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Featured
2 min read
The One Model Team
With Python or R, every step takes time. Cleaning data, writing code, running tests, and then turning it into something leaders can actually use. By the time results are ready, the moment to act may have passed. One AI does the heavy lifting for you. Data is already validated and clean, workflows are guided, and insights land in visuals that are presentation-ready. Leaders see the story immediately and know what to do next. The payoff? Fewer people involved, less time spent, lower cost, and faster answers you can trust. Check out the infographic below to see how One AI stacks up against Python and R. Click here to view the full infographic. Want to learn more about One AI?
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Featured
5 min read
Hayley Bresina
In today’s dynamic workplace, understanding your organization and its people is key to driving change. Predictive analytics and behavioral insights help human resources cut costs, improve efficiency, and drive impact. By forecasting future trends and uncovering root causes, predictive models empower HR professionals to act proactively—whether it’s reducing turnover costs, optimizing team structures, or boosting performance. Predictive analytics isn’t just a nice-to-have—it’s a necessity. Organizations that don’t adopt it risk falling behind competitors using data for smarter decisions and better outcomes. At the core of this shift are HR analysts and One AI machine learning. While data scientists are often the go-to for building machine learning models, HR analysts—human resources, people workforce, or employee analysts—bring a unique advantage. Their deep expertise and familiarity with organizational data make them uniquely well-equipped to unlock the full potential of One AI. Here’s why: They Understand the Data Like No One Else HR analysts are the stewards of their organization’s data, familiar with the nuances, outliers, and trends in employee demographics, engagement surveys, and performance metrics. A study in the Journal of Internet and Information Systems (2022) found that domain expertise is crucial for building effective models, particularly in fields like human resources, where understanding context is key (JIER, 2022). Models informed by domain experts were more accurate and actionable, as they can identify the most relevant data and filter out noise. This expertise ensures models are built on high-quality data, leading to trustworthy insights that drive results. They Bridge the Gap Between Data and Strategy One of the key advantages HR analysts bring is their ability to connect data insights with strategic goals. Unlike data scientists, who focus on technical accuracy, HR analysts align data with outcomes that matter—like improving retention, engagement, and performance. The study in the Journal of Internet and Information Systems (2022) also highlights that HR professionals’ ability to align insights with business strategy ensures analytics are not just interesting, but also impactful. One AI supports this alignment with intuitive visualizations and easy-to-use exploratory data analysis (EDA) tools, allowing HR analysts to uncover actionable insights quickly. For example, in a retention model, HR analysts might find that “team size” predicts attrition. By analyzing this, they could discover that large teams lead to disengagement due to weak personal connections and managers struggling to provide feedback. Insights like these help HR leaders adjust span-of-control policies to optimize team size for both employee satisfaction and managerial effectiveness. With One AI’s advanced EDA and visualization, these insights are clearly presented, enabling HR analysts to turn findings into strategic actions that drive organizational change. They Make Machine Learning Accessible and Ethical HR analysts need to present insights to non-technical stakeholders, making explainability key. One AI’s transparency features—like explainable outputs, adjustable parameters, and clear performance metrics—allow analysts to understand and adjust models, ensuring predictions are clear and defensible. Its intuitive interface helps HR professionals and analysts tailor models while keeping performance metrics front and center to avoid underperforming models. This level of transparency fosters trust in analytics, a critical factor in human resources decision-making where outcomes directly impact people. McKinsey (2023) found that explainable AI improves trust and adoption, particularly in high-stakes fields. One AI also helps HR professionals make accountable, actionable insights (McKinsey, 2023). Additionally, HR analysts are well-versed in ethical data use, making them natural stewards of fair AI. The International Journal of Research Publication and Reviews (2023) emphasizes that explainable AI supports accountability and ethical HR practices (IJRPR, 2023). They Thrive with User-Friendly Tools While data scientists often rely on coding and complex algorithms, HR analysts shine with intuitive, user-friendly platforms like One AI. Built for HR, One AI provides guided workflows and automated processes that remove technical barriers without sacrificing analytical depth. For example, when building an attrition model, analysts can start with pre-set best practices, ensuring a solid foundation without needing technical expertise. If adjustments are needed—like modifying thresholds or adding variables—the interface makes it simple, with clear explanations of their impact. This blend of automation and flexibility lets HR analysts focus on actionable insights rather than navigating technical complexities. As The HR Digest (2024) notes, the right tools empower HR professionals to make data-driven decisions, no matter their technical background (The HR Digest, 2024). Unlocking the Potential of HR Analytics When HR analysts combine their expertise with One AI, they do more than build models—they uncover insights that drive organizational change. By blending their deep data understanding with machine learning, analysts can predict outcomes, identify key behavioral drivers, and implement strategies that deliver results. This collaboration helps organizations make informed decisions, enhance employee experiences, and achieve business goals. For decision-makers, the message is clear: Equipping analytics experts with the right tools strengthens and supports the organization. For HR analysts, using One AI allows them to expand their impact, turning data into actionable strategies. The real question isn’t whether HR analysts should use One AI—it’s how quickly will you enable them to lead in People Analytics?
