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Featured
6 min read
Taylor Clark
Accessing and making sense of data has traditionally required reliance on data engineers and IT teams. This often slowed decision-making and limited HR’s agility. Now, with Agentic AI, One Model is changing that by democratizing data engineering. This technology empowers HR teams to manage and analyze data independently, enabling faster, more informed decisions without waiting for technical support. Let’s explore how Agentic AI is unlocking new levels of autonomy and efficiency for HR teams. The Traditional Data Engineering Challenge Historically, HR teams have relied heavily on technical experts, such as data engineers, IT departments, or external vendors to manage and analyze complex data. These technical teams were the gatekeepers to valuable insights, responsible for cleaning, structuring, and transforming raw data into something actionable. While these specialists brought crucial expertise, the process was often slow, inefficient, and out of reach for non-technical users. For HR departments, this dependency meant delays, bottlenecks, and missed opportunities for real-time decision-making. Relying on data engineers for every transformation or analysis no longer makes sense. The need for speed and agility is more pressing than ever, and HR teams must have the ability to interact directly with their data and generate insights without waiting for a technical team to step in. This is where Agentic AI becomes a game-changer. However, this doesn’t mean that data engineers are no longer needed. Data engineers will always be essential for overseeing complex data architectures, optimizing performance, ensuring data quality, and managing security. Their expertise remains critical in larger-scale transformations and integrations. The real shift with Agentic AI is that HR teams can now handle routine data transformations and analysis independently, freeing up data engineers to focus on high-level, strategic work, such as building sophisticated data models, creating complex workflows, and ensuring the scalability and security of data systems. What is Democratizing Data Engineering? At One Model, democratizing data engineering means breaking down the technical barriers that have traditionally hindered HR teams from fully leveraging their data. With Agentic AI, we’ve embedded data expertise directly into our platform, enabling HR professionals to interact with and manipulate data without the need for a full dedicated data engineering team. This shift allows HR teams to gain deeper insights, run experiments, and make strategic decisions faster than ever before. Unlike traditional systems where data transformations and model developments require specialized knowledge, One Model’s Agentic AI automates these processes, empowering users with the tools to directly engage with their data. By embedding AI into the system, we are removing the technical complexity from the equation and making data engineering more accessible to all levels of the HR team, regardless of their technical expertise. How Agentic AI Empowers HR Teams One of the core benefits of democratizing data engineering is that it allows HR teams to operate with greater autonomy. In the past, if an HR leader wanted to change a data model, they would have had to rely on technical support, which could take days or even weeks to implement. With Agentic AI, HR professionals can now make adjustments in real-time, using a set of powerful yet easy-to-use tools that handle the complex tasks traditionally reserved for data engineers. For example, HR professionals can now create new metrics, perform data analysis, and design new workflows without needing specialized knowledge of data modeling or programming. This self-service approach significantly reduces the dependency on external teams and speeds up the process of gaining valuable insights. By automating data manipulation and analysis, Agentic AI ensures that HR teams can focus on higher-level tasks like strategic decision-making, employee engagement, and talent management. The Future of Data Engineering with Agentic AI As Agentic AI continues to evolve, its ability to automate complex workflows and democratize data engineering will only grow stronger. We are constantly improving the platform’s flexibility, security, and adaptability, ensuring that HR teams can continue to evolve and make smarter decisions. With the power of Agentic AI at their fingertips, HR teams can harness the full potential of their data without needing a large team of data engineers. At One Model, we believe that the future of HR lies in empowering teams to access, analyze, and act on their data without unnecessary delays. By embedding AI directly into the workflow, we are not only simplifying the data engineering process but also giving HR professionals the tools they need to make impactful decisions faster. As we continue to integrate more advanced AI capabilities into our platform, HR teams will have even more autonomy and control over their data, allowing them to drive greater value for their organizations. See the Full Potential of HR with Agentic AI With One Model’s approach to Agentic AI, HR professionals no longer need to rely on large, specialized teams to manage their data. Instead, they can directly engage with the data, make adjustments in real-time, and generate insights without waiting for technical teams to intervene. This is the power of democratizing data engineering, and it’s only the beginning. Ready to take a look? Request a demo.
