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6 min read
The One Model Team
The integration of artificial intelligence into HR practices has begun to transform how companies engage, support, and optimize their workforce. By adopting a holistic, employee-centric approach to AI deployment, organizations and People Analytics teams can foster a culture of innovation, boost productivity, and ultimately create a workplace where employees are empowered to thrive. A Strategic and Employee-Focused Approach to AI Integration Implementing AI within an HR organization demands a strategic and well-orchestrated approach. A key insight from companies successfully embracing AI is to prioritize change management for AI and employee engagement. Rolling out AI solutions shouldn’t feel like a top-down directive imposed on People Analytics teams but rather a collaborative, bottoms-up process that centers on empowering employees. When People Analytics leaders take a gradual, people-first approach, they can ease anxieties associated with AI adoption. The fear that AI will replace jobs has been pervasive, and addressing this concern upfront is essential for cultivating trust. Leaders need to clearly communicate that AI is a tool to enhance, not replace, human effort. This message must resonate throughout the HR department and company, helping employees view AI as a career-enabling partner rather than a threat. Celebrating AI Adoption as a Driver of Change Measuring initial AI adoption within the HR department is a critical first step. Successful teams have shown that fostering a culture where AI usage is celebrated can accelerate adoption. One effective strategy is creating department-wide channels where People Analytics teams can share their experiences using AI and the benefits it has brought to their workflows. Highlighting these success stories not only reinforces positive engagement but also builds momentum as teams see real examples of AI delivering tangible value. Recognition programs that reward early adopters can further stimulate interest and promote active participation. This step aligns with AI change management best practices, which emphasize that for organization transformation with AI to take hold, the individuals most affected must feel supported and valued. Key Areas of AI Implementation in HR Leading organizations have deployed AI across several HR functions to drive efficiency and enhance decision-making. Below are examples of impactful implementations of AI in HR departments: 1. Recruitment and Interview Processes Integrating AI in recruitment has revolutionized how interviews are conducted and evaluated. AI-powered tools can assist hiring managers by recording interviews, generating time-stamped notes, and linking key interview moments to questions asked. This capability alone can save managers substantial time—up to 30–40 minutes per interview—by automating note-taking and providing instant access to video highlights. 2. HR Chatbots for Employee Services Advanced, AI-powered HR chatbots are streamlining routine tasks by handling knowledge base inquiries and processing transactional requests. Integrated within platforms like Slack, these bots can facilitate actions such as submitting time-off requests or accessing benefits information, freeing up HR teams to focus on more strategic work. This integration also simplifies data access and enhances the overall employee experience. 3. AI-Enhanced Learning and Development AI’s application in L&D involves deploying intelligent tools that help curate learning content, suggest development paths, and assist employees with feedback and coaching tailored to organizational competencies and values. The introduction of AI coaches for career growth discussions or navigating difficult conversations empowers employees with customized guidance that aligns with company culture and goals. 4. Democratize People Data Across Management Teams Team leaders at any level of the organization are better leaders when they better understand the workforce under their care. Business and People Analytics teams are instrumental in that goal through building dashboards and answering big complex questions. But what happens when there are too many ad hoc manager questions across the organization for the people analytics teams to answer? Originally, that meant either questions went unanswered or people analytics teams were pulled from larger, more impactful projects to help. With solutions, like One Model, that is no longer an issue. One AI Assistant is helping companies today by giving managers the ability to ask questions on the people data they can access. HR analytics teams can have confidence in the results of One AI Assistant because it provides clear explainability and transparency of outputs. Laying the Foundation for Long-Term Success Sustainable AI integration in HR and People Analytics is not just about deploying new technologies but about embedding AI into the company culture. From day one, leaders need to ensure that the tools are intuitive, accessible, and aligned with the company’s core values. Building trust in AI begins with demonstrating how these tools support employees' roles, making HR tasks less burdensome and enabling teams to tackle more strategic initiatives. HR leaders should collaborate with engineering and data teams to customize AI solutions that fit specific organizational needs. This might involve developing unique AI assistants or prompts that streamline operations and ensure consistency across processes. For instance, internal tools that summarize interview notes or assist with coding can enhance productivity without fundamentally altering job responsibilities. Creating a Legacy of Innovation and Engagement The ultimate goal of integrating AI into HR is not just to boost efficiency but to foster an environment where employees feel excited about leveraging cutting-edge tools. Organizations that prioritize a culture of continuous learning and innovation will find that their employees are more engaged, adaptive, and capable of driving the company forward. By recognizing the transformative potential of AI and implementing it thoughtfully, HR and People Analytics leaders can elevate their people strategy with AI and position their organization as a leader in the future of work. Learn more about One AI Assistant If you would like to learn more about One AI Assistant and how other One AI tools can help your team empower your entire company with business-driving insights into their workforce, reach out. Your Data. Real Answers.
