance graphs , metrics for timely correlating preventive , corrective and preemptive action would not be possible without an efficient algorithms based software. Mastering this machine learning and developing the residual skills would in turn be abling HRs to galvanize the 360 degree affects in an affective & efficient way .

As an HR consultant, we can optimize HR processes using machine learning (ML) algorithms by automating routine tasks such as resume screening, employee onboarding, and performance appraisals. ML algorithms can sift through extensive datasets to discern patterns, forecast employee attrition, and refine talent management strategies. Natural language processing-powered chatbots can manage routine inquiries, enhancing response efficiency. Moreover, ML can tailor training programs to individual needs and ensure regulatory compliance by monitoring policy adherence.

Optimizing processes... identifying redundancies in a process could be possible through ML and would provide suggestions for optimization