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Featured
4 min read
Taylor Clark
At One Model, we’re committed to providing solutions that not only simplify HR processes but also drive measurable business outcomes. With Agentic AI, we're able to automate workflows and deliver data-driven insights with unprecedented speed and accuracy. Unlike traditional chatbots, which only respond to queries, Agentic AI has the autonomy to make decisions, navigate complex tasks, and execute multi step setup workflows. This capability is crucial for HR teams, enabling them to move beyond mundane tasks and focus on strategic initiatives that impact the business’s bottom line. Reducing Operational Overheads One of the most significant benefits of Agentic AI is its ability to reduce reliance on human resources for repetitive tasks. Traditionally, HR teams rely on large data engineering teams to manage complex data and produce actionable insights. With Agentic AI, this process becomes automated, allowing HR professionals to bypass bottlenecks in the system and access valuable insights faster than ever before. As a result, HR teams can shift their focus to high-value work, such as enhancing employee engagement, optimizing talent acquisition, and addressing retention issues. Automating Complex Workflows Another way Agentic AI creates value is through the automation of end-to-end workflows. Consider the process of generating topical storyboards or loading new and critical data. These tasks often require multiple steps, from data collection to analysis and presentation. In the past, these tasks would have taken considerable time, requiring HR staff to manually compile information and coordinate work across different functions. With Agentic AI, these workflows can be automated, a plan is developed, data is processed, storyboards and insights are created all with minimal human intervention. Improving Decision-Making with Real-Time Insights Agentic AI excels at accelerating time-to-insight, empowering HR teams to make faster, data-driven decisions. For instance, in an environment where employee turnover is rising, HR teams can instantly access reports on retention drivers or load external data to better understand the emerging trend, helping them take proactive measures to address the issue. With the ability to pull data from various sources and analyze it on the fly, Agentic AI ensures that HR decisions are always based on the most up-to-date information. Leveraging Customizable Data Tools for Business Needs One of the standout features of Agentic AI is its adaptability to diverse business needs. Whether it’s forecasting employee performance or analyzing workforce demographics, One Model’s platform can accommodate a wide range of data types and structures, ensuring that the AI agents are always working with relevant and well-organized data. This flexibility allows HR teams to tailor workflows to the unique needs of their organizations, increasing the precision and relevance of insights generated. The Role of Human Oversight While Agentic AI can automate many tasks, it still benefits from human guidance. For instance, HR teams can work alongside AI agents to ensure that the insights generated align with business objectives and ethical standards, or to review a proposed plan the Agentic AI wants to take in order to accomplish a task and provide corrective feedback. This collaboration between AI and human experts ensures that the AI remains a powerful tool for decision-making, rather than a source of potential error or misdirection. Accelerating Value with Agentic AI Agentic AI offers organizations a way to unlock greater value from their data and processes. By automating mundane tasks, improving decision-making, and empowering HR teams with customizable data tools, Agentic AI streamlines operations and accelerates business outcomes. As we continue to refine and expand the capabilities of Agentic AI, One Model is committed to ensuring that HR teams can move faster, make smarter decisions, and ultimately generate more value for their organizations. Want to learn more? Request a demo today.
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Featured
6 min read
Taylor Clark
Imagine it’s Monday morning. Your HR team needs critical insights on employee turnover, but you’re still waiting for IT to validate new data sources. As the hours pass, the data arrives late, and you miss the opportunity to proactively address retention concerns. With Agentic AI, this scenario becomes a thing of the past. Agentic AI can automatically connect and validate new data sources, accelerating the process and delivering actionable insights in real-time. At One Model, we’re revolutionizing HR by automating complex tasks, allowing your team to focus on high-impact decisions. Our solution empowers you to implement your data sources 50% faster through a self service approach, enhancing operational efficiency and delivering real business value when it matters most. What is Agentic AI? Think of Agentic AI as a highly skilled, reliable assistant. It’s someone who anticipates what you need next and handles routine complexities effortlessly, allowing you to focus on strategic decisions. Unlike simple automation tools or chatbots, Agentic AI can make decisions, reason through processes, and carry out workflows with little to no human involvement. The AI agents are capable of interacting with multiple tools, gathering insights, and executing tasks independently. As they work with dynamic data and learn from their actions, Agentic AI adapts and adjusts workflows to better meet your organization’s goals. This autonomy delivers more flexible and powerful solutions to complex problems, ensuring that tasks are completed efficiently, and business outcomes are optimized. Key Insights on Leveraging Agentic AI for HR Transformation To fully harness the potential of Agentic AI in HR, organizations must address three critical areas: Why You Need a Good Data Foundation The power of AI, including Agentic AI, lies in its access to quality data. Without a well-structured data foundation, AI cannot provide valuable insights. One of the primary hurdles organizations face when implementing AI is ensuring that their data is clean, organized, and accessible. AI models, including generative AI, can only answer questions effectively when the right data is available. For instance, without structured event data, it’s impossible for AI to generate meaningful business insights. At One Model, we focus