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Featured
8 min read
Hayley Bresina
So, you built and launched a machine learning model with a One AI recipe, congratulations! But the work doesn’t stop there. A model isn’t only about making predictions, it’s about using those predictions to understand your organization and take meaningful action. For example, identifying the main drivers of voluntary attrition can guide targeted retention strategies, while seeing how risk is spread across the business can help you step in early to reduce turnover. In One AI, our visualization templates take predictions a step further by turning them into insights you can use. These visuals have been shaped through years of client work to make complex results clearer and easier to share with stakeholders. With templates that are tested and ready to use, you can spend less time explaining the output and more time acting on it. Visualizations That Empower Strategic Decision-Making 1. Model Performance We believe trust starts with transparency, which is why model performance is front and center. This visualization shows F1 Score, Precision, and Recall for both classes: employees who stay and employees who leave. Showing performance for each class is critical, especially in imbalanced datasets common in people analytics. For instance, if only 10% of employees voluntarily leave and the model predicts everyone will stay, it might appear highly accurate overall (90% accuracy) but completely fail to identify those at risk, which is the point of the model. Breaking metrics down by class gives a true picture of effectiveness and helps you avoid relying on a model that should not be trusted. 2. Drivers Visualization What factors are pushing employees to leave, and what factors are keeping them engaged? The Drivers Visualization shows the most important influences on both sides and how much they matter. For example, in this chart, higher manager performance and longer tenure support retention, while frequent travel and higher job levels increase attrition. With this view, you can take action to address the factors that drive people out and strengthen the ones that encourage them to stay. Because the models use SHAP, you can filter to any group, such as managers, the sales team, or employees in California, and the drivers will reorder to show what matters most for that group. 3. Feature Directionality and Impact Can the same factor push some employees to stay while causing others to leave? The Feature Directionality and Impact visualization shows both the direction of influence and the strength of each factor’s contribution. For instance, salary increases might strongly support retention, while salary stagnation can signal a higher risk of attrition. This view helps you recognize the tradeoffs and complexities behind workforce behavior, so you can weigh interventions more carefully and avoid one-size-fits-all solutions. 4. Where Does Risk Sit? Where are the pockets of highest turnover risk in your organization? This visualization groups employees into low, medium, and high risk and breaks that down by areas like department, tenure, gender, performance score, and more. For example, tenures or job levels may show a higher concentration of high-risk employees. This makes it easier to pilot retention strategies with smaller groups where the impact of intervention will be most immediate and impactful. 5. Geospatial Risk of Voluntary Termination The geospatial map shows where turnover risk is concentrated across regions and locations. For example, you may see higher risk in certain countries or cities compared to others. This makes it easier to identify regional patterns and focus on location-specific strategies, whether the drivers are market conditions or internal practices. 6. Individual Predictions This view provides predictions at the individual level, showing an employee’s likelihood of leaving and the factors influencing that outcome. While models are most reliable at the group level, individual results can still provide helpful context in specific use cases. Because of the sensitivity, this visualization should be tightly permissioned and used with care. The Value of SHAP and Customization All these visualizations use SHAP (SHapley Additive exPlanations), a method that makes predictions easier to understand at both the individual and group level. For each individual data point, such as an employee, SHAP calculates how much each factor influenced the prediction. These individual explanations are then brought together to show the overall drivers of attrition and retention across the organization. Because SHAP works at this individual, detailed level, you can filter results by any dimension, such as department, tenure group, or location. The visuals then adjust dynamically, helping you uncover trends and insights tailored to your organization’s needs. From Insights to Action The goal of these visualizations is not just to explain your data but to guide meaningful action. By seeing the main drivers of voluntary attrition, how they vary across groups, and where the highest risks sit, you can make decisions that directly support your organization’s goals. Use these tools to: Spot early warning signs of turnover and step in before they spread more widely. Shape manager training and support around the factors most linked to attrition. Adjust policies or programs, such as career development or travel requirements, when they appear as key drivers. Plan for the future by building retention insights into workforce and succession planning. Machine learning should help you understand and shape your organization. The visualizations in One AI make sure you are not only building models but also building a stronger, more informed workplace. Curious about One AI? Learn more.
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