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Featured
3 min read
Pria Shah
What Exactly is a Golden Ticket Query? Is it the data science counterpart to the elusive Golden Ticket from Charlie and the Chocolate Factory—a one-way pass to an all-encompassing insight? At first glance, these queries promise ease and instant answers, but do they truly deliver the nuanced understanding your organization needs? Let's dive deeper into what makes these pre-built queries so appealing yet limited, and why embracing a more adaptable approach might be the key to smarter, data-driven decision-making. Golden Ticket Queries, also known as Hard-Coded Queries, are pre-built, standardized queries designed to pull data in the same way every time. Think of them like preset questions that a system will always answer in the same way, without flexibility or context. These queries are often very basic, structured to address common use cases, but they don’t adapt to the unique needs of different users or businesses. Why Are Golden Ticket Queries Often Criticized by Practitioners? Golden Ticket Queries are frequently called out by analytics and AI professionals for a few key reasons: Lack of Flexibility: These queries are static and don’t adapt to new business priorities or shifting data. For example, a query tracking headcount changes might miss important nuances like department-specific trends or seasonal fluctuations. Surface-Level Insights: They often provide basic answers without digging deeper. For example, simply knowing how many employees have high performance ratings doesn’t help you understand what factors contribute to their success or how you can foster high performance across the organization. Missed Opportunities: By sticking with preset queries you miss out on the chance to ask more nuanced specific questions that could reveal new opportunities or solutions tailored to your organization's goals. For instance, turnover metrics might look fine on the surface but miss patterns could emerge when broken down by tenure, engagement, or location. At first glance, Golden Ticket Queries might seem like a quick win, but they rarely provide the depth needed for effective, data-driven decision-making. Our Approach at One Model At One Model, we take a different approach to People Analytics and AI, one that embraces flexibility, customization, and the ability to ask the right questions, tailored to your unique needs. Context Matters: People Analytics isn’t one-size-fits-all. The questions you ask and the insights you seek should be shaped by the specific challenges and goals of your organization. One Model’s One AI Assistant lets you ask dynamic questions that reflect your context, providing answers that are more relevant and actionable. Deeper Insights, Smarter Decisions: By moving away from rigid, canned queries, you open the door to deeper, more thoughtful exploration of your data. Custom queries allow you to uncover insights that are directly tied to your business objectives. Scalable and Adaptable: As your business evolves, so should your analytics. The flexibility built into One Model ensures that as your organization grows, your data exploration and insights grow with it. This adaptability means that your analytics can stay ahead of trends, adjust to new business strategies, and continuously inform smarter decisions. Golden Ticket Queries may seem convenient, but their limitations can hold your organization back from achieving its full potential. Their rigidity and surface-level approach to analytics make them an imperfect fit for today’s complex business environments. One Model believes that AI should help you ask the right questions, not just the easiest ones. With tools designed for flexibility, customization, and actionable insights, we help you uncover the deeper patterns that lead to smarter decisions and better outcomes.
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Featured
5 min read
The One Model Team
In his talk, "Under the Hood: AI-Driven Engineering Workflows for Future of Work," Chris Butler, CEO of One Model, addressed what's coming and how it will impact everything. The key takeaway? AI is about to change the game for productivity by enabling what Chris calls "agentic workflows." Here’s a peek at what that means and why it’s a big deal for your workplace. The AI Ecosystem is Opening Up The enterprise AI ecosystem is evolving quickly. Imagine an AI at work that doesn’t just answer questions but can also take action—accessing tools, managing processes, and optimizing workflows. According to Chris, the likes of Microsoft’s Copilot, Apple’s assistant, and other major players like Salesforce are poised to become the AI linchpins of the workplace. Soon, AI won’t just assist; it will be seamlessly integrated into every facet of your digital workspace. Enter Agentic Workflows One Model is currently building agentic workflows to turbocharge workplace efficiency. Think of a group of specialized AI agents, each with its own job description, working collaboratively—just like a project team. From gathering data, analyzing it, and critiquing results to creating dashboards, these agents mimic the roles of a traditional data team. The result? Faster, smarter outputs that scale without needing more people. Chris gave real-world examples of ai agents in action: An AI project manager, data engineer, and analyst worked together to gather compensation data, clean it, and create insightful reports—tasks that normally take days were completed in hours. The AI agents interact with each other in natural language, refine each other’s work, and iterate until the job is done right. From Four Agents to a Swarm What started as four distinct agents evolved into a swarm—a scalable network of specialized agents able to handle increasingly complex projects. By shifting to a directed graph model, One Model made it possible for multiple agents to work in parallel, dramatically reducing project time. Chris shared an impressive example: A task that two senior data engineers estimated would take twenty days was completed by AI in just 45 minutes. Another key takeaway is that the more specialized the agents work, the higher quality the output. Therefore, having more specialized agents is better than a few multi-purpose ones. What Does This Mean for Productivity? The implications are huge. AI-driven workflows mean fewer manual tasks, faster data processing, and a deeper focus on insights that matter. Companies can double down on their core missions while relying on AI to handle tedious, data-intensive work. Chris predicts that enterprise AI will become the interface we use to ask questions and get work done—a one-stop assistant that pulls insights from different tools and presents them in a digestible way. Dashboards Are Dead—Almost In the future dashboards as we know them may become secondary. Instead of static reports, enterprise AI will generate dynamic, on-demand insights and even make recommendations. Dashboards will still exist, but they’ll be an interface controlled by the AI—just one of many tools in the box. The first point of interaction will be the AI itself, which will decide what tools to use to provide you with answers. Securing the AI Frontier Chris also highlighted a critical concern: securing enterprise AI. As these AIs gain more access to tools and data, the risk of improper usage grows. HR and People Analytics teams need to partner closely with IT to ensure that the right security measures are in place—because once access is lost, it’s hard to regain control. Welcome agentic workflows to the team. Agentic workflows are reshaping the future of work. The enterprise AI of tomorrow won’t just assist employees; it will be an active participant in getting work done—faster, smarter, and more securely. Are you ready to work with your new AI teammates? Are you thinking about using AI? You'll need a solid data platform. Learn why that is so critical and see how you can achieve success by reading our whitepaper.
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