on creating the right data models that can be leveraged by AI agents to drive value and solve complex business problems. How Agentic AI Will Generate Value for Organizations Agentic AI can transform organizations by automating routine, mundane tasks and allowing human workers to focus on more strategic activities. By automating repetitive processes, agents free up HR teams to focus on higher-value work, like improving employee engagement, identifying drivers of performance and retention,and making data-driven decisions. One Model's approach leverages agentic workflows to accelerate tasks like insights generation, enabling faster, more accurate decision-making. This results in significant time savings and improved business outcomes across HR functions. Democratized Data Engineering One of the key advantages of Agentic AI is how it empowers HR teams to achieve more with less. Traditionally, HR teams relied on large data engineering teams, IT departments, or external vendors to handle data transformation and validation. With Agentic AI, One Model's platform brings data expertise directly into HR systems, enabling HR professionals to manage and analyze their data seamlessly without needing constant support from technical teams. As AI agents handle the complex technical work, HR teams can focus on what truly matters, strategic decision-making. This democratization of data engineering means HR leaders can now achieve the level of insight and impact typically reserved for large teams of analysts, boosting efficiency and enabling smarter business outcomes at a fraction of the effort. One Model’s Role in Agentic AI One Model is leading the integration of Agentic AI into HR and People Analytics. By automating tasks like data analysis, we make these processes faster and more impactful. While Agentic AI is still evolving, we’re dedicated to refining its capabilities to ensure effectiveness, security, and ethical integrity. Our focus includes expanding the scope of agentic workflows to give HR teams more autonomy and flexibility. One Model is focused on: Creating real value with AI agents Enhancing AI agents' ability to manage diverse HR data Strengthening data security and privacy Ensuring collaboration between AI agents and HR teams while maintaining transparency and data integrity As our AI agents mature, One Model will continue to enhance their capabilities, helping clients fully leverage Agentic AI while retaining control over their processes. One Model and the Future of Agentic AI in HR As Agentic AI continues to evolve, One Model is positioning itself at the forefront of this revolution in HR technology. By embedding AI agents into our platform, we are helping HR teams move faster, make smarter decisions, and ultimately generate more value for their organizations. As we continue to refine and expand the capabilities of Agentic AI, we remain committed to empowering HR professionals with the tools they need to unlock the full potential of their data. With a solid data foundation, a focus on value-driven automation, and the ability to reduce reliance on external data engineers, One Model is ready to lead the way in the next phase of People Analytics. The future of HR is here, and it’s powered by Agentic AI. Ready to learn more about One Model's Agentic AI? Request a demo today.
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Featured
4 min read
Taylor Clark
Machine Learning Explainability for Human Resources One AI is the HR industry's leading machine learning and predictive analytics technology because it's flexible, secure, and transparent. Watch Taylor Clark, our Chief Data Scientist, demo a brilliant turnover forecast in seconds in the video below. In Taylor's example, you can see turnover going up over time and then coming back down a little. But how can you inform decisions about which direction the trend could go moving forward? And how can you confidently stand behind that prediction? With One AI, it only takes a click. A powerful forecast in a single click? Yes! Generally, Taylor suggests that people think about turnover trends from multiple contexts and perspectives. So in the linked video, he shares his screen to demonstrate the HR industry's fastest way to analyse turnover. In the People Data Cloud™️ platform, it's a single click on the "light bulb" icon of any table, chart, or graph. Once clicked, you see One AI thinking in the background and doing a lot of math. Suddenly, it produces a forecast. What does the cone mean? The shaded cone over the forecasted zone of the graph is considered the range of uncertainty. It's also known as an uncertainty interval or a confidence interval to mathematicians and statisticians. But here's what it's telling us: the actual results could lie anywhere within this cone when that time comes. The range of possible outcomes is also influenced by the modelling technique that is used by One AI. How can decision makers trust this forecast? Most responsible business leaders need to trust the analyses that they are given. Often, this involves understanding the assumptions and techniques used to generate the analysis. One AI has you covered! The easiest way to win confidence and trust in a forecast with One AI is to simply click on any point within the forecasted range. Then you'll see a pop-up on the screen that shows a whole bunch of information. It includes information such as the upper and lower bounds of the forecast, the different types of algorithms that were used in making the prediction, and even the transformations and data sets used to generate the possible outcomes. You don't need to be a PhD to know that this is the first and only HR technology to offer model explainability and explainable artificial intelligence directly in the reports and as an embedded feature of the analysis. Only One AI is this flexible and transparent. This approach to forecasting is unique in that it can be applied to any table or chart within the People Data Cloud platform. Gone are the days when your data science team needed to recreate the wheel for every single forecast demanded by the business. And gone are the days when you need a data science team for every single type of forecast. Now your modeling teams can build a predictive tool that can be reused and reapplied across a broad range of talent decisions, including attrition, performance, engagement, advancement, and so on. See how One AI takes your forecasting game to the next level. Fill out the form below to get a live demonstration.
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Featured
11 min read
Taylor Clark
The human resources department is a mission-critical function in most businesses. So the promise of better people decisions has generated interest in and adoption of advanced machine-learning capabilities. In response, organizations are adopting a wide variety of data science tools and technology to produce economically-optimal business outcomes. This trend is the result of the proliferation of data and the improved decision-making opportunities that come with harnessing the predictive value of that data. What are the downsides to harnessing machine learning? For one, machines lack ethics. They can be programmed to intelligently and efficiently drive optimal economic outcomes. It seems as though the use of machines in decisions and seemingly desirable organizational behaviors. Of course machines lack a sense of fairness or justice. But optimal economic outcomes do not always correspond to optimal ethical outcomes. So the key question facing human resources teams and technology support is "How can we ensure that our people decisions are ethical when a machine is suggesting those decisions?” The answer almost certainly requires radical transparency about how artificial intelligence and machine learning are used the decision making process. It is impossible to understand the ethical aspect of a prediction made by a machine unless the input data and the transformations of that data are clear and understood as well. General differences between various machine learning approaches have a profound impact on the ethicality of the outcomes that their predictions lead to. So let's begin by understanding some of those differences. Let’s focus on the various types of machine learning models: the black box model, the canned model, and the inductive model. What is a Black Box Model? A black box model is one that produces predictions that can’t be explained. There are tools that help users understand black box models, but these types of models are generally extremely difficult to understand. Many vendors build black box models for customers, but are unable or unwilling to explain their techniques and the results that those techniques tend to produce. Sometimes it is difficult for the model vender to understand its own model! The result is that the model lacks any transparency. Black box models are often trained on very large data sets. Larger training sets can greatly improve model performance. However, for this higher level of performance to be generalized many dependencies need to be satisfied. Naturally, without transparency it is difficult to trust a black box model. As you can imagine, it is concerning to depend on a model that uses sensitive data when that model lacks transparency. For example, asking a machine to determine if a photo has a cat in the frame doesn't require much transparency because the objective lacks an ethical aspect. But decisions involving people often have an ethical aspect to them. This means that model transparency is extremely important. Black box models can cross ethical lines where people decisions are concerned. Models, like humans, can exhibit biases resulting from sampling or estimation errors. They can also use input data in undesirable ways. Furthermore, model outputs are frequently used in downstream models and decisions. In turn, this ingrains invisible systematic bias into the decision. Naturally, the organization jeopardizes its ethical posture when human or machine bias leads to undesirable diversity or inclusion outcomes. One of the worst possible outcomes is a decision that is unethical or prejudicial. These bad decisions can have legal consequences or more. What is a Canned Model? The terms "canned model" or “off-the-shelf model” describe a model that was not developed or tailored to a specific user’s dataset. A canned model could also be a black box model depending on how much intellectual property the model’s developer is willing to expose. Plus, the original developer might not understand much about its own model. Canned models are vulnerable to the same biases as black box models. Unrepresentative data sets can lead to unethical decisions. Even a representative data set can have features that lead to unethical decisions. So canned models aren't without their disadvantages either. But even with a sound ethical posture, canned models can perform poorly in an environment that simply isn’t reflective of the environment on which the model was trained. Imagine a canned model that segmented workers in the apparel industry by learning and development investments. A model trained on Walmart’s data wouldn’t perform very well when applied to decisions for a fashion startup. Canned models can be quite effective if your workforce looks very similar to the ones that the model was trained on. But that training set is almost certainly a more general audience than yours. Models perform better when training data represents the real life population that was targeted and represented in the training set. What are Custom Built Models? Which brings us to custom built models. Custom models are the kind that are trained on your data. One AI is an example of the custom built approach. It delivers specialized models that best understand your environment because it’s seen it before. So it can detect patterns within your data to learn and make accurate predictions. Custom models discover the unique aspects of your business and learn from those discoveries. To be sure, it is common for data science professionals to deploy the best performing model that they can. However, the business must ensure that these models comply with high ethical and business intelligence standards. That's because it is possible to make an immoral decision with a great prediction. So for users of the custom built model, transparency is only possible through development techniques that are not cloudy or secret. Even with custom built models, it is important to assess the ethical impact that a new model will have before it is too late. Custom built models may incorporate some benefits of canned models, as well. External data can be incorporated into the model development process. External data is valuable because it can capture what is going on outside of your organization. Local area unemployment is a good example of a potentially valuable external data set. Going through the effort of building a model that is custom to your organization will provide a much higher level of understanding than just slamming a generic model on top of your data. You will gain the additional business intelligence that comes from understanding how your data, rather than other companies' data, relates to your business outcomes. The insights gleaned during the model development process can be valuable even if the model is never deployed. Understanding how any model performs on your data teaches you a lot about your data. This, in turn, will inform which type of model and model-building technique will be advantageous to your business decisions. Don’t Be Misled by Generic Model Performance Indicators A canned model’s advertised performance can be deceptive. The shape of the data that the canned model learned from may be drastically different from the data in your specific business environment. For example, if 5% of the people in the model's sample work remotely, but your entire company is remote, then the impact and inferences drawn by the model about remote work are not likely to inform your decisions very well. When to be Skeptical of Model Performance Numbers Most providers of canned models are not eager to determine the specific performance of their model on your data because of the inherent weaknesses described above. So how do you sniff out performant models? How can you understand a good smelling model from a bad smelling one? The first reason to be skeptical lies in whether the model provider offers relative performance numbers. A relative performance value is a comparative one, and therefore failing to disclose relative performance should smell bad. Data scientists understand the importance of measuring performance. They know that it is crucial to understand performance prior to using a model’s outputs. So avoiding relative performance, the vendor is not being 100% transparent. The second reason to be skeptical concerns vendors who can't (or won't) explain which features are used in their model and the contribution that each feature makes to the prediction. It is very difficult to trust a model's outputs when the features and their effects lack explanation. So that would certainly smell bad. One Model published a whitepaper listing the questions you should ask every machine learning vendor. Focus on Relative Performance….or Else! There are risks that arise when using data without relative performance. The closest risk to the business is that faith in the model itself could diminish. This means that internal stakeholders would not realize “promised” or “implied” performance. Of course, failing to live up to these promises is a trust-killer for a predictive model. Employees themselves, and not just decision makers, can distrust models and object to decisions made with it. Even worse, employees could adjust their behavior in ways that circumvent the model in order to “prove it wrong”. But loss of trust by internal stakeholders is just the beginning. Legal, compliance, financial, and operational risk can increase when businesses fail to comply with laws, regulations, and policies. Therefore, it is appropriate for champions of machine learning to be very familiar with these risks and to ensure that they are mitigated when adopting artificial intelligence. Finally, it is important to identify who is accountable for poor decisions that are made with the assistance of a model. The act of naming an accountable individual can reduce the chances of negative outcomes, such as bias, illegality, or imprudence. How to Trust a Model A visually appealing model that delivers "interesting insights" is not necessarily trustworthy. After all, a model that has a hand in false or misleading insights is a total failure. At One Model, we feel that all content generated from predictive model outputs must link back to that model's performance metrics. An organization cannot consider itself engaged in ethical use of predictive data without this link. Canned and black box models are extremely difficult to understand, and even more difficult to predict how they respond to your specific set of data. There are cases where these types of models can be appropriate. But these cases are few and far between in the realm of people data in the human resources function. Instead, custom models offer a much higher level of transparency. Model developers and users understand their own data much better throughout the model building process. (This process is called Exploratory Data Analysis, and it is an extremely under-appreciated aspect of the field of machine learning.) At One Model, we spent a long time -- more than 5 years -- building One AI to make it easier for all types of human resources professionals build and deploy ethical custom models from their data, while ensuring model performance evaluation and model explainability. One AI includes robust, deep reporting functionality that provides clarity on which data was used to train models. It blends rich discovery with rapid creation and deployment. The result is the most transparent and ethical machine learning capability in any people analytics platform. Nothing about One AI is hidden or unknowable. And that's why you can trust it. Their Artificial Intelligence Still Needs Your Human Intelligence Models are created to inform us of patterns in systems. The HR community intends to use models on problem spaces involving people moving through and performing within organizations. So HR pros should be able to learn a lot from predictive models. But it is unwise to relinquish human intelligence to predictive models that are not understood. The ultimate value of models (and all people analytics) is to make better, faster, more data-informed talent decisions at all levels of the organization. Machine learning is a powerful tool, but it is not a solution to that problem.
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